Advances in Asset Management and Condition Monitoring: COMADEM 2019 [1st ed.] 9783030577445, 9783030577452

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Smart Innovation, Systems and Technologies 166

Andrew Ball Len Gelman B. K. N. Rao   Editors

Advances in Asset Management and Condition Monitoring COMADEM 2019

123

Smart Innovation, Systems and Technologies Volume 166

Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-sea, UK Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. Volumes on interdisciplinary research combining two or more of these areas is particularly sought. The series covers systems and paradigms that employ knowledge and intelligence in a broad sense. Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community. It also focusses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities. The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions. High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/8767

Andrew Ball Len Gelman B. K. N. Rao •

•

Editors

Advances in Asset Management and Condition Monitoring COMADEM 2019

123

Editors Andrew Ball University of Huddersfield Huddersfield, UK

Len Gelman University of Huddersfield Huddersfield, UK

B. K. N. Rao COMADEM International Birmingham, UK

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-3-030-57744-5 ISBN 978-3-030-57745-2 (eBook) https://doi.org/10.1007/978-3-030-57745-2 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The University of Huddersfield (UK), a gold-rated University by the Teaching Excellence Framework (UK), organised, in close partnership with COMADEM International (UK), the 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, COMADEM 2019, on 03– 05 September 2019. The Congress was an outstanding international premier event, proving ground for industrialists, scientists and exhibitors. It gave them a unique opportunity to compare scientific and industrial achievements and asset management and condition monitoring equipment and techniques against each other and to determine how good they are internationally. The Congress was “a stadium” of the highest worldwide successes in asset management, condition monitoring and related areas and was attended by over 180 delegates. Designed as a leading international forum, the Congress attracted a worldwide asset management and condition monitoring audience with high-quality presentations by leading players from Africa, Asia, Australia, Europe and North America. The Congress provided also a unique opportunity to network with academics and industrialists from all over the world. The Congress programme included plenary keynote presentations,invited and contributed presentations, invited overview presentations on hot topics of asset management and condition monitoring, case study presentations, industrial workshops and an exhibition. Under the theme of “Digital Enabled Asset Management”, the Congress papers covered areas of asset management and condition monitoring, including signal and image processing, artificial intelligence, pattern recognition, Internet of things, finite element modelling, root cause analysis, sensors and actuators, maintenance and condition monitoring education and training. Professor Andrew Ball, a renowned expert in the field of diagnostic engineering, delivered the first of the plenary keynote presentations at the Congress. Delegates were also advised on the best techniques to adopt, when approaching leading journals with their research, by the invited lectures from Editor-in-Chief of journal

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Preface

“Mechanical Systems and Signals Processing”, Professor John Mottershead (UK), and Swati Meherishi, who is Editorial Director at Springer Nature. This book contains peer-reviewed research and development articles by researchers, participating in the Congress. The book offers the state of the art of advances in asset management and condition monitoring and also serves as an excellent reference work for academic and industrial scientists and graduate students, working in asset management, condition monitoring and related areas. The editors would like to acknowledge and thank the following people for help in book initiation, preparation and completion: • the members of COMADEM 2019 team of the University of Huddersfield, headed by Research Administrative Support Manager Rebecca Marsden • Springer Nature team members Swati Meherishi, Priya Vyas, Rini Christy, Muskan Jaiswal and Akash Chakraborty and • all manuscript reviewers. Thank you. Andrew Ball Len Gelman Raj Rao

Contents

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Comparison of Amplitude to Real and Imaginary Features of the poly-Coherent Composite Bispectrum (pCCB) Components in Machine Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . Kenisuomo C. Luwei, Jyoti K. Sinha, and Akilu Yunusa-Kaltungo Asset Management Simulation and Optimisation of Railway Bridges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dwayne Nielsen

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Study on the Ultrasonic Attenuation Characteristic Due to Crack in a Two-Dimensional Isotropic Plate . . . . . . . . . . . . . . . Xiaojun Zhou and Huifang Xiao

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4

Mimosa Strong Medicine for Maintenance . . . . . . . . . . . . . . . . . . Riku Salokangas, Erkki Jantunen, Martin Larrañaga, and Petri Kaarmila

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Fault Diagnosis of Motor Broken Bar Using Current and Vibration Fusion Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoyun Gong, Yongjie Jing, Wenliao Du, Hongchao Wang, and Baowei Zhao

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Mixed Kernel Functions for Multivariate Statistical Monitoring of Nonlinear Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karl Ezra S. Pilario and Mahmood Shafiee

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Compound Faults Separation Based on Intrinsic CharacteristicScale Decomposition and Sparse Component Analysis . . . . . . . . . Yansong Hao, Huaqing Wang, Liuyang Song, and Lingli Cui

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Detection Method of Contact-Type Failure Based on Nonlinear Wave Modulation Utilizing Ultrasonic Vibration Driven by Self-excitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takashi Tanaka, Yasunori Oura, and Syuya Maeda Online Condition Monitoring of Engines by a Deep Analysis of the Electrical Conductivity and Relative Permittivity Changes of the Lubricant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manfred R. Mauntz and Jörn Peuser

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91 99

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Are We Ready for Industry 4.0? . . . . . . . . . . . . . . . . . . . . . . . . . . Abdu Shaalan, David Baglee, and Michael Knowles

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Operational Modal Analysis in the Presence of Pulse Train and Harmonics Based on SSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Fulong Liu, Jiongqi Wang, Miaoshuo Li, Fengshou Gu, and Andrew D. Ball

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The Development of a Maintenance Gap Analysis Tool for Use Within the Automotive Supply Chain: A Case Study Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Derek Dixon, Kenneth Robson, and David Baglee

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Feature Subset Selection Using Sparse Principal Component Analysis and Multiclass Fault Classification Using Selected Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Biswajit Sahoo and A. R. Mohanty

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Vibration-Based Detection of Wheel Flat on a High-Speed Train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Ruichen Wang, David Crosbee, Adam Beven, Zhiwei Wang, and Dong Zhen

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Piezoelectric Energy Harvesting System to Detect Winding Deformation in Power Transformers . . . . . . . . . . . . . . . . . . . . . . . 171 Guillermo Robles, Mariano Febbo, Sebastián P. Machado, and Belén García

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Digital Asset Management: New Opportunities from High Dimensional Data—A New Zealand Perspective . . . . . . . . . . . . . . 183 Marianne Cherrington, Zhongyu (Joan) Lu, Qiang Xu, Fadi Thabtah, David Airehrour, and Samaneh Madanian

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Introducing a Field Service Platform . . . . . . . . . . . . . . . . . . . . . . 195 Maike Müller, Dirk Stegelmeyer, and Rakesh Mishra

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An Investigation of Weighted Neural Networks for Rolling Bearing Fault Classification Under Uncertain Speed Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Lun Lin, Yimin Shao, Xiaoxi Ding, and Liming Wang

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An Improved VMD Approach for Sensitive Feature Extraction in the Application of Gears Fault Classification . . . . . . . . . . . . . . 223 Mingkai Zhang, Yimin Shao, Xiaoxi Ding, and Liming Wang

20

Coupled Vibration Analysis of a Bevel Geared Rotor-Bearing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Zhen Liu, Fucai Li, and Bo Jing

21

Parameters Analysis and Optimization Design of a Slotless Halbach Linear Generator for Wave Energy Harvesting . . . . . . . 251 Na Liu, Yimin Tan, Weiqiang Mo, and Zuguang Zhang

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Vibration Characteristics of Self-excited Vibration About Sliding Bearings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Ryosuke Fukui, Hiromitsu Ohta, Yuta Yamada, Naoya Nagahashi, Tomoo Shigi, and Satoshi Tamura

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Compound Fault Diagnosis of Rolling Bearing Based on Transformation Scale Improved BPD and MCKD . . . . . . . . . . 269 Jing Meng, Liye Zhao, and Ruqiang Yan

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Realization of Condition Monitoring of Gear Box of Wind Turbine Based on Cointegration Analysis . . . . . . . . . . . . . . . . . . . 281 Biao Zhang, Chao Zhang, Haoran Duan, Yunting Ma, Jianjun Li, and Lingli Cui

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Research on Surge Control of Centrifugal Compressor Based on Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . 293 Kun Jiang, Yang Xiang, Tianyou Chen, and Chaojun Jiang

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Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Railway Switch Failures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Daniela Narezo Guzman, Edin Hadzic, Benjamin Baasch, Judith Heusel, Thorsten Neumann, Gerrit Schrijver, Douwe Buursma, and Jörn C. Groos

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Service Engineering: Faster Spare Parts Procurement Supported by Digital Technologies . . . . . . . . . . . . . . . . . . . . . . . . 319 Theresa Breckle, Sebastian Allegretti, Sven Seidenstricker, and Bastian Joos

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Temperature Monitoring Data Transmission Through Metallic Barrier Based on Ultrasonic Technology . . . . . . . . . . . . . . . . . . . . 331 Dingxin Yang, Dong Tian, and Haifeng Hu

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Fault Detection and Classification of Rolling Bearings Using Extreme Function Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Xiwen Gu, Shixi Yang, and Evangelos Papatheou

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Modelling and Analysis of Slewing Bearings with Quenched Soft Zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Jianxin Gui, Guangbin Wang, Xiaoli Tang, Zhou Zhou, and Yongzheng Jiang

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Multiple-Model Fault Diagnosis Method for Gas Turbine Based on Soft Switch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Yunpeng Cao, Kehui Zeng, Shuying Li, Fengshou Gu, Yuandong Xu, and Bo He

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Information System Requirements Elicitation for Gravel Road Maintenance – A Stakeholder Mapping Approach . . . . . . . . . . . . 377 Jaime Campos, Mirka Kans, and Lars Håkansson

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A Structured Approach to Risk Assessment of Machine Learning Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Justin Fackrell, Jon Arne Glomsrud, and Siegfried Eisinger

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Model Based Monitoring of Dynamic Loads and Remaining Useful Life Prediction in Rolling Mills and Heavy Machinery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Pavlo Krot, Ihor Prykhodko, Valentin Raznosilin, and Radoslaw Zimroz

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Recognizing Life Cycle Benefits of Real Time Fatigue Monitoring for Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Matti Rissanen, Lasse Metso, Tiina Sinkkonen, and Timo Kärri

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Data Openness Based Data Sharing Concept for Future Electric Car Maintenance Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Lasse Metso, Ari Happonen, Matti Rissanen, Kalle Efvengren, Ville Ojanen, and Timo Kärri

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Detection and Classification of Helicopter Drive Shaft Faults Using Neuro-Fuzzy Based on Wavelet Power Spectrum Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Mohamed A. Hassan, Michael R. Habib, and Abdel M. Bayoumi

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Condition Monitoring of Gravel Roads–Current Methods and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Mirka Kans, Jaime Campos, and Lars Håkansson

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A Kind of Faults Knowledge Discovery Pattern by Means of Rough Set Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463 Rongzhen Zhao, Yaochun Wu, and Tianjing He

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Exploring the Impacts of Using Mobile Collaborative Augmented Reality on the Field Service Business Model of Capital Goods Manufacturing Companies . . . . . . . . . . . . . . . . 473 Stefan Ohlig, Dirk Stegelmeyer, Rakesh Mishra, and Maike Müller

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Fatigue Damage Identification and Remaining Useful Life Estimation of Composite Structures Using Piezo Wafer Active Transducers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Richard Loendersloot, Mohammad Ehsani, and Mahnaz Shamshirsaz

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Characteristics of Maintenance 4.0 and Their Reflection in Aircraft Engine MRO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Lasse Metso and Nils Elias Thenent

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Bond Graph Modelling for Condition Monitoring of Induction Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Aisha Alashter, Yunpeng Cao, Khalid Rabeyee, Samir Alabied, Fengshou Gu, and Andrew D. Ball

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Application of Wavelet Packet Transform and Envelope Analysis on Pressure Pulsations from a Reciprocating Compressor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525 Ugonnaya Enyinnaya Muo, Yuandong Xu, Andrew Ball, and Fengshuo Gu

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Deep Learning Decision Support for Sustainable Asset Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537 Marianne Cherrington, Zhongyu (Joan) Lu, Qiang Xu, David Airehrour, Samaneh Madanian, and Andrea Dyrkacz

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Spectral Element Methods for Damage Detection and Condition Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 Magdalena Palacz, Marek Krawczuk, and Arkadiusz Żak

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A Componential Coding Neural Network Based Signal Modelling for Condition Monitoring . . . . . . . . . . . . . . . . . . . . . . . 559 Khalid Rabeyee, Yuandong Xu, Aisha Alashter, Fengshou Gu, and Andrew D. Ball

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An Investigation into the Sensor Placement of a Marine Engine Lubrication System for Condition Monitoring . . . . . . . . . . . . . . . 573 Jinxin Wang, Zhongwei Wang, Fengshou Gu, Xiuzhen Ma, Jingzhou Fei, and Yunpeng Cao

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Applying Contamination Control for Improved Prognostics and Health Management of Hydraulic Systems . . . . . . . . . . . . . . . 583 Marko Orošnjak, Mitar Jocanović, and Velibor Karanović

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Vibration Monitoring of the Gradual Worn in Journal Bearings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597 Osama Hassin, Jiaojiao Ma, Hao Zhang, Fengshou Gu, and Andrew D. Ball

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A Multi-component Bearing Fault Diagnosis Using Fast Iterative Filtering Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 J. P. Xing, T. R. Lin, and D. Mba

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A Project Management Methodology for the Digitalisation of the Industrial Maintenance Domain . . . . . . . . . . . . . . . . . . . . . 621 Jaime Campos, Mirka Kans, and Antti Salonen

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Effect of Surface Wear on Friction of Spur Gears . . . . . . . . . . . . 631 Yandong Shi, Xiuquan Sun, Yatian Zhou, Fengshou Gu, Guangbin Wang, and Ruiliang Zhang

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Automatic Diagnosis Method of Rolling Bearing Based on LSTM-SAE Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 Ning Cao, Zhinong Jiang, and Jinji Gao

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Rolling Bearing Degradation State Prediction with Deep Fusion Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661 Hao Chen, Niaoqing Hu, and Lun Zhang

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Condition Monitoring Systems in the Railway Industry . . . . . . . . 671 Jordan Brant and Bo Liang

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Fault Diagnosis of Sun Gear in Planetary Gearbox: A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685 Lun Zhang and Niaoqing Hu

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Fault Diagnosis of Spiral Bevel Gears Based on CEEMDAN Permutation Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 Jiang Lingli, Chen Liman, Tan Hongchuang, and Li Xuejun

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Modulation Effect of Planetary Gearbox Faults on Stator Current of Induction Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Xiaowang Chen and Zhipeng Feng

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Modulation Signal Bispectrum Based Monitoring of Tooth Surface Wear for Modification Spiral Bevel Gear . . . . . . . . . . . . . 717 Zhifei Wu, Fengshou Gu, Tie Wang, Ruiliang Zhang, Yandong Shi, and Andrew D. Ball

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Real-Time Motion Control of a Spark Robot Using the Robot Operating System and MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 731 Wei-Jie Tang, Feng-Shou Gu, Rong-Feng Deng, Zhen-Tao Liu, Shao-Yong Cao, and Gui-Ping Lu

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Monitoring Strain Concentration at Fixtures of Wind Turbine Blades Using Fibre Bragg Grating Sensors . . . . . . . . . . . . . . . . . . 741 Pingyu Zhu, Zheng Liu, Xuebin Feng, Jiang Wu, Hongjie Xie, Mengjiao Huang, and Marcelo A. Soto

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A Novel Method for Periodical Impulses Detection and Its Applications in Rubbing Fault Diagnosis . . . . . . . . . . . . . 747 Peng Zhou, Zhike Peng, Shiqian Chen, and Qingbo He

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Application of Haar Wavelet De-noising with Cross Correlation and Neighboring Coefficients to the Bearing Faults Prognosis . . . 761 Wei Cao, ZhenYuan Gou, Dong Wang, Han Zhang, and Hassan Javed

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Research and Implementation of Real-Time Motion Control of Robot Based on Kinect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 779 Guiping Lu, Weijie Tang, Jianwei Zheng, Ting Chen, and Xinfeng Zou

66

Design and Research of New-Type Clamping Fixture Based on Tensile Test of Wire and Cable Materials . . . . . . . . . . . 793 Zhiyong Xiao, Guiping Lu, and Zhensheng Zhong

67

Repetitive Transient Extraction Algorithm for the Fault Diagnosis of Planetary Gearbox via Encoder Signal . . . . . . . . . . . 811 Chuancang Ding, Ming Zhao, Jing Lin, Kaixuan Liang, and Jinyang Jiao

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A Novel Residual Domain Adaptation Network for Intelligent Transfer Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827 Jinyang Jiao, Ming Zhao, Jing Lin, Kaixuan Liang, and Chuancang Ding

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A Tooth-Wise Dimensionality Reduction Approach Based on Encoder Signal for the Diagnosis of Gearbox . . . . . . . . . . . . . . 841 Kaixuan Liang, Ming Zhao, Chuancang Ding, Jinyang Jiao, and Jing Lin

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A New Methodology to Deal with the Multi-phase Degradation in Rolling Element Bearing Prognostics . . . . . . . . . . . . . . . . . . . . 855 Amirhossein Mollaali, Mehdi Behzad, and Motahareh Mirfarah

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Torsional Vibration Analysis Applied for Centrifugal Pump Condition Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 871 Marticorena Matías, Mayer Rodrigo, Vignolo Juan, and García Peyrano Oscar

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Vibrations Based Lubricity Condition Monitoring of Journal Bearings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883 JiaoJiao Ma, Yuandong Xu, Fulong Liu, Zhanqun Shi, Hao Zhang, Fengshou Gu, and Andrew D. Ball

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A Non-linear Tent Map-Based ADC Design for Sensitive Condition Monitoring Measurement Systems . . . . . . . . . . . . . . . . 893 Philippa Hazell, Peter Mather, Andrew Longstaff, and Simon Fletcher

74

The Application of a New Technique to Determine the Beginning of the Setting Time for Cement-Based Materials . . . . . 903 Michaela Hoduláková and Libor Topolář

75

Study of Latent Self-healing Ability of Sodium Hydroxide Activated Blast Furnace Slag Systems via Non-destructive Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915 Richard Dvorak, Libor Topolar, Vlastimil Bilek, and Petr Hruby

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Condition Monitoring of Lubricant Shortage for Gearboxes Based on Compressed Thermal Images . . . . . . . . . . . . . . . . . . . . . 927 Xiaoli Tang, Ke Li, Pieter A. van Vuuren, Junfeng Guo, Funso Otuyemi, Fengshou Gu, and Andrew D. Ball

77

Fault Diagnosis of Reciprocating Compressor Using Empirical Mode Decomposition-Based Teager Energy Spectrum of Airborne Acoustic Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939 Debanjan Mondal, Dong Zhen, Fengshou Gu, and Andrew D. Ball

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Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis . . . 953 Haiyang Li, Huaqing Wang, Funso Otuyemi, Dong Zhen, Fengshou Gu, and Andrew D. Ball

79

Detection and Diagnosis of Mechanical Seal Faults in Centrifugal Pumps Based on Acoustic Measurement . . . . . . . . 963 Alsadak Daraz, Samir Alabied, Dong Zhen, Fengshou Gu, and Andrew D. Ball

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Condition Monitoring of Reciprocating Compressor Based on Acoustic Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 977 Miaoshuo Li, Robin Appadoo, Wei Hu, Fengshou Gu, and Andrew Ball

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A Baseline-Free Damage Detection Method for Operation Structure Based on Nonlinear Ultrasonic Guided Waves . . . . . . . 985 Yanping Zhu and Fucai Li

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Generative Adversarial Networks Enhanced Extreme Learning Machine to Classify Faults in Rolling Bearings . . . . . . . . . . . . . . . 995 Yun Gao and Jiawei Xiang

83

Personalized Fault Diagnosis Method Based on FEM Simulation Driving Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1003 Xiaoyang Liu and Jiawei Xiang

84

Guided Wave Based Debonding Detection in CFRP-Reinforced Steel Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 Jingrong Li and Ye Lu

85

The Fourth Industrial Revolution: Digital Transformation and Industry 4.0 Applied to Product Design, Manufacturing and Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1023 Abdel-Moez Bayoumi, Rhea McCaslin Matthews, and Amr A. Abdel Fatah

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Development of a Predictive Maintenance 4.0 Platform: Enhancing Product Design and Manufacturing . . . . . . . . . . . . . . . 1039 Clint Saidy, Sruthi Puthan Valappil, Rhea McCaslin Matthews, and Abdel Bayoumi

87

The Application of Statistical Quality Control Methods in Predictive Maintenance 4.0: An Unconventional Use of Statistical Process Control (SPC) Charts in Health Monitoring and Predictive Analytics . . . . . . . . . . . . . . . . . . . . . . . 1051 Clint Saidy, Kaishu Xia, Anil Kircaliali, Ramy Harik, and Abdel Bayoumi

88

An Impact Event Detection System for Composite Box Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1063 Vafa Soltangharaei, Rafal Anay, Deepak Begrajka, Matthijs Bijman, Mohamed Khaled ElBatanouny, Paul Ziehl, and Michel J. L. van Tooren

89

LSTM Residual Signal for Gear Tooth Crack Diagnosis . . . . . . . 1075 Wenyi Wang, F. Antonio Galati, and Dyana Szibbo

90

A New Approach to Automated Bearing Fault Severity Assessment Using Deep-Learning . . . . . . . . . . . . . . . . . . . . . . . . . 1091 Lucas Mailey, Pietro Borghesani, Wade A. Smith, Wenyi Wang, and Zhongxiao Peng

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The Uncertain Vibrations of a Rotor Operating with Angular Acceleration Based on Taylor Expansion . . . . . . . . 1105 Chao Fu, Yuandong Xu, Yongfeng Yang, Fengshou Gu, and Andrew Ball

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Development of a Non-invasive Screening Technique for Detection of Wrist Fractures in Children . . . . . . . . . . . . . . . . 1115 Ridita Ali, Lyuba Alboul, and Amaka C. Offiah

93

Development and Evaluation of an Accelerometry System Based on Inverted Pendulum to Measure and Analyze Human Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1129 Oseikhuemen Davis Ojie and Reza Saatchi

94

A Finite Element Study to Assess Fracture Risk in Humans with Low Bone Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 Connor Recknell and Reza Saatchi

95

Envelope Ensemble Average of Largest Amplitude Impact Transients for Diagnosing Rolling Element Defects in Bearings . . . 1151 Lei Hu, Fengshou Gu, Jing He, Niaoqing Hu, and Andrew Ball

96

Planning Maintenance Actions in Train Operating Companies—A Portuguese Case Study . . . . . . . . . . . . . . . . . . . . . 1163 Marie Méchain, António R. Andrade, and Marta Castilho Gomes

97

The Detection of Defects on Metallic Subsurface Based on Pulsed Eddy Current Thermography . . . . . . . . . . . . . . . 1183 Fan Jiang, Xiaoyu Xu, Dong Zhen, Hao Zhang, Shijie Dai, and Zhanqun Shi

98

Rolling Element Bearing Fault Diagnosis Based on the Wavelet Packet Transform and Time-Delay Correlation Demodulation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1195 Chen Zhang, Junchao Guo, Dong Zhen, Hao Zhang, Zhanqun Shi, Fengshou Gu, and Andrew Ball

99

Gear Vibration Signal Extraction Based on Meshing Impact Under Heavy Load Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1205 Shuiguang Tong, Yuanyuan Huang, Zheming Tong, Ning Tang, Yue Yu, Yi Zhou, and Feiyun Cong

100

A Recognition Method via Improved CEEMDAN and Multiscale Entropy for Enhancing the Diagnostic Accuracy . . . . . 1215 Feng Ding, Xuejiao Chen, Jianhui Tian, and Wenjuan Wang

101

A New Approach of Manufacturing Tolerance Allocation Based on NSGA-III Algorithm for the In-Process Monitoring of Sheet Metal Parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1233 Yanfeng Xing, Chengyu Jiang, and Liuxiang Luo

102

Towards the Development of a Tribotronic Gearbox . . . . . . . . . . 1243 O. Adeyemi, A. Onsy, and I. Sherrington

Contents

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103

Measurement and Signal Processing of Incipient Cavitation in Vortex Zone of Francis Turbine . . . . . . . . . . . . . . . . . . . . . . . . 1251 Ning Tang, Shuiguang Tong, Zheming Tong, Hao Zhang, Yinhua Wang, Dongping Shen, and Feiyun Cong

104

Energy Harvesting from Knee Motion Using Dielectric Elastomer Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1261 Sujit Kumar Sahu, Anup Sankar Sadangi, and Karali Patra

105

Field Identification of Dynamic Coefficients of Journal Bearings on Flexible Rotor-Bearing System . . . . . . . . . . . . . . . . . . . . . . . . . 1273 Yang Kang, Jiaojiao Ma, Hao Zhang, Zhanqun Shi, Fengshou Gu, and Andrew Ball

106

Neural Network Analysis of Bone Vibration Signals to Assesses Bone Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285 Hajar Razaghi, Reza Saatchi, and Amaka C. Offiah

107

The Separation of Vibration Components Based on Sparse Nonnegative Tensor Factorization . . . . . . . . . . . . . . . . . . . . . . . . . 1297 Haobin Wen, Lin Liang, Ben Niu, Lei Shan, Maolin Li, and Guang Li

108

Planetary Bearing Fault Diagnosis for a CH-46E Helicopter Main Gearbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1307 L. Zhou, F. Duan, S. Ojolo, A. Ogundare, X. Li, and David Mba

109

Using Energy Consumption Profiles as an Indicator of Equipment Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1317 Adrian Morris, David Baglee, and Michael Knowles

110

Technical and Operational Barriers that Affect the Successful Total Productive Maintenance (TPM) Implementation: Case Studies of Abu Dhabi Power Industry . . . . . . . . . . . . . . . . . 1331 Abdulla Y. Alseiari, Peter Farrell, and Yassin Osman

111

Quality of Service in IEEE 802.11ac and 802.11n Wireless Protocols with Applications in Medical Environments . . . . . . . . . 1345 Abdussalam Salama and Reza Saatchi

112

Machine Health Monitoring Using Artificial Intelligence (AI) . . . . 1359 Khalique Umair, Xu Guanghua, Fei Liu, Longtian Chen, Renghao Liang, Niu Ben, and Bhatti Ahmad Waqas

113

Chatter Characteristics Analysis of a Compliant Workpiece in Straight Turning Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . 1375 Kaibo Lu, Yuhao Wang, Peisheng Lou, Fengshou Gu, and Xinyu Pang

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114

A Hybrid Prognostics Approach for Motorized Spindle-Tool Holder Remaining Useful Life Prediction . . . . . . . . . . . . . . . . . . . 1385 Fengxia Han, Hongjun Wang, Cheng Qiu, and Yuandong Xu

115

Research on Assembly Optimization of Sugarcane Harvester Cutter Frame System Under Complex Excitations . . . . . . . . . . . . 1401 Chen Qiu, Hanning Mo, Shangping Li, Daiyun Yang, and Xiao Lai

116

A Meshing Resonance Based Demodulation Algorithm and Its Application for Planet Gear Tooth Root Crack Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415 Tianyang Wang and Fulei Chu

117

Modelling of Spur Gear Dynamic Behaviours with Tooth Surface Wear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1437 Xiuquan Sun, Tie Wang, Ruiliang Zhang, Fengshou Gu, and Andrew D. Ball

118

Risk Assessment on Information Security of Ship Networks in Yangtze River Delta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1451 Lin Yin, Liqun Peng, and Zhixiong Li

119

Delivering Early Warning for Heavy Vehicle Anti-rollover Based on Connected Vehicles V2I Messages . . . . . . . . . . . . . . . . . 1467 Lin Yin

120

Optimizing the Maintenance Strategy Through Predicting Remaining Useful Life (RUL) for Critical Medical Equipment Based on Degradation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 1483 Ali Salih

121

An Evaluating Study of Using Thermal Imaging and Convolutional Neural Network for Fault Diagnosis of Reciprocating Compressors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1495 Rongfeng Deng, Xiaoli Tang, Lin Song, Abdullahi Abdulmumeen, Fengshou Gu, and Andrew D. Ball

122

Remaining Useful Life Prediction for Bearings of Shearer Rocker Transmission Parts Based on Internet of Things . . . . . . . 1505 Hua Ding, Liangliang Yang, Kaibo Lv, Zhaojian Yang, and Zeyin Cheng

123

Large Data and AI Analysis Based Online Diagnosis System Application of Steel Ladle Slewing Bearing . . . . . . . . . . . . . . . . . . 1519 Wei Hu, Fengshou Gu, and Shiqi Chen

124

Fault Diagnosis of Planetary Gearbox Based on Random Forest and Singular Value Difference Spectrum . . . . . . . . . . . . . . . . . . . . 1529 Mingxin Chen, Xinyu Pang, and Kaibo Lu

Contents

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125

A Fast 3D Reconstruction Method Based on Curve Segment of Binocular Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1541 Junyi Lin, Kaiyong Jiang, Yi Guo, Lei Wu, Miaoshuo Li, Fengshou Gu, and Andrew D. Ball

126

Achieving Optimised Infrared Thermography in Innovative Asset Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1553 Roderick Thomas

About the Editors

Professor Andrew Ball holds a BEng from the University of Leeds and a PhD from the University of Manchester. Following his doctorate, he took the Shell sponsored lectureship in Maintenance Engineering at the University of Manchester; he was promoted to Professor of Maintenance Engineering in 1999 and was Head of School of the Manchester School of Engineering from 2003 to 2004. In 2005, he became Dean of the Graduate School, and then in 2007 he moved to the University of Huddersfield as Pro-Vice-Chancellor for Research and Enterprise. His research expertise is in the detection and diagnosis of faults in mechanical, electrical and electro-hydraulic machines, in data analysis and signal processing and in measurement systems and sensor development. He is the author of over 300 technical and professional publications, and he has spent a large amount of time lecturing and consulting to the industry in all parts of the world. He has to date graduated more than 100 doctoral degrees in the fields of Mechanical, Electrical and Diagnostic Engineering, he holds honorary professorial positions at six overseas universities, and he sits on three large corporate scientific advisory boards. Len Gelman, PhD, Dr. of Sciences (Habilitation) joined Huddersfield University as Professor, Chair in Signal Processing/Condition Monitoring and Director of Centre for Efficiency and Performance Engineering, in 2017 from Cranfield University, where he worked as Professor and Chair in Vibro-Acoustical Monitoring since 2002. He developed novel condition monitoring technologies for aircraft engines, gearboxes, bearings, turbines and centrifugal compressors. He published more than 250 publications, 17 patents and is Co-editor of 11 books published by Springer. He is Fellow of BINDT, International Association of Engineers and Institution of Diagnostic Engineers, Executive Director, International Society for Condition Monitoring, Honorary Technical Editor, International Journal of Condition Monitoring, Editor-in-Chief, International Journal of Engineering Sciences

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About the Editors

(SCMR),Chair, annual International Condition Monitoring Conferences, Honorary Co-Chair, annual World Congresses of Engineering, Co-Chair, International Conference COMADEM 2019 and Chair, International Scientific Committee of Third World Congress, Condition Monitoring. He was General Chair, First World Congress, Condition Monitoring, Chair, Second World Congress, Engineering Asset Management and Chair, International Committee of Second World Congress, Condition Monitoring. He is Chair of International CM Groups of ICNDT and EFNDT and Member of ISO Technical Committee, Condition Monitoring. He made 42 plenary keynotes at major international conferences. He was Visiting Professor at ten Universities abroad. Prof. Dr. B.K.N. Rao is Director of COMADEM International, UK. Back in 1988, he firmly believed in the proactive interdisciplinary-based philosophy of condition monitoring and diagnostic engineering management (COMADEM). Since then with the collaboration of many well-known universities, research and development organisations and professional bodies, he has organised annual international Congresses and exhibitions on this theme in many different parts of the world. COMADEM is now recognised by many as an international platform where representatives from industry and researchers meet, shaping the future and creating value. He has supervised and externally examined a number of PhD projects and published in excess of 150 original papers in many scientific and engineering journals. He has edited/co-edited more than 35 books and conference proceedings in the field of COMADEM. He gained his B.Sc in Physics, Chemistry and Mathematics from the University of Mysore; Postgraduate Diploma of the Indian Institute of Science (DIISc) in Industrial Administration; M.Sc in Environmental and Human Factors in Engineering from the University of Southampton; PhD in Mechanical Engineering from the University of Birmingham. In 2004, he was awarded the Higher Doctorate in Technology (DTech) from the Sunderland University for “his significant, sustained and distinguished contribution in the field of Condition Monitoring”.

Chapter 1

Comparison of Amplitude to Real and Imaginary Features of the poly-Coherent Composite Bispectrum (pCCB) Components in Machine Diagnosis Kenisuomo C. Luwei , Jyoti K. Sinha , and Akilu Yunusa-Kaltungo

Abstract Earlier studies have successfully demonstrated the use of the poly-coherent composite bispectrum (pCCB) in the faults identification in rotating machines. However, only amplitudes of the pCCB components were used in the earlier studies. Since the pCCB components are complex numbers (both amplitudes and phases). Hence, the real and imaginary features of the pCCB components are also explored in the fault identification of rotating machines in the current study. The observations from the present study through the experimental rig are presented and compared with the earlier observations using the amplitudes of the pCCB components. It shows that the real and imaginary of the pCCB components shows improvements in fault identification and classification along with a good representation of machine behavior, compared with the magnitude only of pCCB components.

1.1

Introduction

Vibration-based Faults Diagnosis (VFD) in rotating machines is well known technique [1]. However it is still an active area of research to improve the VFD approach for different machine faults. The most challenging task is to analyze the vibration data from all bearings in a machine to identify the fault if existing in the machine. Sinha K. C. Luwei (&)
J. K. Sinha
A. Yunusa-Kaltungo The University of Manchester, Manchester M13 9PL, UK e-mail: [email protected] J. K. Sinha e-mail: [email protected] A. Yunusa-Kaltungo e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_1

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and Elbhbah [2] have initially developed a composite coherent spectrum by fusing the data from all bearings into one single spectrum for the machine to lift limitation for analyzing all data separately. The method was further improved through development of composite coherent bispectrum and tri-spectrum [3, 4]. This composite bispectrum and tri-spectrum are developed based on the concept of the higher order spectra (HOS). This method is then modified to the poly-coherent composite bispectrum (pCCB) [5] to further improve fault diagnosis in rotating machines. The usefulness of this pCCB is already demonstrated through an experimental rig at multiple speeds above and below the machine critical speeds [5–7]. However, only amplitudes of the pCCB components were used in the earlier studies. Since the pCCB components are complex numbers (both amplitudes and phases). Hence, the real and imaginary features of the pCCB components are also explored in the faults identification in rotating machines in the current study. A couple of earlier research studies also used the real and imaginary parts of the higher order spectrum (HOS) for the crack detection only and not for the detection of different rotating machine faults [8, 9]. These earlier studies have not provided much detail of the HOS components used for the crack detection but have used bicoherence and normalised HOS. However both bicoherence and normalised HOS should have only amplitude components [10]. The current research is significantly different and not using the simple well-known bispectrum or bicoherence. The study is using the pCCB analysis developed based on the concept of the bispectrum analysis [5]. Here the real and imaginary parts are not treated separately as parameters but has solved that the matrix of complex quantities by separating them into the real and imaginary parts. The observations from the present study through the experimental rig are presented and compared with the earlier observations using the amplitudes of the pCCB components.

1.2

pCCB Computation

The poly-coherent composite bispectrum B is represented mathematically as [5, 6]; Pns  Bðfl fm Þ ¼

r¼1



XrpCCS ðfl ÞXrpCCS ðfm ÞXrpCCS ðfl þ fm Þ ns

 ð1:1Þ

r where XpCCS in Eq. (1.1) is the poly-coherent composite Fourier Transform (FT) for the rth segment of the measured vibration data from ‘b’ bearing location at frequency fk and ns is the number of equal segment used for the FT computation. r Hence XpCCS is computed as [5, 6];

1 Comparison of Amplitude to Real and Imaginary Features …

XrpCCS ðfk Þ ¼

Xn

s

r¼1

Xr1 ðfk Þc212 Xr2 ðfk Þc223 Xr3 ðfk Þc234 Xr4 ðfk Þ. . .Xrðb1Þ ðfk Þc2ðb1Þb Xrb ðfk Þ

3

1b

ð1:2Þ

where Xr1 ðfk Þ; Xr2 ðfk Þ; Xr3 ðfk Þ; Xr4 ðfk Þ; . . .; Xrb1 ðfk Þ and Xrb ðfk Þ are respective representations of the FT of the rth segment at frequency fk of the vibration responses at measured bearings 1, 2, 3, 4, …, (b − 1) and b. Also, c212 ; c223 ; . . .c2ðb1Þb; respectively represents the coherence between bearing 1–2, 2–3…, (b − 1)b (where b = 1, 2, …, b), and the SpCCS ðfk Þ is the pCCS at frequency fk [5, 6]. The characteristics of the bispectrum can be drawn from the coupling between the frequencies fl ; fm and fl þ fm . Worthy of note is that the pCCB components are represented as Bpq if the frequencies at fl and fm are the pth and qth respective harmonics of the machine’s rotating speed frequency [11].

1.3

Experiments

Experimental set up including its first few natural frequencies obtained by modal testing and the simulated machine conditions (baseline healthy and faulty) is presented here.

1.3.1

Experimental Set Up

The experimental rig (Fig. 1.1) is patterned after a complex industrial machine with multiple bearings operating at various speeds [6]. It is located at the University of Manchester in the dynamic laboratory. The rig is made up of a long and short shaft (1000 mm and 500 mm respectively and 20 mm diameter) which holds three balance disc, D1–D3 (125 mm diameter  14 mm thickness) having two on the long shaft and one on the short shaft. The shafts are connected by a rigid coupler which is powered by a motor connected to the long shaft by a flexible coupler. Four flexible bearing pedestal, Bg1–Bg4 is placed along the shaft connecting the shaft to the bearing by springs (Fig. 1.2). The experimentally identified first few natural frequencies are 11.52 Hz, 18.62 Hz, 30.75 Hz, 49.13 Hz and 85.83 Hz [12].

1.3.2

Tested Conditions

In this study, three speeds were selected as in earlier study [6] i.e. 450 RPM (7.5 Hz), 900 RPM (15 Hz) and 1350 RPM (22.5 Hz). Details of the tested conditions included a baseline healthy which may be seen to have some residual misalignment and residual unbalance (RMRU) as no system is perfectly balanced

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Fig. 1.1 Schematic of experimental rig

Fig. 1.2 Schematics of bearing pedestal of experimental rig with spring connection (flexible)

[5] crack near bearing 1 (CrackNrBg1), crack near bearing 2 (CrackNrBg2) and rub near Disc 1 (RubNrD1) [6]. For each condition, ten (10) sets of measured vibration data was collected at the three selected speeds for each of the tested conditions with each condition tested independent of the others.

1 Comparison of Amplitude to Real and Imaginary Features …

5

Fig. 1.3 Typical pCCB plots at 900 rpm (15 Hz) for (a) Healthy (b) faulty condition

1.4

pCCB Analysis

Based on computation of the poly-coherent composite bispectrum from Eq. (1.1) above, the plot in Fig. 1.3, gives a representation of a typical pCCB healthy and faulty condition. In this study, similar analysis was done for vibration signals collected at the selected speeds i.e. 450 RPM (7.5 Hz), 900 RPM (15 Hz) and 1350 RPM (22.5 Hz). Signal processing was done using a sampling frequency of 10 kHz, 95% overlap with 16,384 data point, 129 averages and a frequency resolution of 0.6104 Hz. A typical plot of pCCB at 900 RPM is represented in Fig. 1.3. As can be seen, the baseline healthy condition had pCCB components, B11 , B12 = B21 and B13 = B31 while the faulty conditions had the presences of B11 , B12 = B21 and B13 = B31 B22 , B23 ¼ B32 and B33 . Similar observation was seen in both 450 RPM and 1350 RPM only with lower and higher amplitude respectively. The observation is good indication of the presence of a fault, however overall machine dynamics is difficult to extract as there seem to be similarity in all faulty conditions [6]. Thus, further investigation using the principal component analysis (PCA) pattern recognition approach for classification of the multiple speeds operation of the machine [5, 6].

1.4.1

Feature Selection and Classification

In this study, features have been selected based on the frequency coupling with respect to the machine rotating frequency components and higher components which may show sensitivity in the diagnosis process. The selected features are B11 , B12 and B13 . For clarity, the amplitude of the pCCB components is used to build a data matrix such that [6];

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2

BC1 S1

b11D1 b12D1 6 .... ¼4 .. b11Dn b12Dn 2

BC1 S1 6 .. B¼4 . BC4 S1

3


bpqD1 .. 7 .. . 5 .


bpqDn C1 S1


.. .




3 BC1 S3 .. 7 . 5

ð1:3Þ

ð1:4Þ

BC4 S3

where BC1 S1 is the matrix of the selected pCCB amplitude components at a single condition and single speed (3), Bpq is the number of pCCB components, Dn is the number of data set, C is the machine condition, S is the operating speed. By extending B in Eq. (1.4), the complex number (a + jb) of each pCCB component is used to create a data matrix C for real and imaginary, such that Real (B) = B R and Imaginary (B) = B Img, thus; 

ðB RÞ C¼ ðB ImgÞ

 ð1:5Þ

Equation (1.4) is a representation of the pCCB amplitude components having a data matrix of 40  9 and Eq. (1.5) is the real and imaginary pCCB components with a matrix of 80  9 at integrated multiple speeds for all conditions respectively. The computed data matrix is inputed into a PCA algorithm where a plot of PC2 vs PC1 gives a representation for fault identification and classification.

1.5

Observations

Figure 1.4 shows data fusion of multiple speeds classification of the various machine conditions based on their dynamic behaviors using PCA pattern recognition [5, 6]. Figure 1.4(a) presents amplitude of the pCCB components with machine conditions identified and classified while Fig. 1.4(b) gives the same for the real and imaginary pCCB components. As can be seen, Fig. 1.4(a) had good clustering, allowing a separation of the various conditions with both crack condition appearing close to each other indicating similar fault may cluster around same region [6]. Clearly, Fig. 1.4(b) presents a better clustering of individual conditions as well as an improved separation between them. CrackNrBg1 and CrackNrBg2 also appeared in same region but with increased separation. The clustering of RMRU and RubNrD1 is evidently better in Fig. 1.4(b) than in Fig. 1.4(a).

1 Comparison of Amplitude to Real and Imaginary Features …

7

Fig. 1.4 Classification of pCCB components at multiple speeds for (a) pCCB amplitude only, (b) pCCB real and imaginary

1.6

Conclusion

The present study confirms that the real and imaginary of the pCCB components shows improvement in fault identification and classification as well as a good representation of machine behavior when compared with the amplitude of the pCCB components. However, the observation is just based on the limited rotor defects. Hence it plans to test this approach on different rotor defects.

References 1. Sinha, J.K.: Vibration Analysis, Instruments, and Signal Processing. CRC Press, Florida (2015). https://doi.org/10.1201/b17938 2. Sinha, J.K., Elbhbah, K.: A future possibility of vibration based condition monitoring of rotating machines. Mech. Syst. Signal Process. 34, 231–240 (2013). https://doi.org/10.1016/j. ymssp.2012.07.001 3. Elbhbah, K., Sinha, J.K.: Fault diagnosis in rotating machines using composite bispectrum. In: International Conference on Recent Advances in Structural Dynamics, pp. 1–8 (2013) 4. Yunusa-Kaltungo, A., Sinha, J.K.: Combined bispectrum and trispectrum for fault diagnosis in rotating machines. J. Risk Reliab. 228(4), 419–428 (2014). https://doi.org/10.1177/ 1748006X14524547 5. Yunusa-Kaltungo, A., Sinha, J.K., Nembhard, A.D.: A Novel fault diagnosis technique for enhancing maintenance and reliability of rotating machines. Struct. Health Monit. 14(6), 604– 621 (2015). https://doi.org/10.1177/1475921715604388 6. Luwei, K.C., Sinha, J.K., Yunusa-Kaltungo, A.: Poly-coherent composite bispectrum analysis for fault diagnosis in rotating machines. In: 3rd International Conference Maintenance Engineering (IncoME III) Paper Ref. ME2018_1151, University of Coimbra, Portugal, pp. 336–344 (2018) 7. Luwei, K.C., Yunusa-Kaltungo, A., Sha’aban Y.A.: Integrated fault detection framework for classifying rotating machine faults using frequency domain data fusion and artificial neural networks. Machines 6(4), 59, 1–16 (2018). https://doi.org/10.3390/machines6040059

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8. Gelman, L., White, P., Hammond, J.: Fatigue crack diagnostics: a comparison of the use of the complex bicoherence and its magnitude. Mech. Syst. Signal Process. 19(4), 913–918 (2005) 9. Gelman, L., Yakasai, S.: Complex higher order spectra vs. it’s magnitude for nondestructive testing of nonlinearity in structures. Non-destructive Test. Eval. 21(2), 103–107 (2006) 10. Collis, W.B., White, P.R., Hammond, J.K.: High order spectra: the bispectrum and trispectrum. Mech. Syst. Signal Process. 12(3), 264–283 (1998) 11. Elbhbah, K., Sinha, J.K.: Bispectrum for fault diagnosis in rotating machines. In: 17th International Congress on Sound and Vibration, Cairo, Egypt (2010) 12. Luwei, K.C., Sinha, J.K., Yunusa-Kaltungo, A., Elbhbah, K.: Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines. In: 14th International Conference on Vibration Engineering and Technology of Machinery, Lisbon, Portugal, pp. 1–6 (2018). https://doi.org/10.1051/matecconf/201821121005

Chapter 2

Asset Management Simulation and Optimisation of Railway Bridges Dwayne Nielsen

Abstract Bridges deteriorate with age and use, and eventually become unsafe without adequate maintenance. Infrastructure managers are being challenged to find economical solutions to address the issues caused by increased asset degradation, while simultaneously reducing maintenance costs on ageing infrastructure. The asset management approach adopted in this study suggests and allocates strategic level maintenance budgets to individual asset components based on asset importance, deterioration and cost of various maintenance options. The aim of the approach is to identify the most efficient combination of future maintenance actions for each bridge in the network based on a limited network budget. Asset management simulations are performed to identify the effects of changing current and future maintenance budgets, application of different maintenance actions and their impact on future maintenance requirements. Maintenance plans are created using a two-phase approach. The first phase applies an optimisation algorithm that combines dynamic programming with Monte Carlo Simulations to consider uncertain input values. The second phase uses the binary integer programming method to allocate detailed maintenance plans within budget constraints. A sample set of 17 bridges from an Australian railway network were analysed utilising this approach. The results suggest a maximum and minimum range for strategic level maintenance budgets for each year in a 100 year plan and a detailed maintenance plan for the next 5 years.

2.1

Introduction

Railway transport plays an important role in sustaining the economy of developed nations and can aid in building the economy of developing nations. Bridges perform an important function in the track route design and are ubiquitous in the D. Nielsen (&) Central Queensland University, Rockhampton, Australia e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_2

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majority of the world’s railway networks. As with other civil structures, bridges deteriorate over time and use, and would eventually become unsafe without adequate maintenance. Bridge maintenance costs increase as the asset deteriorates, which places a growing burden on maintenance budgets of ageing asset populations. However, despite the most diligent maintenance practices, eventually the bridge condition will reach a point when it may be uneconomical or not practical to conduct routine maintenance and major maintenance alternatives, including bridge renewal (replacement) or refurbishment, may need to be considered. Furthermore, the increasing demand for transport has increased the number of trains and has pushed axle loads to the limit of safe design. Therefore, infrastructure managers are being challenged to find economical solutions to address the issues associated with large populations of old and deteriorating bridges, while simultaneously addressing the additional maintenance implications associated with an increasing number of trains and higher axle loads. Predicting the future maintenance cost of complex, deteriorating assets can be difficult over longer planning horizons. Complex assets, including bridges, can contain many components with each component performing an important role within the structure. Some important components have a level of redundancy and failure of one of these components may not lead to immediate failure of the structure, but can contribute to failure over time [1]. Many components may have multiple options for repair or replacement, each with its own cost and reliability. Furthermore, each maintenance option may have a different rate of deterioration based on future maintenance, environmental or operational use. Therefore, the many sub-bridge components included in large bridge populations with their multifarious interactions on surrounding components, compounded by the need to measure deterioration and economically justify repair alternatives, have traditionally made bridge maintenance optimisation very complex. Furthermore, maintenance budgets are under pressure from competing organisational priorities and future budget allocations may be uncertain.

2.1.1

Maintenance Planning Characteristics

Bridge maintenance planning can be considered a temporal optimisation problem, as multiple maintenance actions are conducted at discrete times over the service life of the bridge based on future consequences relating to safety, political or economic impacts [2]. Furthermore, all maintenance planning and optimisation must ensure the track and structures are maintained above a minimum threshold to guarantee operational safety and effectiveness. A major characteristic of bridge maintenance planning is that decisions are based on uncertain data (assuming reasonable predictions with qualitative information), which can include uncertain remaining component service life before maintenance, deterioration rates, maintenance costs and life improvement after maintenance.

2 Asset Management Simulation and Optimisation of Railway Bridges

11

Current maintenance actions have an impact on future maintenance requirements and past maintenance actions cannot be reversed. For example, poor past maintenance practices can lead to higher future maintenance costs. Over recent years, Bridge Management Systems were shown to have limitations integrating bridge and network level decisions, and also in optimising assessment and maintenance decisions when analysing large bridge inventories and processing complex algorithms [3]. Additionally, bridge maintenance planning requires optimisation of multiple objectives, which can include cost, asset importance and reliability.

2.1.2

Maintenance Optimisation Objectives

A popular approach to determine the lowest maintenance cost is with the Life Cycle Cost Analysis (LCCA) method or the Minimum Life Cycle Cost (MLCC) objective. The LCCA compares cumulative costs of maintenance plans (future maintenance actions) and selects the lowest total cost over the life of the structure [4]. However, LCCA cannot identify the optimal timeframe for bridge renewal, as the remaining component value is not included in the analysis. Furthermore, LCCA may not correctly identify the lowest maintenance cost if the renewal or disposal date is not certain. Bridges can typically stay in safe operation much longer than the design life, which indicates that the LCCA method, by itself, is not a suitable method for identifying the minimal maintenance costs in cases of an uncertain lifecycle end date or renewal [5]. The Minimum Bridge Value (MBV) is an objective function that identifies the future time when the remaining bridge value is at its lowest. This approach can also identify the maintenance plan that leads to the lowest future remaining value. However, the MBV may not always provide the lowest cost maintenance plan for a specific lifecycle end date. A combination of the MLCC and the MBV objective allows the “best” features from each approach. The MBV identifies future opportunities or bridge renewal windows, where bridge components are at their combined lowest value. The MLCC identifies the lowest cost maintenance plan for each future renewal window. The MLCC + MBV approach provides a number of opportunities in future years when a bridge may be suitable for renewal and the MLCC objective provides the maintenance plan required to meet the minimum remaining value in the selected year. Bridges selected for renewal will have a maintenance plan that reduces maintenance effort over time to reach the desired minimum remaining bridge value. However, the MLCC + MBV approach is only suitable for bridges selected for renewal. Another popular approach for selecting maintenance actions is the Maximum Benefit/Cost (MB/C) objective. This approach always selects the maintenance plan with the highest benefit/cost, where benefit is measured by the component life gained, or another parameter based on the multiple attribute utility theory, and cost

12

D. Nielsen

is the cost of maintenance. The MB/C approach will always keep the bridge in a better condition than other approaches and has a low maintenance cost over longer planning horizons. However, the MB/C approach has been criticised as “over maintaining” or “gold plating” due to the asset performance being maintained well above the minimum performance threshold. The last and most popular maintenance approach is the Minimal Cost (MC) objective function. This approach always selects the lowest cost maintenance alternative regardless of the component life gained. Minimum cost maintenance alternatives are usually repairs that deteriorate quickly and do not increase the life of the component significantly, which implies that frequent maintenance is required to ensure the bridge is kept in a safe operational condition.

2.2

Decision Support Method

Bridges, like many other assets, require routine periodic maintenance over their operational life to ensure they reach, or possibly exceed, their original design life. Over the life of the bridge, various maintenance options can be applied at each maintenance intervention, and each option has an impact on bridge condition, deterioration rate of maintenance works, expected maintenance life and a maintenance cost. The decision support framework presented provides a logical structure with a phased analysis approach, which simulates the future performance of the entire bridge network. The simulation is based on the premise that every maintenance action changes the intervention time of future maintenance actions. An extensive repair or bridge refurbishment can reduce future maintenance for several years. However, a minimal repair may have a short life expectancy and may need to be revisited frequently to maintain the repair. Asset management simulations deteriorate each individual bridge component and identify the impacts of changing maintenance budgets, application of different maintenance actions and their impact on future maintenance requirements. The framework outputs suggest asset renewal timeframes and associated budgets based on bridge importance, deterioration and future cost of maintenance. Another outcome of this approach are detailed maintenance plans for each bridge component. The analysis process is divided into two phases: bridge level and network level analysis. A graphical representation of the analysis process is shown in Fig. 2.1. The first phase creates multiple optimal maintenance plans or sequences of future maintenance activities from a set of viable alternatives for each asset component over a 100 year planning horizon. Maintenance plans are created using a novel multi-objective optimisation dynamic programming method. The dynamic programming approach includes Monte Carlo Simulations (MCS) to consider uncertain input values. The second phase uses integer linear programming to identify which of the maintenance plans were selected for implementation based on bridge importance

2 Asset Management Simulation and Optimisation of Railway Bridges

13

and budget constraints. Bridges can be selected for renewal in this stage, which may impact the selection of maintenance plans for the remainder of the bridge network.

2.2.1

Input and Pre-processing

The input and pre-processing step exports relevant data held in existing Bridge Management Systems or databases into the correct format for further analysis. The types of data collected are shown in Table 2.1. Inspection results are qualitative and can vary significantly based on the inspector’s experience and knowledge [6]. Therefore, an MCS was employed to address this issue by performing the analysis multiple times and randomly varying the input parameters within the selected distribution range. The distribution types and range for each parameter are provided in Table 2.1.

Bridge inventory

Bridge rang

Remaining life esmaon

Maintenance cost model

Evaluate and idenfy 100 year maintenance plans for each objecve

Bridge Importance score

Calculate bridge renewal score to idenfy bridge renewal windows

Phase 2 Network level analysis

Maintenance improvement model

Bridge and maintenance characterisc dataset

Phase 1 Bridge level analysis

Inputs Pre-Processing

Phased approach for analysis

Annual budgets

Select bridges for renewal Excess

Insufficient

Impose load or speed restricons, possibly temporarily close bridges to reduce maintenance costs

Is maintenance funding within the acceptable 5 year range?

Within range

Conduct analysis to develop a 5 year maintenance program

Fig. 2.1 Phased approach to bridge maintenance planning [5]

14

D. Nielsen

Table 2.1 Analysis parameters [5] Parameter

Configuration, models and values

Cost of maintenance actions Component service life Component life improvement after maintenance Bridge renewal threshold

Random variable ±20% value, normal distribution Random variable ±20% value, normal distribution Random variable ±20% value, normal distribution

Bridge level planning horizon Network level analysis period Worst allowable condition Number of MCS Number of bridges in dataset Number of bridge components in dataset Bridge age range Average bridge age

2.2.2

25% of replacement value. Bridge cannot be selected for renewal if residual value is above this value 100 year—Long-term plan 5 year—Short term plan 4 on a scale from 1 (new) to 5 (old) 1000 Simulations 17 bridges 917 components 1915 to 1985 68.4 years

Repair Rating and Cost Model

Identifying the future service life of every repair type and the service life of replacement component alternatives can be a computationally time-consuming process. Therefore, four maintenance actions were selected for the analysis, each with a relative cost and improvement. The four maintenance actions and related parameters are shown in Table 2.2. Each repair incurs a cost and provides an improvement in the form of life gained to the component. The life gained is measured as an improvement to the component’s condition rating. For example, if a component was at condition rating 5 (poor condition) and was repaired with a repair rating 2, then the new condition will be rating 3 (average condition).

Table 2.2 Cost parameters [5] Repair rating, Rr

Maintenance description

Life gain

Cost (Steel and concrete)

Cost (Timber)

1 2 3 4

Minor repair Major repair Refurbishment Replacement

+1 +2 +3 +4

$1,500 $3,000 $4,500 $6,000

$100 $150 $200 $250

Cr Cr Cr Cr

2 Asset Management Simulation and Optimisation of Railway Bridges

15

Examples of the cost model are provided below: • A steel bridge girder 20 m long receives a repair rating 1 activity. The cost would be 20 m
$1,500 = $30,000. • If two bearings were repaired with a rating 1 repair, then the cost would be two units
$1,500 = $3,000. • If a concrete deck needed to be replaced, which was 12 m long and 2 m wide, then the replacement cost would be 12 m
2 m
$6,000 = $144,000. • If these three repairs were all on the same bridge, then the estimated repair cost would be $30,000 + $3,000 + $144,000 = $177,000.

2.2.3

Bridge Dataset

The bridge dataset was supplied by an Australian railway organisation and included inspection and maintenance records of 17 bridges, which comprised 917 components. The bridges in the dataset were located on the Eastern coastal region of Australia. Bridge construction was a combination of concrete piers on either pile or spread footings, with steel girders and timber transoms. Railway bridge construction dates in the dataset range from 1915 to 1985. The collected data was placed into a consistent format and a summary is presented in Table 2.3. Table 2.3 Bridge dataset [5] Bridge ID

Constructed (year)

Length (m)

Bridge importance

Renewal cost ($)

Number of components

198 200 202 204 210 212 213 218 219 221 222 224 226 230 232 758 953

1915 1978 1919 1984 1979 1919 1919 1981 1923 1985 1984 Unknown 1985 1922 1915 1919 1983

495.56 29.26 60.8 118.4 35.14 107.94 51.6 102.41 177.48 35.39 43.89 69.5 21.42 210.3 230.45 40.7 31.63

92% 67% 15% 95% 42% 42% 74% 59% 74% 99% 74% 16% 82% 36% 67% 50% 18%

16,353,480 965,580 2,006,400 3,907,200 1,159,620 5,397,000 1,702,800 5,120,500 5,856,840 1,167,870 2,194,500 3,475,000 1,071,000 10,515,000 11,522,500 1,343,100 1,043,790

133 19 33 133 26 55 41 55 73 26 29 37 26 74 100 26 32

16

D. Nielsen

The limited steps in the 5-category component rating system means that components with a longer service life were placed into the 20-year plus category due to the uncertainty with making long-term performance predictions. The bridge inspection reports indicate that inspectors are prone to leave the majority of their bridge components in either the 40 year zone (condition rating 2 for steel) when no sign of a defect is evident and the 20 year zone (condition rating 3 for steel) if the component starts to show minor or moderate signs of a defect. Inspectors allow components to remain at condition rating 3 until they start to exhibit advanced stages of deterioration.

2.2.4

Bridge Importance Score

The bridge importance score is usually based on the utilisation of each bridge in the network and can be derived from the annual number of passengers or annual tonnages transported. Traffic with a mix of passengers and freight use the same approach by including the number of passengers and Million Gross Tonnes of freight and applying a weighting factor to each train type. Bridge importance was calculated from Eq. 2.1 in cases of mixed rail traffic types [5]. P P Fbranch þ wp Pbranch P Bi ¼ P ð2:1Þ Fnetwork þ wp Pnetwork where: P P Fbranch is the sum of all freight gross tonnages transported over a branch P Fnetwork is the sum of all freight gross tonnages transported over the network P Pbranch is the sum of all passengers transported over a branch Pnetwork is the sum of all passengers transported over the network wp is the weighting factor for passenger traffic as equivalent freight tonnage Note: Bi ¼ 1:0 when the branch carries all traffic in the network

2.2.5

Phase 1—Bridge Level Analysis Phase

The LCCA model has long established that costs should be minimised over the life of the structure. However, the actual life of each structure is usually ill-defined and the cost implications of delaying a bridge renewal beyond the design life are uncertain. Furthermore, once scheduled for renewal, the scheduled date can still change due to other economic pressures, implying any scheduled renewal date is also uncertain. Therefore, the bridge level analysis approach produces maintenance plans leading up to the time of scheduled renewal and identifies the cost impact of delaying renewals. However, not every bridge in the network would necessarily be

2 Asset Management Simulation and Optimisation of Railway Bridges Fig. 2.2 Dynamic programming process [5]

17

Read input data from csv file Monte Carlo Loop Loop = loop - 1

Yes

Generate MCS number and PDF for each random variable

Subroutine: MCS generator

Identify optimum plans

Subroutine: AMP-DP search algorithm

Loop > 0 No

Write CSV file with optimal plans for each element

scheduled for renewal, and alternative approaches are required to plan their future maintenance needs. A task specific dynamic programming algorithm (Fig. 2.2) was developed to produce sequences of maintenance activities that included MCS for subjective input values and optimised using the selected objective functions. The output from the analysis is a collection of maintenance plans consisting of a sequence of maintenance activities, costs and intervention times for each bridge component. The maintenance plans for each bridge are available for selection in the network level analysis. The bridge level optimisation used the three objective functions of MLCC + MBV, MB/C and MC. The MLCC + MBV objective optimised four maintenance actions (repairs), the MB/C optimised three actions (having omitted the replacement repair) and the MC objective used the lowest cost maintenance action (minor repair). The bridge level analysis creates one maintenance plan for each of the MC and MB/C objectives and 100 maintenance plans for the MLCC + MBV objectives (i.e., one plan for each year in the planning horizon).

2.2.6

Phase 2—Network Level Analysis

The second phase rationally allocates maintenance plans to available network budgets and suggests long-term planning opportunities for bridge renewals. This phase follows the process presented in the Phase 2 section of Fig. 2.1. The first step calculates the bridge renewal scores.

18

D. Nielsen

Bridge Renewal Score The aim of the multi-objective bridge renewal ranking process is to identify the “best” windows of opportunity to conduct bridge renewals. It does not make economic sense to renew a bridge based solely on its remaining value, as bridge “suitability” and ongoing maintenance costs are also an important factor. However, many bridges have a finite service life and will eventually need renewal. Therefore, the argument is not one about acceptable suitability and costs but becomes a question of when the bridge should be renewed, based on suitability and costs. Calculating alternative future annual maintenance plan costs and future bridge remaining values assists in making decisions for bridge renewals. In large bridge networks, some individual bridges can be difficult to separate between which bridges should be selected for replacement and when they should be replaced. Bridge renewals should ideally be conducted just prior to the occurrence of the lowest bridge value (resulting from the combined deterioration of individual bridge components) and while the bridge is safely operating, thus gaining the greatest return from the initial bridge investment. Bridge infrastructure are long-term investments and renewals may be scheduled many decades in advance, assuming future budgets are known. The minimum bridge value (Bv ) and the bridge importance score (Bi) were combined mathematically to calculate the bridge renewal score, shown in Eq. 2.2 [5]. Bridges under a moderately low percentage of their replacement value (Bn ) may be eligible for renewal. The 25% value was selected as one indicator for consideration in determining bridge renewals.  If ðBv \0:25Bn then Br ¼

    Bv wi Bi þ wv 1 
4
100 else Br ¼ 0:0 Bn ð2:2Þ

where: wi is the weighting factor for bridge importance wv is the weighting factor for bridge value wi þ wv ¼ 1 Bi is the bridge importance Bv is the current bridge value Bn is the new bridge value Equation (2.2) provides bridge renewal scores between 1 and 100, with the higher score indicating higher criticality and priority. As the MLCC + MBV objective produces one bridge maintenance plan for every year, then the bridge renewal score is also created for each year. As discussed earlier, a maintenance plan based on LCCA is only valid if the bridge renewal date is known and fixed. Therefore, to ensure the optimal

2 Asset Management Simulation and Optimisation of Railway Bridges

19

maintenance plan is fixed throughout the remaining life of the bridge, the renewal maintenance plans are budgeted with a higher priority than the remaining network. Maintenance Plan Selection After the scheduling of bridges for renewal, the analysis process identifies and allocates a maintenance plan for each individual bridge that satisfies the limited remaining network budget. The allocation of asset maintenance plans within the limited annual maintenance budget was solved with a binary integer linear programming method with a single objective function providing detailed (component level) maintenance plans over a 5 year period. The single objective function was developed with the multiple attribute utility theory [7, 8] using individual asset importance and maintenance improvement. The analysis was designed to prioritise optimal maintenance plans to the most critical assets in the network. The binary integer linear programming equations that solve the network optimisation problem for a given year are presented in Eqs. 2.3, 2.4 and 2.5 [5]. Maximise

XT

XN XP

y¼1



X B L a¼1 yka iyk yka

k¼1

ð2:3Þ

subject to: XN XP k¼1

a¼1

Xyka Cyka  Gy ; for y ¼ 1; 2; 3. . .T

XT y¼1

XN XP k¼1

a¼1

Xyka ¼ 1

ð2:4Þ ð2:5Þ

where: k = bridge a = bridge improvement after maintenance activities y = year X ¼ 0 or 1 Biyk = bridge importance of bridge k at year y Lyka = improvement from activities at year y, bridge k and maintenance plan a Cyka = cost of maintenance plan a for bridge k at year y N = number of bridges in the network P = number of maintenance plans for each bridge and year T = number of years in the analysis period Gy = maintenance budget for year y

20

2.3

D. Nielsen

Results and Discussion

The results of the first phase are multiple 100 year maintenance plans for each bridge, which are presented as a summary of the total bridge network. The second phase shows a range between the desired and minimum budgets over a shorter 5 year maintenance plan.

2.3.1

Phase 1 Results

16 14 12 10 8 6 4 2

MC

MB/C

MLCC+MBV

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97

Fig. 2.3 Future bridge network value cost

Network bridge value ($M)

Phase 1 results are presented in two sections to better describe the process. The first section covers the results without MCS and the second section includes MCS. Figure 2.3 shows the residual value of the future bridge network based on optimisation of the three objective functions without MCS. Figure 2.4 shows the cumulative maintenance cost to achieve the values shown in Fig. 2.3. These graphs highlight that the MC objective produced the lowest network bridge value at the highest maintenance cost, while the MB/C objective produced the highest network bridge value at approximately 50% of the maintenance cost of the MC objective. As stated earlier, the MLCC + MBV objective is valid if bridge renewal timeframes are firmly set. In Figs. 2.3 and 2.4, the MLCC + MBV graph shows the MLCC objective results for each year of the planning horizon. Furthermore, these graphs show that the MLCC + MBV objective produces higher bridge values than the MC objective at the lowest maintenance cost. Therefore, the lowest cost maintenance plans are identified using the MLCC + MBV objective function when a bridge has been firmly scheduled for renewal. Figures 2.5 and 2.6 show the network maintenance costs with MCS for the MC and MB/C objectives respectively. These graphs highlight the high annual maintenance costs associated with the MC objective, compared to the lower average costs of the MB/C objective. A graph showing the cumulative maintenance cost differences is presented in Fig. 2.7.

Planning horizon (years)

MC

100

MB/C

21 MLCC+MBV

80 60 40 20

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97

Fig. 2.4 Cumulative network maintenance

Network maintenance cost ($M)

2 Asset Management Simulation and Optimisation of Railway Bridges

1,800 1,500 1,200 900 600 300 0 1 8 15 22 29 36 43 50 57 64 71 78 85 92 99

Fig. 2.5 Network maintenance cost—MC

Maintenance Cost ($'000)

Planning horizon (years)

3,500 3,000 2,500 2,000 1,500 1,000 500 0 1 8 15 22 29 36 43 50 57 64 71 78 85 92 99

Fig. 2.6 Network maintenance cost—MB/C

Maintenance Cost ($'000)

Planning horizon (years)

Planning horizon (years)

22

D. Nielsen

MC

100

MB/C

80 60 40 20 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

Maintenance cost ($M)

120

Planning horizon (years) Fig. 2.7 Cumulative network maintenance cost after MCS

Bridge renewal score %

70

Bridge 198

Bridge 200

Bridge 202

Bridge 213

60 50 40 30 20 10 0

1

2

3

4

5

6

7

8

9

10

Planning horizon (years) Fig. 2.8 Bridge renewal scores for selected bridges limited to 10 years

2.3.2

Phase 2 Results

The second phase of results is presented in two sections covering the bridge renewal scores followed by the simulation results. Bridge renewal scores covering a sample of four bridges is presented in Fig. 2.8. This Figure shows Bridge 213 as having the highest renewal score at year 10, which is closely followed by Bridge 198 at the same year. As bridges deteriorate over time, their values drop, which increases their eligibility for renewal. Bridge 200 has the lowest renewal score and thus is not a high priority for renewal during this 10 year snapshot. Figures 2.9 and 2.10 highlight the desired and minimal cumulative budgets for the following five years. Figure 2.9 shows the maintenance cost range without renewals, and Fig. 2.10 shows the range with two renewals. Where the first renewal scheduled was Bridge 213 at year 5 and Bridge 198 at year 19. The renewal cost of Bridge 213 was approximately $1.7 M (Table 2.3). The suggested budget for

Fig. 2.9 Cumulative maintenance cost—$2M budget

Maintenance cost ($'000)

2 Asset Management Simulation and Optimisation of Railway Bridges

23

Desirable (MB/C) Minimal (MC) $2M soluƟon (without renewals)

3,000 2,500 2,000 1,500 1,000 500 0

1

2

3

4

5

Fig. 2.10 Cumulative maintenance cost—$2.9M budget

Maintenance cost ($'000)

Maintenance program (Years) Desirable (MB/C) Minimal (MC) $2.9M soluƟon (with renewals)

4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 1

2

3

4

5

Maintenance program (Years)

renewal was $2.9 M, which included the Bridge 213 renewal cost and the network’s bridge maintenance for the next 5 years. Figure 2.9 shows the annual funding for a 5-year, $2M budget solution. Simulations using various budget amounts can identify future impacts on asset maintenance.

2.4

Summary and Conclusion

This paper offers a new approach to asset maintenance planning that solves the time-dependant optimisation problem. This approach calculates the future sequence of maintenance actions to meet multiple objective functions. The analysis was conducted in two phases. The first phase included the development of multiple optimised maintenance plans for each bridge. This included evaluating bridges at the component level using component maintenance costs and deterioration rates to identify the future intervention times and costs. The first phase also included the

24

D. Nielsen

development of bridge renewal maintenance plans, which evaluated four maintenance alternatives for each bridge component at each year of a 100 year planning horizon. A recursive algorithm considered the future impact of each maintenance action at each year of the planning horizon to identify one maintenance plan that delivers the lowest maintenance cost for each planning year. The second phase assigns one maintenance plan to each bridge in the network based on bridge importance and maintenance improvement and limited by budget. The analysis provided an estimate of the budget range, suggesting between a minimum maintenance budget and a desired limit. “What if” and alternate planning scenarios can be performed to identify the impact of maintenance budgets on future maintenance costs. The analysis showed that optimised maintenance plans could be developed using a typical industry dataset and this approach can provide asset management organisations with additional tools to support decision making that aligns with their asset strategies/plans.

References 1. Yang, S.-I., Frangopol, D.M., Neves, L.C.: Service life prediction of structural systems using lifetime functions with emphasis on bridges. Reliab. Eng. Syst. Saf. 86(1), 39–51 (2004). https://doi.org/10.1016/j.ress.2003.12.009 2. Whittle, P.: Optimization Over Time. Wiley, New York (1982) 3. Okasha, N.M., Frangopol, D.M.: Computational platform for the integrated life-cycle management of highway bridges. Eng. Struct. 33(7), 2145–2153 (2011). https://doi.org/10. 1016/j.engstruct.2011.03.005 4. Hawk, H.: NCHRP report 483: bridge life cycle costs analysis. In: National Cooperative Highway Research Program. Transportation Research Board, Washington, DC (2003) 5. Nielsen, D.: Decision support system for railway bridge maintenance management. Central Queensland University (2017) 6. Adey, B.T., Klatter, L., Kong, J.S.: Overview of existing bridge management systems. In: International Association of Bridge Maintenance and Safety (2010) 7. Von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944) 8. Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York (1976)

Chapter 3

Study on the Ultrasonic Attenuation Characteristic Due to Crack in a Two-Dimensional Isotropic Plate Xiaojun Zhou and Huifang Xiao

Abstract In this paper, scattering and attenuation of ultrasonic waves by embedded horizontal crack of different depth is studied using a two-dimensional isotropic plate. The finite element analysis (FEA) with absorbing boundary condition is employed to simulate the experimental pulse-echo mode to obtain ultrasonic data. The presence of a dead zone directly under the surface in the pulse-echo test is recovered. Effect of crack depth on the scattering of elastic waves and the resulted attenuation behavior is characterized by the defined energy attenuation coefficient. The variation of energy attenuation coefficient with crack depth is approximated by a second order polynomial function. The maximum attenuation occurs when the crack is located at central plate in the thickness direction and the attenuation decreases as the crack approaches surface.

3.1

Introduction

Ultrasonic methods have been widely used for the non-destructive detection and localization of possible crack in metal plates [1–3]. For a typical ultrasonic non-destructive evaluation (NDE) process, ultrasonic wave propagations are generated by an actuator/transducer in the tested component. Scattering and reflection of elastic waves and the resulted attenuation are expected when crack is encountered. Since the scattered wave field and the ultrasonic attenuation carry rich information on the characteristics of the crack, the invisible cracks in a metal plate can be possibly predicted with the measured ultrasonic data.

X. Zhou China Ship Research and Development Academy, Beijing 100192, China e-mail: [email protected] H. Xiao (&) School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_3

25

26

X. Zhou and H. Xiao

Significant effort has been expended to characterize the scattering and attenuation of elastic waves by the crack and the further identification. To directly relate the measured ultrasonic signal to embedded crack in material, different techniques have been developed. The most widely used method is time of flight diffraction (TOFD) [4, 5]. It usually employs longitudinal waves which are transmitted and received using transducers that are scanned over the surface of the inspected component. Internal cracks can be detected by comparing characteristic parameters of measured signals with those of the defect free structure [6]. In this paper, the transit scattering and attenuation of elastic waves by embedded crack with different depth is studied. This is done in the simplest possible setting, i.e. the 2D isotropic in-plane problem with an interior parallel rectangle crack. The finite element analysis (FEA) with absorbing boundary condition is employed to simulate the experimental pulse-echo mode to obtain ultrasonic data. The ultrasonic attenuation due to crack is characterized by the defined energy attenuation coefficient and is established as a function of crack depth.

3.2 3.2.1

Description of the Studied Model Geometry Definition

The geometry of a two-dimensional isotropic plate under investigation is presented in Fig. 3.1. A rectangle plate has the dimensions of width B, and height H. A rectangular crack, with width l and height d, is located in the mid-width of the plate and parallel to the plate edge. The position of the crack in the plate is determined by the distance of the crack centerline to the front edge, h. A line pressure excitation normal to the plate edge is applied uniformly along the plate width to simulate the effect of a transducer. This forces vibrating in the z direction and generates incident waves of the longitudinal (L0) wave mode principally parallel to the excitation direction, i.e. along z and those of the fundamental shear (S0) mode principally perpendicular to it, i.e. along x. The generated waves propagate through the plate and interact with the crack and the edges. The L0 mode reflects from the crack edge and is scattered by its tips as well as undergoing conversion to the S0 mode.

3 Study on the Ultrasonic Attenuation Characteristic Due to Crack …

27

Pressure

Fig. 3.1 The geometry of a two-dimensional isotropic panel with embedded crack

S0

S0

Front edge

h L0 l

H Crack

d

z o

B/2

x

B

The displacement responses at the front edge where the excitation applied are monitored to model the pulse-echo mode of operation in experimental test. If a crack is present in the component the wave will interact with the crack before it reaches the backwall. Since the reflected part of the wave has travelled less distance than the echo from the backwall, it will arrive earlier at the transducer. To examine the influence of the crack depth on the scattering and the resulted attenuation behavior, different values of h will be investigated using FE simulations. The objective is to quantify the effect of crack depth on the ultrasonic attenuation.

3.2.2

Finite Element Modeling

The problem was simulated using the commercial package ABAQUS. The pressure excitation is applied in the z direction perpendicular to the front edge of the plate formed by all nodes through the width. The excitation is a single cycle Hanning windowed pulse centered at the frequency of fc = 10 MHz with a time duration of Dt = 200 ns, as shown in Fig. 3.2 in both time and frequency domains. The two corner nodes at the back edge were constrained from moving in all directions. The material considered for the domain is steel with Young’s modulus E = 200 GPa, Poisson’s ratio v = 0.28 and density q = 7850 kg/m3. The corresponding wave velocities in the medium, using Christoffel’s equation [7], are longitudinal wave velocity cp = 5707 m/s, shear wave velocity cs = 3155 m/s and Raleigh wave velocity cR = 2912 m/s. Thus, for a pulse frequency of 10 MHz, the wavelength of the elastic L0 mode in steel is thus k = 0.57 mm.

28

X. Zhou and H. Xiao

(a)

(b)

1

0.7

Amplitude

Amplitude

0.6 0.5 0

0.5 0.4 0.3 0.2

-0.5

0.1 -1

0

50

100

t (ns)

150

200

0 0

5

10

15

f (MHz)

20

25

30

Fig. 3.2 An excitation signal in the form of a pressure pulse in a time and b frequency domains. The central frequency is fc = 10 MHz

The domain is discretized using rectangular elements. The mesh convergence was achieved with element size of k/60, i.e. 60 elements per L0 wavelength at the center frequency employed. The time step was chosen to be 0.2 ns. Absorbing layers with increasing damping were implemented around the left and right edges to eliminate the reflections from boundaries [8, 9]. The nodal displacements of the front edge in the z direction were recorded and averaged to characterize the reflected response.

3.2.3

Verification of FE Modeling

To validate accuracy of the finite element (FE) modeling, results of wave propagation in a defect free domain of 5 mm
5 mm are presented. The normal pulse shown in Fig. 3.2 is applied at the front edge parallel to the z direction. The z-displacement, i.e. A-scan plot of the averaged results from all the nodes at the front edge is shown in Fig. 3.3. It can be seen that the response of a defect free plate consists of a first arrival, Fecho, followed by a second arrival, 1st Becho, due to a backwall reflection. Further echoes at later times due to reverberations in the plate also observed, i.e. 2st Becho, 3st Becho, 4st Becho. The first backwall echo 1st Becho reaches the front edge after a time of flight of about 1.80 ls and the time interval between neighboring echoes has the value of about 1.80 ls, which agrees well with the theoretically computed value of 1.75 ls. Thus, this example verifies the accuracy of the simulation of the wave propagation from time-of-flight consideration.

3 Study on the Ultrasonic Attenuation Characteristic Due to Crack …

29

0.08

Fig. 3.3 Comparison of the numerical and experimental reflected z-displacements in a defect free plate with a 5 mm thickness

Numerical Experimental

0.06 0.04

z (μm)

0.02 0 -0.02 -0.04

Fecho

-0.06 -0.08

1st Becho 0

1

2

3

2st Becho 4

t (μ s)

5

3st Becho 6

4st Becho

7

8

A simple experiment for detecting the ultrasonic wave generated by dual incident laser source in a defect free steel specimen was also performed. The tested steel plate has a rectangle cross-section and the dimensions are thickness H = 5 mm and width B = 20 mm. The experimental waveform of the reflected displacement in z direction is also shown in Fig. 3.3. It can be seen that the simulation results agree well with experimental results in arriving time of backwall echoes.

3.3

Numerical Results and Discussion

To gain an understanding of crack depth on the scattering and reflection of elastic waves and the resulted attenuation behavior, different crack depth values, h, are considered, for a crack with dimensions l = 0.5 mm and d = 0.1 mm. The results are presented in the form of A-scan data. The displacement fields of the entire domain at different time steps in the form of snapshot are also presented.

3.3.1

Time Domain Responses

To characterize the crack depth, a non-dimensional parameter b is defined as b¼

h h0

ð3:1Þ

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where h0 is the propagating distance of the incident excitation with 1 h0 ¼ Dtcp 2

(a)

(b)

0.04

0.04

0.02

z ( m)

z ( m)

0.02

0

-0.02

T1

T2

Ce c h o 4s t Ce c h o

0

-0.02

3s t Ce c h o 2s t Ce c h o

Ce c h o

Ce c h o

1s t Ce c h o

3s t Be c h o

Fe c h o -0.06 0

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(e)

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(f)

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0.02

0.02

0

0

z ( m)

z( m)

1s t Be c h o 2

(d)

z( m)

z ( m)

(c)

-0.02

-0.02

-0.04

-0.04

-0.06

-0.06

-0.08 0

1

t ( s)

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-0.08 0

4s t Be c h o

2s t Be c h o

-0.04

-0.04

T4

T3

1

2

3

4

t ( s)

5

6

7

-0.08 0

t ( s)

Fig. 3.4 The reflected z-displacement for six different crack depth a b = 0.44 b b = 1.76 c b = 3.52 d b = 5.28 e b = 7.04 f b = 8.33

3 Study on the Ultrasonic Attenuation Characteristic Due to Crack …

31

and Dt is the time duration of the incident excitation, cp is the velocity of the L0 wave mode in the plate. For the calculation in this work, h0 has the value of h0 = 0.57 mm. Simulations were performed for eight different non-dimensional crack depths of b = 0.44, 0.88, 1.76, 3.52, 5.28, 7.04, 7.90, 8.33, corresponding to h = 0.25 mm, 0.5 mm, 1 mm, 2 mm, 3 mm, 4 mm, 4.5 mm and 4.75 mm, respectively. It is noted that for a crack at the central thickness, non-dimensional crack depth has the value of b = 4.39. Figure 3.4 shows examples of the reflected displacement in y direction for six different crack depths of b = 0.44, 1.76, 3.52, 5.28, 7.04, 8.33. In Fig. 3.4(b)–(e), obvious reflections from the crack face, i.e. 1st Cecho, 2nd Cecho, 3rd Cecho, 4th Cecho, are observed between the frontwall echo Fecho and the first backwall echo 1st Becho, and the neighboring backwall echoes, i.e. 1st Becho, 2nd Becho, 3rd Becho, 4th Becho for non-dimensional crack depth of b = 1.76, 3.52, 5.28, 7.04, compared with the response of the crack free case shown in Fig. 3.3. The backwall reflection is more intensive at early echoes and exhibits a larger amplitude. For crack depth b = 0.44, the time difference between the frontwall echo and the crack echo is smaller than the duration time of the frontwall echo, the flaw echo is superposed with the frontwall echo, and cannot be recognized, as shown in Fig. 3.4 (a). Similarly, for crack depth b = 8.33, the time difference between the backwall echo and the crack echo is smaller than the duration time of the backwall echo, the crack echo is superposed with the backwall echo, and also cannot be recognized, as shown in Fig. 3.4(f). These are the dead zones in the near-surface region in the pulse-echo test in which the transducer serves as both a transmitter and a receiver [10].

3.3.2

The Energy Attenuation Coefficient

It has been shown above that the position of crack in thickness direction has a great effect on the backwall echoes of the reflected responses in both time and frequency domain. The energy associated with backwall echoes, which is more sensitive to crack position [11], is further extracted to characterize the effect of crack depth on the attenuation behavior. The energy carried by each echo is proportional to the square of the amplitude [12]. Therefore, a simplified measure of the energy for backwall echoes is defined as Z Eb ¼

t2

z2 dt

ð3:2Þ

t1

where z is the displacement, t1 and t2 are the initial time and ending time of the backwall echo respectively. The integration is performed over the whole time trace in the backwall echo time duration. For the eight different crack depths considered, the energy values of all the four backwall echoes in the reflected responses are calculated. An exponent function is further used to fit the relationship between the calculated energy value of the back

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echoes, Eb, and the position of back echo, n, for cracks with different depth and is given by Eb ¼ E0 ean

ð3:3Þ

where a is the energy attenuation coefficient. Figure 3.5 shows the calculated energy value of different backwall echoes for different non-dimensional crack depth as b = 0.44, 1.76, 5.28, 8.33 and the corresponding fitted values using Eq. (3.3) for central frequency fc = 10 MHz. The position of backwall, n (n = 1,2,3,4), represents the four backwall echoes in the reflected responses shown in Fig. 3.4. It is seen that the energy decreases as the position of backwall echo increases and this attenuation follows the exponent relationship. The variations of crack position in thickness direction greatly influence the energy attenuation coefficient. Figure 3.6 shows the plot of energy attenuation coefficient a as a function of non-dimensional crack depth b for incident excitations of central frequency fc = 10 MHz and fc = 20 MHz. A maximum is observed for crack depth of about b = 4.4, i.e. in the central thickness and the coefficient decreases as b value increases or decreases. The extracted results in Fig. 3.6 show that the energy attenuation coefficient behaviours nonlinearly with the crack depth. The nonlinear expression relating the energy attenuation coefficient and the non-dimensional crack depth is approximated by aðbÞ ¼ k0 þ k1 b þ k2 b2

ð3:4Þ

where the coefficients k0, k1 and k2 are determined by the material properties of the plate, the excitation frequency and time duration. It is determined that the non-dimensional crack depth has the value of b = 4.2 and b = 4 for the peak

4

Fig. 3.5 Plot of the energy value of the backwall echoes versus the position of back echo for cracks with different depth under excitation central frequency fc = 10 MHz

4.5

x 10

β=0.44 β=0.44,fitted β=1.76 β=1.76,fitted β=5.28 β=5.28,fitted β=8.33 β=8.33,fitted

4 3.5 3

E

b

2.5 2 1.5 1 0.5 0 0

1

2

3

Position of backwall echo n

4

5

3 Study on the Ultrasonic Attenuation Characteristic Due to Crack … Fig. 3.6 Plot of attenuation coefficient a as a function of non-dimensional crack depth b for different excitation frequencies of fc = 10 MHz and fc = 20 MHz

33

1.4

10MHz, calculated 10MHz,fitted 20MHz, calculated 20MHz,fitted

1.2

α

1

0.8

0.6

0.4

0

2

4

β

6

8

10

attenuation obtained using Eq. (3.4), for different frequencies fc = 10 MHz and fc = 20 MHz, respectively. It is also observed that the energy attenuation coefficients for incident excitation with a higher frequency, i.e. fc = 20 MHz are larger than that of a smaller frequency, i.e. fc = 10 MHz, as expected.

3.4

Conclusions

In this paper, two-dimensional scattering and attenuation of ultrasonic waves in a metal plate containing a crack of different depth has been studied using finite element analysis (FEA). The presence of a dead zone directly under the surface in the pulse-echo test has been recovered. The energy attenuation coefficient was defined to characterize the effect of crack depth on the scattering of elastic waves and the resulted attenuation. It has been shown that the energy attenuation coefficient varies nonlinearly with the crack depth and this change has been approximated by a second order polynomial function. It has also been shown that the maximum attenuation occurs when the crack is located at central plate and the attenuation decreases as the crack approaches plate surface. Acknowledgements This work was supported by the National Natural Science Foundation of China [grant number 51775037].

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References 1. Eriksson, A.S., Bostrom, A., Datta, S.K.: Ultrasonic wave-propagation through a cracked solid. Wave Mot. 22, 297–310 (1995) 2. Wagle, S., Kato, H.: Ultrasonic wave intensity reflected from fretting fatigue cracks at bolt joints of aluminum alloy plates. NDT&E Int. 42, 690–695 (2009) 3. Messineo, M.G., Rus, G., Eliçabe, G.E., et al.: Layered material characterization using ultrasonic transmission. An inverse estimation methodology. Ultrasonics 65, 315–328 (2016) 4. Charlesworth, J.P., Temple, A.G.: Engineering Applications of Ultrasonic Time of Flight Diffraction, 2nd edn. Research Studies Press Ltd, Philadelphia (2001) 5. Capineri, L., Tattersall, H.G., Silk, M.G., et al.: Time-of-flight diffraction tomography for ndt applications. Ultrasonics 30(5), 275–288 (1992) 6. Fukunaga, H., Hu, N., Chang, F.K.: Structural damage identification using piezoelectric sensors. Int. J. Solids Struct. 39, 393–418 (2002) 7. Kline, R.A.: Nondestructive Characterization of Composite Media. Technomic Publishing Company, New York (1992) 8. Rajagopal, P., Lowe, M.J.S.: Scattering of the fundamental shear horizontal guided wave by a part-thickness crack in an isotropic plate. J. Acoust. Soc. Am. 124(5), 2895–2904 (2008) 9. Dattal, D., Kishoret, N.N.: Features of ultrasonic wave propagation to identify defects in composite materials modelled by finite element method. NDT&E Int. 29, 213–223 (1996) 10. Takada, H., Tomura, Y., Aratani, M., et al.: On-line detection system for internal flaws in as-hot-rolled steel strip using ultrasonic probe array. Mater. Trans. 52, 531–538 (2011) 11. Molero, M., Segura, I., Aparicio, S., et al.: On the measurement of frequency-dependent ultrasonic attenuation in strongly heterogeneous materials. Ultrasonics 50, 824–828 (2010) 12. Kishore, N.N., Sridhar, I., Iyengar, N.G.R.: Finite element modelling of the scattering of ultrasonic waves by isolated flaws. NDT&E Int. 33, 297–305 (2000)

Chapter 4

Mimosa Strong Medicine for Maintenance Riku Salokangas , Erkki Jantunen , Martin Larrañaga , and Petri Kaarmila

Abstract The paper describes how the use of Mimosa open source data model supports the development of a low-cost condition monitoring system that is capable to carry out automatic diagnosis and prognosis. Mimosa follows the ISO 13374 definitions (condition monitoring) and links well with the ISO 17359 (diagnosis) and ISO 13381 (prognosis). The Mimosa data model defines all the necessary ontology for the automatic system. As a use case the paper describes the installation of the Mimosa data model in a Raspberry where MariaDB is used as the database engine. A low-cost accelerometer has been installed to a Raspberry thus enabling the collection of vibration data from rolling element bearings of a conveyor. In addition, a low-cost system that uses Arduino is presented for data collection in future use cases. The necessary signal analysis functions are programmed with Python which offers a wide collection of useful functions. The paper summarises the key role of Mimosa in building and using this kind of automatic monitoring systems.

4.1

Introduction

Condition-based maintenance (CBM) is nowadays a well-accepted technology in the industry, and it makes possible to save money and reduce costs over a long period of time. The problem of corrective maintenance is that the component is replaced when it breaks down and there is no information on the Remaining Useful Life (RUL). It is important to think carefully about whether CBM would add value to the availability of a component or machine and, on the other hand, is it possible to avoid an unexpected shutdown by using it. CBM is based on the idea that maintenance is done when it is needed. Maintenance is performed after one or more indicators indicate that the devices do not work or that the performance of the R. Salokangas (&)
E. Jantunen
P. Kaarmila VTT Technical Research Centre of Finland Ltd., P.O. Box 1000, 02044 VTT Espoo, Finland e-mail: riku.salokangas@vtt.fi M. Larrañaga Mondragon Goi Eskola Politeknikoa, Loramendi 4, 20500 Arrasate, Spain © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_4

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equipment is deteriorated [1]. The automatic Condition Monitoring System (CMS) continuously monitors the production system and provides essential information about the condition of the machine or device, which can then be easily analysed by signal processing methods. The goal is to find out if there is a fault in the system and identify it with fault diagnosis. The next step is to predict the RUL and make recommendations for extending it. In addition, post mortems are performed to avoid repeating the same mistakes. The purpose of the paper is to present the benefits of Mimosa in particular and the results of using Mimosa in the Serena EU project will be presented in the future. This paper presents tools needed to perform CBM, first of all, in accordance with the standard and on the other hand cost-effectively. It describes how the use of Mimosa open source data model supports the development of a low-cost CMS that is capable to carry out automatic diagnosis and prognosis. The work demonstrates that Mimosa follows the ISO 13374 definitions (CMS) and links well with the ISO 17359 (diagnosis) and ISO 13381 (prognosis) as the only standard data model so far. It defines all the necessary ontology for the automatic system. As a use case, the paper describes the installation of the Mimosa data model in a Raspberry where MariaDB is used as the database engine. A low-cost accelerometer has been installed to a Raspberry Pi thus enabling the collection of vibration data from rolling element bearings of a conveyor. The aim is to present the necessary signal analysis methods that are programmed with Python, which offers a wide collection of useful functions including envelope analysis in bearing CMSs’. In addition, the paper presents a low-cost system that uses Arduinos and Raspberry Pies for future use cases and summaries tests carried out in the industry.

4.2 4.2.1

ISO Standards ISO-13374

Typically, computer programs written for machine CMS, diagnostics, and prognostics are differently customized and incompatible software so that they cannot exchange information, not to mention synchronized data processing. This means in practice that there is a huge amount of work to be done to integrate these programs. The ISO 13374 standard provides guidance and requirements for what automated maintenance software should be able to provide to ensure that machine CMS and data processing is as effective as possible and benefits the end user as much as possible [2]. Various data processing and analysis methods are required to interpret the data obtained from CMS. The aim of combining these techniques is to proactively determine the cause and severity of faults, the RUL, and the need for action and maintenance [2]. The ISO-13374 recommends either manual or automatic data processing and information flow shown in Fig. 4.1 so that the Condition Monitoring (CM) can be performed successfully. The data stream proceeds from the data collection to the recommended actions, so that the data of the previous processing blocks is transferred to the next level [2].

4 Mimosa Strong Medicine for Maintenance

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Fig. 4.1 Data-processing and information-flow blocks [2, 3]

Data-Processing and Information-Flow Blocks: A full CBM system is composed of 6 different blocks. The standard describes each of them including their functions and how they are interconnecting with each other. This way, both the CBM developer and user are going to be able to use interoperable hardware and software. Figure 4.1 visualizes the functional blocks that the ISO-13374 standard defines [2].

4.2.2

ISO 17359

The ISO17359 standard provides instructions for machines for CM and diagnostics where measured or calculated quantities are, for example, vibration, temperature, flow rates, contamination, power and speed. Measurements usually indicate machine performance, condition, and quality. The standard sets out the procedures that must be taken into account when determining the CM program for all types of machines. It is therefore intended to provide a method that can be implemented when monitoring the condition of the machine. The goal is to identify and avoid root cause failure modes by providing maintenance instructions including instructions for setting alarm criteria, performing diagnosis and prognosis, and improving their confidence level [4]. The procedure that can be used to implement a CM program comprises the following steps: Cost benefit analysis, equipment audit, reliability and criticality audit, selection of appropriate maintenance strategy, selection of monitoring methods, data acquisition and analysis, determination of maintenance actions and review [4]. Detailed information of these steps is given in the standard.

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ISO 13381

The complete process of machine CM consists of five distinct phases: the detection of problems, the diagnosis of the faults and their causes, the prognosis of future fault progression, the recommendation of actions and post-mortems [5]. A prognosis is an estimate of the time that is available before the machine enters a failure condition and probability of an existing or future failure mode. It is based on information and experience of the progress of the fault. The goal is to predict the RUL for the machine user with a sufficient level of confidence. The prognosis process can be described as follows: 1) Define the end point, 2) Determine or evaluate the behaviour and expected degradation of parameters or descriptors, 3) Assess the current state of deterioration, 4) The estimation of the remaining life expectancy or expected time to failure, 5) Define the confidence level, 6) Determine the desired prognostic event horizon [5] (Fig. 4.2).

Fig. 4.2 Prognostics and diagnostics across the failure progression timeline [5, 6]

4 Mimosa Strong Medicine for Maintenance

4.3

39

Mimosa

It is typical that the information required for production equipment is fragmented between different information systems, so that the information required is in the wrong system and, moreover, is targeted at the wrong people. The stored information is scattered across different systems so that, e.g. CM data is in a different place than reporting of the remedies that have been made [7]. The same applies to the diagnostic and prognostic data. This makes it difficult to view data at the same time, and to produce reports, integrated views, or to synchronize data. In this way, one has to tailor and use time to combine different databases and data models. The MIMOSA Open Systems Architecture for Enterprise Application Integration (OSA-EAI) definitions have been designed to integrate all these separate components so that the right information goes to the right person in the right place. The data model comprises the integration of engineering, maintenance, operation and reliability data into one single area. Previously, these pieces have been completely separate from each other, and now it is possible to utilize only one data model and multiply the benefits from data when all the information is in the same place. At the same time, it is also possible to view the data in a synchronized manner and generate reports for those who need it [1, 7–9]. The Machinery Information Management Open Systems Alliance (MIMOSA) is a not-for-profit trade association composed of industrial asset management system providers and end-users. It includes standard open source MIMOSA OSA-EAI and MIMOSA OSA-CBM. MIMOSA OSA-EAI is presented in Fig. 4.3. It is presented as a relational database that includes pre-designed domains such as registry, CM, reliability, maintenance and work management functions. MIMOSA OSA-CBM standardizes the transferring of information in a CBM system. It describes the six functional blocks of CBM systems (Fig. 4.1) and the interfaces between them [7, 8]. • CM – Information management – Build on Open Standards • MIMOSA • Used by e.g. Boeing, Rockwell, B.P., U.S. Navy, VTT etc. [7] • Based to the standards ISO-13374-1, ISO-13374-2 • ISO-13374-1: Condition Monitoring and Diagnostics of Machines. Data Processing, Communication and Presentation. PART 1: General Guidelines • ISO 13374-2: Condition Monitoring and Diagnostics of Machines. Data processing, Communication and Presentation PART 2: Data Processing • MIMOSA OSA-EAI • Open System Architecture for Enterprise Application Integration • Primary domains are registry, CM, reliability, maintenance and work management functions.

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Fig. 4.3 MIMOSA data model diagram

• MIMOSA OSA-CBM • Open System Architecture for Condition-Based Maintenance • Implements ISO-13374-1 • Harmonized with OSA-EAI Figure 4.3 presents the standardized CBM process where the MIMOSA database can be used when saving the data or making queries [2, 10].

4.4 4.4.1

Hardware Raspberry Pi

A Raspberry Pi 3 Model B has been utilized for enabling the collection of vibration data from the rolling element bearings of a conveyor. The Raspberry Pi is a Single-board computer with, Ethernet, wireless LAN and Bluetooth connectivity [11]. The Raspberry Pi is an alternative to very expensive single-board boards. The board has 40-pin extended GPIO, but it does not have an Analog to Digital Convertor (ADC) [11]. The operating system (OS) that has been used is Raspbian, a Debian based OS optimized for the Raspberry Pi hardware [12] (Fig. 4.4).

4 Mimosa Strong Medicine for Maintenance

41

Fig. 4.4 Raspberry Pi 3 Model B [11]

4.4.2

Accelerometer and AD Processing

For making the measurements of the bearings possible a circuit that has an accelerometer has been created. The circuit consists of a RC Low-pass filter (LPF), a programmable gain amplifier (PGA) and an ADC. Below is a detailed explanation of the components. Accelerometer, Filter and Programmable Gain Amplifier. The accelerometer that has been used is the ADXL001, a wide-band, single-axis and analog output accelerometer of ± 70 g. An evaluation board EVAL-ADXL001Z has been utilized to facilitate the evaluation of the accelerometer’s performance [13]. To avoid the aliasing effect, a 720 Hz cut-off frequency RC LPF has been used, with a 220X resistor and a 1lF capacitor. The accelerometer that has been chosen is ± 70 g, but depending on the rotational speed of the machine, the g force that the accelerometer measures could be lower than 10 g. In this case, the signal needs to be amplified. An eight gain selections MCP6S21 microchip has been utilized not only for the amplification of the signal but also to work as a buffer [14, 15] (Fig. 4.5). Analog to Digital Converter and Communication with Raspberry. The external ADC that has been utilized is the ADS131A04. In the Table 4.1 can be seen some of it characteristics. The communication between the Raspberry Pi and the ADC has been done with SPI. The sampling rate used in this use case is 8kSPS. Every 125 microseconds the ADC captures the data of the accelerometer. The sampling frequency has been set configuring the internal clocks that is created from the crystal oscillator. The values of the different internal clock are the following [15] (Table 4.2).

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Fig. 4.5 Image of the evaluation board [16]

Table 4.1 Table with ADS131A04 ADC characteristics [17] Channel

Resolution

Type

Data rates

Protocol

4 Channel

24-bit

delta-sigma (DR)

up to 128kSPS

SPI

Table 4.2 Table with internal clock frequencies [15]

4.4.3

fCLKIN

fICLK

fMOD

fDATA

16.384 MHz

4.96 MHz

1.24 MHz

8 kHz

Low Cost System

Mimosa can be utilized to store the data of a high number of low-cost sensors. Below is described a proposal of how to make a system to store the data of many sensors using Arduino boards and one Raspberry Pi as a gateway with Mimosa. There are multiple types of Arduinos [18] that could be used for this purpose. In this case the suggested system uses Arduino Due. Arduino Due: Is a microcontroller board based on a 32-bit ARM core microcontroller working at 84 MHz. Some of the characteristics of the boards are that it has 54 digital IO’s, 12 analogy inputs, 4 UARTs (hardware serial ports), and a SPI header [19]. The Due allows to make a SPI communication with other SPI devices. The three common options are the MISO (Master In Slave Out), MOSI (Master Out Slave In) and the SCK (Serial Clock) [20]. In addition, the USART0 and the USART1 can be configured as SPI Masters or Slaves [21]. As mentioned above the board has 54 digital outputs, using the free IO’s as a chip selector (CS). In theory a high number of devices could be managed with one Arduino, but in reality that is not possible. Because SPI bus has a maximum capacity that it can support. In addition, the SPI can be used for low meters. People already have managed to use one SPI Master with 5 slaves. A proposal could be to have a system with one Arduino Due that is connected to three 8 channel ADC´s reading from different vibration accelerometers using the above-mentioned SPI, USART0 and USART1.

4 Mimosa Strong Medicine for Maintenance

4.5

43

Signal Analysis

Various techniques have been used to monitor the condition of bearings, such as vibration, acoustic emission, oil contamination, ultrasonic and shock-pulse measurements to detect faulty rolling element bearings. Nevertheless, most of the advanced signal processing methods are associated with vibration measurements. Some studies have used signal processing techniques, such as RMS, kurtosis, FFT, etc., but most of the research has focused on developing advanced signal processing techniques, such as envelope analysis, wavelet, and decision-making techniques such as expert systems and fuzzy logic [5]. Signal processing methods can be divided into the time and frequency domain analysis. The time-domain signal processing methods are simple and are mainly based on statistical parameters such as RMS, mean, kurtosis, crest factor, etc. Trend curves based on RMS value are one of the most used methods to detect correlation between vibration acceleration and wear of the rolling element throughout the lifetime [22–26]. Kurtosis and crest factor values increase as peaks in the vibration signal increase. In this sense, the kurtosis and the crest factor are very sensitive to the shape of the signal. However, it has been found that the skewness is a rather weak method for detecting the fault characteristics of rolling bearings [27], although it can be an effective measure of signals containing asymmetry, i.e. non-linearity. The frequency domain methods were introduced to provide another way of detecting faults in the bearings. FFT is one of the most common methods for converting a signal from a time domain to a frequency domain and generate a spectrum. However, it is not easy to detect the peak caused by a defect due to slipping and masking of other stronger vibrations, except for the effects of harmonics and sidebands of defect frequencies [28]. In addition, the FFT method is based on the assumption of periodic signals that is not suitable for non-stationary signals. However, the rolling element bearing output signals contain non-stationary components for changes in the operating conditions of the machine as well as for bearing itself [29]. Time-frequency analysis is the most popular way to handle non-stationary signals. The Wigner-Ville distribution, the short-time Fourier transform, and the wavelet transform represent a kind of compromise between time and frequency domain methods of the signal and include both time and frequency information. Based on all this information the envelope analysis was selected as a most suitable method to detect bearing fault frequencies. In the envelope analysis the unfiltered vibration acceleration time domain signal is band pass filtered around the bearing’s natural frequency, which is typically between 500 and 3000 Hz, depending on the size of the bearing [30]. Thereafter, the band pass filtered time domain signal is rectified and demodulated using the Hilbert transform. Then the FFT transform is carried out with this envelope signal. This produces an envelope spectrum. The idea of the envelope analysis is to study the bearing fault frequencies such as inner and outer ball pass frequencies and cage defect. By using this method, the bearing fault frequencies can be seen clearly.

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The amplitudes produced by this envelope spectrum are compared to the envelope spectrum of a new bearing and based on that the alarm limits are defined. In this way, information can be obtained in advance when the bearing is decomposing and thus define the RUL.

4.6

Use Case Results

The following is a presentation of the general data architecture used in the EU Serena project and a presentation of powerful Mimosa usage in the edge, cloud and analytics. The vibration acceleration is measured from the bearing with the edge device Raspberry Pi and stored in Raspberry Pi’s own local Mimosa database populated with metadata. The data is then transferred to the Serena cloud so that the actual measurement data is stored in the HDFS file system. The Mimosa database in Serena cloud has already been populated with metadata, and therefore it forms the metadata model of the Serena architecture. Mimosa has links to the vibration data stored in the HDFS file system so that Mimosa always knows exactly what metadata is associated with vibration data. Figure 4.6 shows Serena’s data architecture. Analytics are implemented as a cloud service, and the data is retrieved from the Serena cloud, analysed and returned to the Serena cloud HDFS file system, with links to the metadata in the Mimosa database again. Later on, the idea is to implement the analysis itself into the Raspberry Pi edge device. The alarm limits for the data to be analysed are defined in the Mimosa database. If a fault is detected as a result of the analysis, Mimosa informs about the necessary maintenance actions. The necessary diagnostic and prognostic algorithms, such as envelope analysis, are programmed with Python, utilizing Python’s Numpy, Scipy and Scikit-Learn libraries. Maintenance actions, work orders and their scheduling can also be implemented with Mimosa. The necessary user interfaces can be integrated into the system via REST web services and thus the necessary information is always available in the right place. Figure 4.7 shows a more detailed analytics framework. After all, the benefits of Mimosa are highlighted, especially when there are hundreds or even thousands of bearings under control and the bearings of machines can be located in different factories and countries all over the world. The Mimosa main metadata model consists of an enterprise, a site, a segment, an asset and a measurement location. This combination enables the traceability of measurements regardless of situation. Combined with this, the automatic diagnostics enables further CBM of the bearings regardless of their number. Otherwise, it is difficult, if not impossible, to carry out CBM. In addition, during replacement of bearings, it is easy to update and copy the information including parameters and type data to Mimosa.

4 Mimosa Strong Medicine for Maintenance

Fig. 4.6 Serena’s data architecture solution

Fig. 4.7 Serena’s analytics framework

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Conclusion

This paper introduced the most important standards related to automatic CBM, diagnostics and prognostics. The paper also describes the Mimosa data model and introduces low-cost sensor solutions and data acquisition devices as well as typical methods used for bearing maintenance. In addition, a bearing CM solution for the Serena EU project is introduced. In conclusion, the standard-based open data model solution Mimosa proved to be an excellent tool for combining the various solutions required for CBM, from data acquisition, signal processing and machine state recognition to diagnostics, prognostics and decision support. The purpose of the paper was to present the benefits of Mimosa in particular and the results of using Mimosa in the Serena project will be presented in the future. Altogether, Mimosa really is strong medicine for automatic CBM. It can also be said that low-cost sensor solutions and data acquisition devices will take over the industry in a cost-effective way. These devices are already able to perform quite complicated actions already today, for example, to install Mimosa in the device and, on the other hand, to use an edge device with some analysis in itself. Acknowledgements Research leading to these results has received funding from the EU Horizon 2020 program under the project Serena (Number 767561).

References 1. Arnaiz, A., Iung, B., Al-Najjar, B., Jantunen, E., Holmberg, K., Naks, T., Baglee, D.: A new integrated e-maintenance concept. In: Holmberg, K., Adgar, A., Arnaiz, A., Jantunen, E., Mascolo, J., Mekid. S. (eds.) E-Maintenance, pp. 61–82. Springer, London (2010). chapter 4 2. ISO 13374-1. Condition monitoring and diagnostics of machines – Data processing, communication and presentation – Part 1: General guidelines (2003) 3. The Pennsylvania State University, Applied Research Laboratory 4. ISO 17359. Condition monitoring and diagnostics of machines – General guidelines (2018) 5. ISO 13381-1. Condition monitoring and diagnostics of machines – Prognostics – Part 1: General guidelines (2015) 6. El-Thalji, I.: Dynamic modelling and fault analysis of wear evolution in rolling bearings, Espoo, VTT Technical Research Centre of Finland Ltd, (2016). chapter 2, pp. 15–33, chapter 3, pp. 34–39 7. www.mimosa.org 8. Hegedüs, C., Dominguez Arroyo, P., Di Orio, G., Flores, J.L., Intxausti, K., Jantunen, E., Larrinaga, F., Maló, P., Moldován, I., Schneickert, S.: The MANTIS reference architecture. In: Albano, M., Jantunen, E., Papa, G., Zurutuza, U. (eds.) The MANTIS Book Cyber Physical System Based Proactive Collaborative Maintenance (River Publishers Series in Automation, Control and Robotics Gistrup Denmark), pp. 37–92 (2019). Chapter 3 9. López-Campos, M.A., Crespo Márquez, A., Gómez Fernández, J.F.: Advanced maintenance modelling for asset management, techniques and methods for complex industrial systems. In: Crespo Márquez, A., González-Prida Díaz, V., Gómez Fernández, J.F. (eds.)The Integration of Open Reliability, Maintenance, and Condition Monitoring Management Systems, pp. 43– 55. Springer, London

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10. ISO 13374-2:2007 Condition monitoring and diagnostics of machines – Data processing, communication and presentation – Part 2: Data processing 11. Raspberrypi.org 2019. Raspberry Pi 3 Model B specifications. https://www.raspberrypi.org/ products/raspberry-pi-3-model-b/ 12. Raspbian.org 2019 [Online]. https://www.raspbian.org/ 13. Analog.com 2019 EVAL-ADXL001 board datasheet. https://www.analog.com/media/en/ technical-documentation/evaluation-documentation/EVAL-ADXL001.pdf 14. Microchip 2019, MCP6S21 chip datasheet. http://ww1.microchip.com/downloads/en/ DeviceDoc/21117B.pdf 15. Gorostegui Gabiria, U.: Automatic integration of cyber physical condition monitoring systems as part of computerized maintenance management system, master thesis, Mondragon Unibertsitatea (2017) 16. Analog.com 2019 evaluation boards EVAL-ADXL001. https://www.analog.com/en/designcenter/evaluation-hardware-and-software/evaluation-boards-kits/eval-adxl001.html#eboverview 17. Texas instruments 2019, ADS131A0x Datasheet. http://www.ti.com/lit/ds/symlink/ ads131a04.pdf 18. Arduino products. https://www.arduino.cc/en/main/products 19. Arduino due. https://store.arduino.cc/due 20. Arduino SPI. https://www.arduino.cc/en/reference/SPI 21. Arduino Forum. https://forum.arduino.cc/index.php?topic=283766.0 22. Jantunen, E.: How to diagnose the wear of rolling element bearings based on indirect condition monitoring methods. Int. J. COMADEM 9(3), 24–38 (2006) 23. Schwach, D., Guo, Y.: A fundamental study on the impact of surface integrity by hard turning on rolling contact fatigue. Int. J. Fatigue 28, 1838–1844 (2006) 24. Harvey, T.J., Wood, R.J.K., Powrie, H.E.G.: Electrostatic wear monitoring of rolling element bearings. Wear 263, 1492–1501 (2007) 25. Yoshioka, T., Shimizu, S.: Monitoring of ball bearing operation under grease lubrication using a new compound diagnostic system detecting vibration and acoustic emission. Tribol. Trans. 52(6), 725–730 (2009) 26. Zhi-qiang, Z., et al.: Investigation of rolling contact fatigue damage process of the coating by acoustics emission and vibration signals. Tribol. Int. 47, 25–31 (2012) 27. Tyagi, S.: A comparative study of SVM classifiers and artificial neural networks application for rolling element bearing fault diagnosis using wavelet transform preprocessing. World Acad. Sci. Eng. Technol. 43, 309–317 (2008) 28. Ocak, H., Loparo, K.A., Discenzo, F.M.: Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics. J. Sound Vibr. 302, 951–961 (2007) 29. Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Sig. Process. 18, 199–221 (2004) 30. Halme, J., Andersson, P.: Rolling contact fatigue and wear fundamentals for rolling bearing diagnostics-state of the art. J. Eng. Tribol. 224, 377–393 (2009)

Chapter 5

Fault Diagnosis of Motor Broken Bar Using Current and Vibration Fusion Signal Xiaoyun Gong, Yongjie Jing, Wenliao Du, Hongchao Wang, and Baowei Zhao Abstract Motor broken bar is a common fault for the asynchronous motor. An intelligent diagnosis method for motor broken bar fault is presented. The intelligent diagnosis method combining Ensemble Empirical Mode Decomposition (EEMD) and Support Vector Machine (SVM) is used to identify the fault type of the motor broken bar. EEMD is used to extract the frequency character from motor vibration signal and current signal. By comparing the stability of the frequency band energy characteristics between the vibration signal and the current signal under different working conditions, it is concluded that the results of vibration signal is better than the current signal. At the same time, the quad-classifier kernel function parameters are optimized using the grid selection method. A multi-information fusion method based on current and vibration signal is designed. It is effective to identify broken bar fault from motor multi-faults. It can resolve the difficulty of multi-component fault feature extraction from multi-faults with broken bar fault.

5.1

Introduction

As an important energy converter, motor is ubiquitous in engineering due to its excellent performance and high applicability [1]. The motor broken bar is a common fault type for the asynchronous motor. When the motor breaks, the rotor speed will change, causing abnormal vibration of the motor and even resulting in huge damage to the motor. Scholars at home and abroad studied diagnostic methods of motor fault on the basis of current signals. Antoni proposed a spectral kurtosis method based on time-frequency analysis, which is suitable for fault signal detection under strong noise [2–4]. Rangel-Magdaleno used Hilbert spectrum for detecting the initial broken strip and the detection of early faults [5]. Zhao proposed a fault detection method based on Hilbert transform and wavelet packet to eliminate the influence of X. Gong (&)
Y. Jing
W. Du
H. Wang
B. Zhao Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_5

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fundamental components [6]. With wavelet packet method for extracting the fault vector of motor rotor broken bar, Qiao presented a fault diagnosis method based on firefly-particle swarm neural network [7]. Li used Hilbert modulus spectrum analysis proposed for rotor broken bars [8]. However, above diagnostic methods are only based on current signals. In industrial production, the working environment of the bearing is very complicated. Once the load bearing has a chance of failure, it will have a huge impact. This impact will be transmitted to the motor through the shaft and the coupling, which will easily lead to the broken wire of the motor. It is not easy to extract fault features from fault signal of the bearing overlaps with the broken signal of the motor. SVM is a machine learning method developed on the basis of statistical learning theory. It has good learning ability for small training samples, a simple structure and high generalization ability. It can effectively overcome small samples and over-fitting [9]. Its main idea is to construct a classification of samples by nonlinearly mapping the training samples from the original space to a high-dimensional feature space and constructing the optimal classification hyperplane in the high-dimensional space [10]. This paper uses EEMD and SVM methods to identify the motor broken fault from coupling faults. A multi-information fusion method based on current and vibration signals is designed. The current signal is used to separate the broken fault frequency of the motor, and the vibration signal is used to separate the bearing fault frequency. Firstly, the measured vibration signal and current signal are decomposed into a series of IMF components by EEMD. Then the signal energy of each IMF component is calculated as the feature vector. Finally, the signal energy of the IMF component is output to the trained SVM for classification. Experimental datas are used to diagnose the broken bar fault in the motor coupling faults.

5.2 5.2.1

Intelligent Diagnosis Method Based on EEMD and SVM EEMD

The idea of the EEMD method is that the average value obtained by multiple measurements of a component can improve the accuracy of the measurement. The method can obtain a set of IMF components. For the original signal xðtÞ, its EEMD decomposition steps are as follows [11]: (1) Adding a set of white noise sequences with mean values of zero and equal variance to the original signal ðj ¼ 1; 2; 3;


; mÞnðtÞ, and then obtaining a signal consisting of m signals. xj ðtÞ ¼ xðtÞ þ nj ðtÞ

ð5:1Þ

5 Fault Diagnosis of Motor Broken Bar …

51

(2) Each signal in the noisy signal group xj ðtÞ is EMD decomposed to obtain a corresponding IMF component of each signal, and the signal xj ðtÞ can be expressed as xj ðtÞ ¼

n X

ci;j ðtÞ þ rin ðtÞ

ð5:2Þ

i¼1

Where: ci;j is the i-th IMF component of xj ðtÞ; rin ðtÞ is the residual function of signal xj ðtÞ. (3) Repeat steps (1) and (2) for each xj ðtÞ with white noise and repeat m times to obtain m-group IMF components. (4) Find the mean of the m-group IMF component and the mean of the m-group margin, then the original signal xj ðtÞ can be expressed as xðtÞ ¼

n X

ci þ r

ð5:3Þ

i¼1

5.2.2

Fault Feature Extraction and Intelligent Diagnosis

The original signal is decomposed and reconstructed by the EEMD algorithm, which reduces the interference of unrelated components. EEMD method is used to decompose the original signal, and the signal energy of the IMF is obtained as the feature vector input into the SVM for classification training. The steps of fault diagnosis method based on the EEMD decomposition and SVM are as follows: (1) The motor vibration signal and the current signal are collected for EEMD decomposition, and a set of IMF components arranged in descending order of frequency is obtained. (2) Calculate the correlation coefficient between each component and the original signal, filter the IMF component according to the correlation coefficient, set the threshold h ¼ 0:2, retain the IMF component of the correlation coefficient qxy  0:2, and reconstruct the signal. (3) The Hilbert transform is performed on the reconstructed signal, and the envelope of the reconstructed signal is obtained and its spectrum is obtained. (4) Feature vector extraction. The energy Ei of the nth-order IMF component of each set of signals is obtained. Because c1 ; c2 ;


; cn , the frequencies involved are different, so the signal energy contained is different, then the n-dimensional vector fE 1 ; E2 ;


; En g forms an automatic division of signal energy. Ei ¼

X

Cik2 ðtÞ

ð5:4Þ

where Cik is the magnitude of the discrete points of each IMF component.

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(5) Classifier training. The n-dimensional vector obtained by the m training samples is used as the feature vector of the classification identification, and input into the electric SVM for training to obtain the SVM classifier. (6) Classification identification diagnosis. Extract the n-dimensional vector of the test sample into the trained classifier, and the classifier outputs the classification diagnosis result. The specific steps are shown in Fig. 5.1. Among the inner product kernel functions of SVM, the most studied kernel functions are linear kernel function, polynomial kernel function (PKF), radial basis kernel function (RBF), and perceptron kernel function (Sigmoid kernel function). In this paper, the SVM of polynomial kernel function is chosen, and the optimal parameters suitable for the kernel function are found by the grid optimization method. Fig. 5.1 Diagnostic method flow

5 Fault Diagnosis of Motor Broken Bar …

5.3

53

Experimental Verification

The test bench produced by SpectraQuest is used to simulate the motor broken fault. The experiments such as motor no-load broken strip, motor load broken strip, coupling faults of motor broken bar and motor bearing fault, coupling faults of motor broken bar and load bearing fault are simulated. The structure of the test bench is shown in Fig. 5.2, and the motor parameters of the test bench are shown in Table 5.1. In the selection of the sensor, the piezoelectric acceleration sensor and the laser speed sensor are selected according to the experimental requirements. Test under the condition that the motor is under no load and under load, set the motor speed to 2400 r/min, the sampling frequency is 1280 Hz, and the analysis points are 32768. The coupling faults takes into account the bearing fault, the sampling frequency is 12800 Hz, and the analysis points are 131072. The experiment is divided into six groups, which are motor no-load normal state, motor no-load broken-bar state, motor load normal state, motor load broken bar state, coupling faults of motor broken strip and load bearing inner ring fault state, coupling faults of motor broken bar and motor bearing outer ring fault status. The load is loaded by the magnetic powder brake. The experimental conditions of each group are shown in Table 5.2. The fault frequency of the inner ring of the load bearing is calculated to be 193, 193.5 Hz, and the fault frequency of the outer ring of the motor bearing are 120, 120.5 Hz. The reconstructed signal envelope spectrum of six states is obtained through experiments, and results are shown in Fig. 5.3.

Fig. 5.2 Experiment bench structure

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Table 5.1 Experimental motor parameters

Parameter

Numerical value

Phase Relative number Number of stator slots Number of rotor slots Stator inner diameter Rotor outer diameter Air gap length Rated power Rated voltage

3 1 34 24 82.1 mm 80.5 mm 0.8 mm 370 W 220 V

Table 5.2 Six sets of experimental conditions Grouping

Motor status

A B

Motor no-load normal state Motor no-load break state

C D

Motor 75% load normal state Motor 75% load breaking state

E

Coupling faults of motor broken bar and motor bearing fault status Coupling faults of motor broken bar and load bearing fault status

F

Vibration signal failure frequency (Hz)

Current signal failure frequency (Hz)

39.67, 40.11, 239.56, 239.78

39.78 , 40.22

37, 41, 238, 236, 234

38, 42

In Fig. 5.3(a), it can be seen that when the motor is broken, obvious peaks are obvious at the fault modulation frequency (0.22 Hz) and its multiplication (0.44, 0.66 Hz). In Fig. 5.3(c), the result of current signal is not good. In Fig. 5.3(b) and (d), there is a sharp peak at the fault modulation frequency (2 Hz), and it is clearly distinguished from the surrounding frequency. Bearing fault frequencies 120, 193.5 Hz appear on the envelope spectra of Fig. 5.3(e) and (f). The motor broken bar vibration is too weak and the frequency of the fault is submerged. In Fig. 5.3(g) and (h) that in the case of coupling faults, the current signal can still separate the broken bar fault, and the amplitude of the fault frequency is larger and more reliable. 10 sets of vibration signal data are taken from different states of the motor (motor load normal, motor load broken bar, coupling faults of motor broken strip and load bearing inner ring fault, coupling faults of motor broken bar and motor bearing

5 Fault Diagnosis of Motor Broken Bar …

˄a˅No-load motor broken bar vibration

˄c˅No-load motor breaking current

55

˄b˅Load motor broken bar vibration

˄d˅Load motor breaking current

0.15 Broken strip normal

0.05

Amplitude(g)

0.1

120Hz

0 0

200

100

0.05

0 0

50

100

150

200

250

Frequency(Hz)

˄e˅Coupling faults of motor broken bar and motor bearing outer ring fault vibration ˄f˅Coupling faults of motor broken strip and load bearing inner ring fault vibration 0.03

10

1.5

-3

Broken strip

2Hz

1 Amplitude(g)

0.02

normal

0.5 0 5

0

10

0.01

0 0

50

100

150

200

250

Frequency(Hz)

˄g˅Coupling faults of motor broken bar and motor bearing outer ring fault current ˄h˅Coupling fault of motor broken strip and load bearing inner ring fault current

Fig. 5.3 Reconstructed signal envelope spectrum under different states

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Fig. 5.4 Category labels in different states and their distribution in 9-dimensional space

Table 5.3 Statistical analysis of small sample SVM classification results in four states of vibration signal motor Kernel function

c

g

Training samples

Test samples

False judgment

Correct rate

Time/s

Linear kernel function Polynomial kernel function RBF kernel function Sigmoid kernel function

1

1

40

40

2

95%

0.000397

1

1

40

40

9

77.5%

0.000454

1

1

40

40

1

97.5%

0.000498

1

1

40

40

2

95%

0.000471

Table 5.4 Statistics of small sample SVM classification results in four states of current signal motor Kernel function

c

g

Training samples

Test samples

False judgment

Correct rate

Time/s

Linear kernel function Polynomial kernel function RBF kernel function Sigmoid kernel function

1

1

40

40

13

67.5%

0.000506

1

1

40

40

10

75%

0.000468

1

1

40

40

11

72.5%

0.000497

1

1

40

40

15

62.5%

0.000471

5 Fault Diagnosis of Motor Broken Bar …

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（a）Linear kernel function

（b）Polynomial kernel function

（c）RBF kernel function

（d）Sigmoid kernel function

Fig. 5.5 Fusion signal SVM classification result prediction

Table 5.5 Statistics of multi-sample SVM classification results in four states of current and vibration signal motor Kernel function

c

g

Training samples

Test samples

Linear kernel function Polynomial kernel function RBF kernel function Sigmoid kernel function

1

1

40

40

1

1

40

1

1

1

1

False judgment

Correct rate

Time/s

2

95%

0.000425

40

1

97.5%

0.000466

40

40

1

97.5%

0.000520

40

40

13

67.5%

0.000510

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outer ring fault). The above four types of vibration signals IMF are input as training data into the SVM four classifier for constructing the motor broken strip coupling faults in the SVM, wherein the parameters are selected as c = 1, g = 1, and the category labels of the training set are in 9 dimensions. The distribution is shown in Fig. 5.4. 10 sets of data are extracted from the samples of the four states of the motor broken bar to form a test set, which is input into the newly constructed four 4 classifiers for prediction. The accuracy and time of each classification prediction are shown in Table 5.3. Similarly, the current signals under different motor states are used to classify the motor coupling faults. The accuracy and time of each classification prediction are shown in Table 5.4. The experimental data of the motor four states are selected, some data are used as training samples, and others are used to be input into the four classifier combined with current and vibration signal as test samples. The predicted results are shown in Fig. 5.5. The prediction results are shown in Table 5.5.

5.4

Discussion and Conclusion

It can be seen from Fig. 5.3 that the diagnostic method based on EEMD and Hilbert envelope demodulation can suppress noise, highlight the fault frequency, and the motor broken fault can be accurately extracted. It can be found from Tables 5.3 and 5.4 that the accuracy of the classification based on the vibration signal SVM classifier is very high, except for the classification accuracy of the polynomial kernel function is lower, the accuracy of the other three SVM classifications is over 95%. The accuracy of classification based on current signal SVM classifier is relatively low, the classification effect is not good, and the accuracy of the Sigmoid kernel function classifier classification is even as low as 62.5%. In Table 5.5, the accuracy of the SVM classifier classification based on current and vibration signals is very high. Although the accuracy of the Sigmoid kernel function SVM classifier is somewhat low, the other three have reached more than 95%. Using EEMD and SVM method to diagnose broken bar from motor coupling faults, the following conclusions can be obtained: (1) The motor combined with the Hilbert demodulation analysis method can extract the fault frequency information of the motor broken strip, and realize the accurate diagnosis of the broken motor fault, no matter whether it is under no load or load. From the results of the vibration signal and the current signal, the effect of the current signal is better than the vibration signal analysis. (2) For the coupling faults of the motor, the classification effect of the classifier based on the vibration signal is better than that of the classifier based on the current signal.

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(3) Based on the current and vibration signal, SVM classifier classification accuracy is very high. It can accurately identify the motor broken bar fault, and achieve the fusion of current signal and vibration signal in the intelligent diagnosis of motor broken bar fault. Acknowledgements This research is supported by National Natural Science Foundation of China, (No. 51405453, U1804141), and by International Science and Technology Cooperation Projects of Henan Province (No. 182102410052).

References 1. Lin, X.: Fault diagnosis of motor bearing based on combination of wavelet packet and EMD. Taiyuan University of Technology (2010) 2. Antoni, J.: The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech. Syst. Signal Process. 20(2), 282–307 (2006) 3. Antoni, J., Randall, R.B.: The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Signal Process. 20(2), 308–331 (2006) 4. Antoni, J.: Fast computation of the kurtogram for the detection of transient faults. Mech. Syst. Signal Process. 21(1), 108–124 (2007) 5. Rangel-Magdaleno, J., Peregrina-Barreto, H., Ramirez-Cortes, J., et al.: Hilbert spectrum analysis of induction motors for the detection of incipient broken rotor bars. Measurement (2017). S0263224117303792 6. Zhao, Q., Pang, J., Wang, W., Gao, M., Wei, N.: Study on fault diagnosis method of rotor broken bar of main fan of coal mine. China Coal 43(08), 105–108+127 (2017) 7. Qiao, W.: Firefly-particle optimization neural network for fault diagnosis of rotor broken bars in induction motors. Motor Control Appl. 44(01), 83–88 (2017) 8. Li, H., Wei, Y., Yang, A.: Overview of fault diagnosis methods for induction motors based on current characteristic signals. Value Eng. 37(502)(26), 167–169 (2018) 9. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995) 10. Wang, J., Wu, Q., Qin, B., et al.: Prediction of oxygen demand for BOF using particle swarm optimization support vector machine. Foundry Technol. (8), 1806–1809 (2014) 11. Zhao, B., Gong, X., Jing, Y., Wu, C.: Diagnostic method of motor broken bar EEMD based on vibration signal. Mining Mach. 46(08), 69–75 (2018)

Chapter 6

Mixed Kernel Functions for Multivariate Statistical Monitoring of Nonlinear Processes Karl Ezra S. Pilario

and Mahmood Shafiee

Abstract Machine learning techniques have now become pervasive in the field of process condition monitoring. In particular, kernel methods are those that use kernel functions to allow for the efficient nonlinear analysis of process data by projecting them onto high-dimensional spaces. A widely used kernel machine in multivariate process monitoring is kernel principal components analysis (KPCA). Many choices of kernel functions were used in previous KPCA studies. However, the use of single kernels alone was recently shown to give only limited expressive ability. In this work, we explored the impact of combining various kernel functions to the performance of KPCA for condition monitoring. Fault detection performance is defined by percent correct detection of faulty states and non-detection of normal states. Optimal kernel parameters were obtained using the genetic algorithm (GA). Visualizations of the boundary between normal and faulty states are provided for demonstration in a chemical process case study. This work can inform the development of mixed kernels for nonlinear process monitoring, not only in KPCA, but also in other kernel machines.

6.1 Introduction Data-driven machine learning methods are actively being developed for condition monitoring in the process industries. This is mainly due to the increasing rate and Supported by the Engineering Research and Development for Technology (ERDT) Program of the Department of Science and Technology (DOST), Philippines. K. E. S. Pilario (B) · M. Shafiee Department of Energy and Power, Cranfield University, Bedfordshire MK43 0AL, UK e-mail: [email protected] M. Shafiee e-mail: [email protected] K. E. S. Pilario Department of Chemical Engineering, University of the Philippines, Diliman, Quezon City 1101, Philippines © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_6

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volume of data being generated during the operation of industrial plants [1]. Plant data from hundreds to thousands of process variables are now being collected every few seconds [2]. By extracting useful information from the data, process condition monitoring can be performed more effectively. This paper focuses on fault detection, whose aim is to determine whether an abnormal event has occurred in the process accurately and as early as possible [1]. In a recent literature review, principal components analysis (PCA) based feature extraction was shown to be the most prevalent method used in data-driven process monitoring [1]. PCA is an unsupervised learning machine which seeks a reduced set of uncorrelated feature variables, which are linear combinations of the original variables, that retains the most information in terms of variance of data [3]. Monitoring the PCA features instead of the original variables is preferred because the multivariate nature of the data is treated upon using PCA, e.g. correlated variables [2]. Since PCA only considers linear relationships, a nonlinear extension of PCA was proposed by Schölkopf et al. [3], called kernel PCA (KPCA). KPCA uses kernel functions to approximate the covariance among the variables prior to feature extraction. Many kernel functions are available, the most widely used of which is the Gaussian radial basis function (RBF). For instance, Choi et al. [4] used the RBF kernel in KPCA for fault detection and diagnosis. However, it has been shown recently that single kernels have limited flexibility in capturing nonlinearities among process variables [5]. The issue is related to the tradeoff between the interpolation and extrapolation ability of the kernel. This limitation can be alleviated by combining kernels [6]. In this paper, we aim to explore the impact of various choices of kernel combinations to fault detection performance in multivariate processes. The results from this work can inform the development of mixed kernels in other kernel machines, such as Support Vector Machines (SVM) and Gaussian processes, for condition monitoring. The paper is organized as follows. KPCA is revisited in Sect. 6.2. Mixed kernels and parameter tuning are discussed in Sect. 6.3. KPCA results and discussion are given in Sect. 6.4. We conclude the paper in Sect. 6.5.

6.2 KPCA for Condition Monitoring The KPCA method for condition monitoring by Choi et al. [4] is adopted in this paper. Let xi ∈  M , i = 1, 2, . . . , N denote a training data set of N samples of M process variables. The data is normalized to zero mean and unit variance. Using a kernel function K (· , ·), the sample covariance is estimated as [4]:     K = K i j = K (xi , x j ) ∈  N ×N .

(6.1)

The kernel matrix, K is then centered as [4], Kc = K − 1 N K − K1 N + 1 N K1 N

(6.2)

6 Mixed Kernel Functions for Multivariate Statistical Monitoring ...

63

where 1 N ∈  N ×N is a matrix with (1 N )i j = 1/N . The centered kernel matrix is decomposed as [3]: (6.3) Kc /N = VVT where the columns of V are the eigenvectors, α i ∈  N , i = 1, . . . , N , and  = diag(λ1 , λ2 , ...λ N ) is a diagonal matrix of decreasing eigenvalues. From Eq. (6.3), the projection matrix is formed as P = V−1/2 ∈  N ×N where −1/2 is used to scale the eigenvectors so that α i , α i  = 1/λi . Lastly, the KPCA features, tk , are obtained using the projection [5]: T = [tk ]k=1,...,N = PrT Kc ∈ Rr ×N

(6.4)

where Pr denotes the first r columns of P, and tk ∈ r is a feature vector at the kth sampling instant. In this paper, r is chosen automatically using a 99.9% cumulative percent variance (CPV) scheme [7]. Equation (6.4) effectively projects the raw data onto a high-dimensional space to account for nonlinearly related variables. To monitor the KPCA features using multivariate statistics, the T 2 index is commonly used, as given by [4]: (6.5) Tk2 = tkT r−1 tk where r = diag(λ1 , λ2 , ...λr ). The T 2 distribution can be estimated from N samples under normal operation via kernel density estimation (KDE) [5]. The upper control limit (UCL), denoted as 2 , is then computed based on a significance level, α. A test sample is deemed TUCL 2 during online monitoring. faulty whenever Tk2 exceeds TUCL

6.3 Mixed Kernels 6.3.1 Local and Global Kernels Kernel functions can be categorized as either local or global [6], having good interpolation and extrapolation abilities, respectively. Interpolation ability refers to the ability of a kernel to learn patterns within the region in the data space where the training samples are located. Extrapolation ability refers to the ability to infer patterns beyond this region. By combining a local and global kernel, both abilities can improve simultaneously [5]. The general form of a convex combination of kernels is given by [6]: (6.6) K mix = ωK global + (1 − ω)K local where ω ∈ [0, 1] is a tunable scalar weight. Table 6.1 lists the mixed kernels studied in this paper, which are taken from [8]. Only a linear kernel is used as global kernel to keep the investigation simple.

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Table 6.1 Mixed kernels considered in this paper (see [8]). Code Name K global MK1

Linear + Gaussian RBF

1 + x, x 

MK2

Linear + Rational Quadratic

1 + x, x 

MK3

Linear + Laplacian

1 + x, x 

MK4

Linear + Cauchy

1 + x, x 

MK5

Linear + Sigmoid

1 + x, x 

MK6

Linear + Multiquadric 1 + x, x 

MK7

Linear + Inverse Multiquadric

1 + x, x 

K local    2 exp − x−x c 1−

x−x 2 x−x 2 +c

   exp − x−x c    2 −1 1 + x−x c   x,x  tanh dim(x) +c  x − x 2 + c2  −0.5 x − x 2 + c2

In each mixed kernel, c and ω are the tunable kernel parameters. The mixed kernel, K mix (x, x ), is used in KPCA via Eq. (6.1).

6.3.2 Kernel Parameter Tuning Kernel parameters need to be tuned to maximize fault detection performance. At optimal parameters, different mixed kernels can also be compared at their best settings. In this paper, performance is defined as the correct monitoring rate (CMR) [7], expressed as CMR = (%DR + %NDR)/2, where: 2 |faulty) no. of samples (T2 > TUCL × 100% (6.7) no. of samples (faulty) 2 no. of samples (T2 < TUCL |normal) × 100% (6.8) %NDR (nondetection rate) = no. of samples (normal)

%DR (detection rate) =

which is computed from both training and test sets. Many approaches can be used to maximize CMR while changing the kernel parameters. Genetic algorithm (GA) based optimization was used in this study, as did Jia et al. [7], to explore the search space efficiently.

6.4 Case Study In this section, mixed kernel based KPCA is evaluated in a simulated continuous stirred tank reactor (CSTR) case study. Details of the CSTR are available in [5].

6 Mixed Kernel Functions for Multivariate Statistical Monitoring ...

2 UCL

2 UCL

(a) PCA only

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(b) MK1 Detected at Sample 130

Detected at Sample 199

2 UCL

2 UCL

(c) MK5

(d) MK2

Detected at Sample 130

Detected at Sample 109

Fig. 6.1 T 2 contour maps and monitoring charts using: a PCA only; b KPCA with MK1; c KPCA with MK5; d KPCA with MK2; see Table 6.1 for details

Only two process variables, namely the outlet concentration C [mol/L] and temperature T [K ], are used for monitoring. The training data set consists of 300 samples of C and T under normal operation of the CSTR. The test set consists of the same number of samples, but with a sensor drift fault in the variable T from the 75th sample onwards as in T := T − 0.3t. Note that the fault magnitude starts small, but then it worsens in time. In this example, the goal is to find a boundary around the normal samples with the maximum CMR using KPCA with various mixed kernels. This boundary is the locus 2 in the data space, defined at α = 99% significance level of the T 2 distribution of TUCL at normal operation for this case study. The search space for kernel parameter tuning is

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Table 6.2 Monitoring performance of optimized mixed kernels Code c ω CMR, % PCA MK1 MK2 MK3 MK4 MK5 MK6 MK7 †

N/A 6.760 0.884 4.018 3.234 0.492 3.626 1.667

N/A 0.0980 0.0824 0.1804 0.0078 0.0235 0.1882 0.0196

76.69 87.71 92.64 91.94 89.69 87.57 89.61 88.01

Detection time† Sample 199 Sample 130 Sample 109 Sample 115 Sample 130 Sample 130 Sample 100 Sample 130

Detection Time = first occurrence of 6 successive alarms.

c ∈ [0.1, 100] and ω ∈ [0, 1]. The GA settings for maximizing CMR are as follows: population size, 30; number of bits in a chromosome, 16; number of elites retained in each generation, 5; crossover rate, 100%; mutation rate, 5%; maximum number of iterations, 25. The GA implements fitness-proportionate selection with elitism. Results for selected methods are shown in Fig. 6.1.1 Notice first that the process indeed exhibit nonlinear behavior, due to the inverse relationship between C and 2 boundary is inaccurate, as shown in Fig. 6.1(a). T . Hence, an ellipsoidal PCA-TUCL Using PCA alone, the fault was detected only at Sample 199. Among the mixed kernels considered in this work, only MK1 and MK5 gave the 2 boundary, that is, one that is smooth and able to learn the expected shape of the TUCL nonlinear relationship between C and T . For MK1 and MK5, the detection time were both at Sample 130, which is significantly earlier than PCA. In other kernel choices, such as in Fig. 6.1(d), the shape of the boundary is not reliable for monitoring due to its unusual shape. Although these kernels gave earlier detection times and higher 2 boundary visualizations prove CMRs than MK1 and MK5 (see Table 6.2), the TUCL that they merely overfit to the training samples. Hence, mixtures of RBF and sigmoid kernels to linear kernels are recommended in KPCA-based condition monitoring. The basis for using sigmoid kernels is strengthened by the fact that they are commonly used in neural networks.

6.5 Conclusion In this paper, seven different mixtures of global and local kernels were investigated for Kernel PCA-based condition monitoring. The genetic algorithm was used to tune their parameters. Using a nonlinear CSTR case study, it was found that the use of RBF/sigmoid and linear kernel mixtures is reliable for condition monitoring, based 2 boundary and detection time. on visualizations of the TUCL 1 This figure was generated using the code available in https://uk.mathworks.com/matlabcentral/ fileexchange/69941-kernel-pca-contour-maps-for-fault-detection.

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In the future, many other kernel combinations can also be investigated, such as products of kernels, integration of kernels, etc. These can lead to more accurate feature extraction tools for nonlinear process monitoring. Conflict of Interest The authors declare that they have no conflict of interest.

References 1. Alauddin, M., Khan, F., Imtiaz, S., Ahmed, S.: A bibliometric review and analysis of datadriven fault detection and diagnosis methods for process systems. Ind. Eng. Chem. Res. 57(32), 10719–10735 (2018). https://doi.org/10.1021/acs.iecr.8b00936 2. Kourti, T.: Process analysis and abnormal situation detection: from theory to practice. IEEE Control Syst. Mag. 22(5), 10–25 (2002). https://doi.org/10.1109/MCS.2002.1035214 3. Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998) 4. Choi, S.W., Lee, C., Lee, J.M., Park, J.H., Lee, I.B.: Fault detection and identification of nonlinear processes based on kernel PCA. Chemom. Int. Lab. Syst. 75, 55–67 (2005). https://doi.org/10. 1016/j.chemolab.2004.05.001 5. Pilario, K.E.S., Cao, Y., Shafiee, M.: Mixed kernel canonical variate dissimilarity analysis for incipient fault monitoring in nonlinear dynamic processes. Comput. Chem. Eng. 123, 143–154 (2019). https://doi.org/10.1016/j.compchemeng.2018.12.027 6. Jordaan, E.M.: Development of robust inferential sensors: industrial applications of support vector machines for regression. Ph.D. Thesis, T.U. Eindhoven (2002). https://doi.org/10.6100/ IR561175 7. Jia, M., Xu, H., Liu, X., Wang, N.: The optimization of the kind and parameters of kernel function in KPCA for process monitoring. Comput. Chem. Eng. 46, 94–104 (2012). https://doi.org/10. 1016/j.compchemeng.2012.06.023 8. Kernel Functions for Machine Learning Applications. http://crsouza.com/2010/03/17/kernelfunctions-for-machine-learning-applications/. Accessed 25 Apr 2019

Chapter 7

Compound Faults Separation Based on Intrinsic Characteristic-Scale Decomposition and Sparse Component Analysis Yansong Hao, Huaqing Wang, Liuyang Song, and Lingli Cui

Abstract The frequent occurrence of rotating machinery faults seriously affects the operation of the equipment and the production of the enterprise. And it is worth mentioning that there are few single faults in the rotating equipment failure while the multiple faults are the norm, which undoubtedly increases the difficulty for fault diagnosis. Therefore, it is of great essentiality to address compound faults of rotating machinery. A novel compound faults separation method based on intrinsic characteristic-scale decomposition (ICD) was suggested to detect multi-faults in the case of underdetermined blind source separation (UBSS) when traditional diagnosis techniques fail. To achieve UBSS, ICD is utilized to decompose a single observation signal into multiple product components (PCs). Then, sparse representation is used to improve the signal sparsity, guaranteeing the normal operation of the sparse component analysis (SCA) algorithm. In addition, because the compound fault features cannot only be extracted by ICD, the spares-promoted PCs are arranged in the SCA processing to separate the multiple signal. Simulations and experiments based on the proposed method was successfully verified. Meanwhile, the Empirical mode decomposition (EMD)-based independent component analysis (ICA) is utilized as a contrast to verify the effectiveness of suggested method. The results suggest that the proposed method can deal with the multiple signal separation of roller bearing.

Y. Hao
H. Wang (&)
L. Song College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China e-mail: [email protected] L. Cui Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing, China © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_7

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Intelligent equipment diagnosis has become a focus of development in industrial society. As an indispensable part of mechanical equipment, rotating machinery is of great magnificence to the industrial society. Roller bearings are fragile components in rotating machinery, and their inevitable sudden failures often cause a lot of economic losses [1]. Usually, bearing failure is more common with multiple source faults. The generation of multiple faults has brought great difficulties to the detection and identification of critical equipment, and has become a difficult topic in the field of mechanical fault diagnosis. To guarantee the bearing is operated under healthy conditions, the development of advanced signal processing methods for separating and identifying multiple faults characteristics has become a key issue in mechanical fault diagnosis [2]. Multi-source signal separation method has become a research hotspot in the field of machinery today. As a mainstream multi-source signal separation method, blind source separation (BSS) has been widely used in various fields including mechanical fault diagnosis [3]. Currently, there are two main types of BSS methods in the field of mechanical fault diagnosis. One is based on signal independence called independent component analysis (ICA) method, and the other is based on signal sparsity called sparse component analysis (SCA) method. ICA originated earlier and it is more mature now. In recent years, it has been used by many scholars to separate source signals in mechanical fault diagnosis. For instance, Li et al. [4] presented a method to diagnose the compound faults based on the ICA algorithm, improving the effectiveness of diagnosis processing. Luo et al. proposed a technique combined VMD with ICA, separating the multiple source signals under the undetermined condition [5]. However, ICA algorithm needs to keep the source signal as independent as possible. And there is a certain degree of mutual interference between various source signals in recent complex industrial environment, making it increasingly difficult for ICA method to exert its advantages. Moreover, one of the inevitable steps in ICA is to estimate the number of source signals. Once an error occurs in this step, the result of the algorithm will be inaccurate. Therefore, some researchers have studied other approaches to address the multiple source problems. As another mainstream BSS algorithm, SCA is gradually being paid more attention by researchers and applied to various fields including mechanical faults areas [6]. It is different from the ICA algorithm and it uses the sparsity of the signal to complete the separation of the source signal. Hao et al. proposed a novel SCA method based on three-dimensional feature, separating the multiple rotating machinery faults in the SCA framework [7]. However, the signal must have sufficient sparse characteristics to ensure the normal operation of the SCA algorithm. Vibration signal usually has not the proper sparsity due to the complex condition. To address the above problem and improve the sparsity of signal, a novel SCA method based on the intrinsic characteristic-scale decomposition (ICD) algorithm and Majorization-Minimization (MM) algorithm is suggested here. ICD is a novel decomposition method which can effectively handle non-stationary rotating device

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signals [8]. MM can accurately solve the minimum value problem to achieve the purpose of sparse optimization [9]. The rest of parts are arranged as follows. The basic theories of ICD and MM are described in Sect. 7.2. Section 7.3 shows the details of the approach based on the suggested framework. Simulated and experimental results are mainly displayed in Sect. 7.4. At last, the conclusions are drawn in Sect. 7.5.

7.2 7.2.1

The Basic Theory of Sparsity-Promoted SCA ICD Basic Process

ICD method is proposed as an adaptive signal processing method, decomposing the initial signal into several components. The detailed steps of ICD can be express as follows. Details are shown in Fig. 7.1. xð t Þ ¼

n X

PCi ðtÞ þ rn ðtÞ

ð7:1Þ

i¼1

reference points τ k +1 − τ k ( X − Xk ) τ k +2 −τ k k +2

ai ( t ) = ∏ aij ( t )

Ak +1 =X k +

I n pu t signal

local mean points local envelope points M k = ( X k + Ak ) / 2 Ek = X k − Ak / 2

Whether s(t) is an purely frequency Yes signal

x (t )

Mean curve m ( t ) Envelope curve a ( t )

Calculate h (t ) = x (t ) − m (t ) s (t ) = h (t ) / a (t )

Fig. 7.1 Steps of ICD method

PC1 ( t ) = a1 ( t ) s1n ( t )

x (t ) = s (t )

n

∑ PC ( t ) i

Yes

i =1

+

rn ( t )

r1 ( t ) = x ( t ) − PC1 ( t )

No

x (t ) =

Is r(t) constant or monotonic?

No x ( t ) = ri ( t )

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Majorization-Minimization Algorithm

The sparse representation feature extraction method can be expressed as follows: 

1 ^a ¼ arg min F ðaÞ ¼ ky  Dak22 þ kkak1 2 a

 ð7:2Þ

where F is the objective function, consisting of a data fidelity term ky  Dak22 and a regular term kak1 . Parameter k is a regularization parameter. The starting point of this idea is that Hk ða; ak Þ is simpler than directly minimizing F ðaÞ. The MM method obtains the corresponding sequence value ak by minimizing Hk ða; ak Þ [33]. Here Hk ða; ak Þ should satisfy the properties as follows: Hk ða; ak Þ  F ðaÞ 8a 2 RN Hk ða; ak Þ ¼ F ðak Þ

ð7:3Þ

Figure 7.2 shows the MM execution process for minimizing univariate functions. At the first iteration, the initial value ak þ 1 of the next iteration is obtained by minimizing Hk ða; ak Þ, that is, Hk þ 1 ða; ak þ 1 Þ is equal to the value of the objective function F at ak þ 1 . Through a similar repeated iteration, it finally converges to the minimum value of the objective function F.

Hk(α)

Hk(α , α k) , F(α )

Fig. 7.2 Simple illustration of MM execution steps

Hk+1(α)

0

α k+1 α k

F (α)

α

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The Framework of Sparsity-Promoted SCA

In summary, the basic principles of each step in the framework proposed in this paper are given in the previous section. In order to solve the compound failure problem of rotating machinery, this section presents the detailed framework content and it can be divided into two steps. In the first step, ICD is utilized to decompose a single channel vibration signal into multi-channel signals. In the second step, MM is used to promote the signal sparsity so that the sparse signal can satisfy the requirement for the subsequent process. The proper function is constructed and is used to perform the majorization iterative procedures. The sparse signal can be obtained based on the approach. In the last step, the compound faults signal will be separated according to the sparse signal. Finally, envelope analysis for separation signal is taken advantage of extracting the fault feature. The detailed procedures are presented in Fig. 7.3.

Rotation machine

Data acquisition 5

0

-5

0

0.2

0.4

0.6

0.8

1

Intrinsic characteristic-scale decomposition Input signal

Demodulation operation

x (t )

Generate PC

Whether

Output PCs

Whether Yes

Yes No

x (t ) = s (t )

No

x ( t ) = ri ( t )

Sparse signals

Majorization iterative

Hk(α , α k) , F(α )

Hk(α ,α k)

Separated sources

0.5

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20

40

60

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100

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60

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s1

F (αk)

0

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-0.5

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40 s2

0.5 0

0

α k+1 α k

-0.5

α

-1

Fig. 7.3 The detail steps of proposed method

-0.5

0

0.5

1

0

20

40 s3 f/Hz

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Simulated and Experimental Verification Simulation Verification

Three signals are simulated as the experimental subject based on Eq. (7.4), and their characteristic frequencies are 22 Hz, 54 Hz and 73 Hz, respectively. The source signal spectra are presented in Fig. 7.4. To construct an BSS environment, a random matrix is used to mix the source signals. Figure 7.5 shows that the clustering feature with noise and the clustering feature with noise based on MM. The sparsity-promoted cluster effect can be seen in Fig. 7.5(b). The result presented in Fig. 7.6 indicates that the three original source signals are separated and prove the feasibility of the suggested method. sðtÞ ¼ A0 e

pfn fðtksÞ

qffiffiffiffiffiffiffiffiffiffiffiffiffi sin[2pfn  1  f2  ðt  ksÞ þ nðtÞ

Fig. 7.4 Spectrum of source signal: a spectrum of s1 ; b spectrum of s2 ; c spectrum of s3

ð7:4Þ

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Fig. 7.5 Scatter diagrams of mixed signals: a scatter diagram with noise; b scatter diagram with MM

Fig. 7.6 Envelope spectra of separated sources: a envelope spectrum of s2 ; b envelope spectrum of s3 ; c envelope spectrum of s1

7.4.2

Experimental Verification

A bearing test bench shown in Fig. 7.7 was used to verify the feasibility of the proposed method. The test bench consists of a rotating machine, a rolling bearing and acceleration sensors. Accelerometer sensors mounted on the bearing housing are used to collect the vibration signal. Furthermore, the sampling time is 10 s and the vibration fault signals with 900 rpm are used to prove the feasibility of the suggested method. The bearing used in experiment is NTN 204 bearing. The compound faults signal and its spectrum are shown in Fig. 7.8. It can be seen that only 60.27 Hz is apparent, meaning that the outer race has failure.

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Fig. 7.7 The experimental system

Fig. 7.8 The vibration signal of compound fault. a The waveform of signal; b The spectrum of signal

Fig. 7.9 The separated outer race signal based on the proposed method. a The waveform of the outer race signal; b The spectrum of the outer race signal

To separate the compound fault, the proposed method is utilized to perform. First, ICD is used to decompose the single channel into multiple channel. Then, MM is utilized to obtain the sparse signal to ensure that the algorithm works properly. Separated results are displayed in Fig. 7.9 and Fig. 7.10. 74 Hz shown in Fig. 7.10 is closed to the theoretical value of roller fault. The results indicate that the compound faults are separated based on the proposed method.

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Fig. 7.10 The separated roller signal based on the proposed method. a The waveform of the roller signal; b The spectrum of the roller signal

Fig. 7.11 The separated signal based on the EMD-ICA method. a The waveform of the outer race signal; b The spectrum of the outer race signal; c The waveform of the roller signal; d The spectrum of the roller signal

7.4.3

Comparative Reference

The EMD-based ICA method is utilized as a contrast to verify the effectiveness of suggested method. It can be seen from Fig. 7.11 that the outer race component is still mixed in the roller signal, indicating that the separation is not complete.

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Conclusion

In this paper, multi fault signals are separated based on SCA framework that combines ICD with SCA. ICD is utilized to decompose the channel into multiple ones, which overcome the insufficient number of observations. The PCs are then used as the input matrix of the MM algorithm to obtain proper sparsity for signal. Finally, the separations of the multiple faults are obtained through SCA. Simulated and experimental results indicate the effectiveness of the suggested algorithm. Additionally, the proposed method can successfully separate compound fault signals which is compared with the traditional ICA method such as EMD-based on ICA method. Acknowledgements This work is supported by the National Natural Science Foundation of China (Grant No. 51675035 and No. 51805022).

References 1. Zhou, H., Chen, J., Dong, G., Wang, H., Yuan, H.: Bearing fault recognition method based on neighborhood component analysis and coupled hidden Markov model. Mech. Syst. Signal Process. 66–67, 568–581 (2016) 2. Zhong, J., Wong, P., Yang, Z.: Fault diagnosis of rotating machinery based on multiple probabilistic classifiers. Mech. Syst. Signal Process. 108, 99–114 (2018) 3. Yu, K., Kai, Y., Bai, Y.: Estimation of modal parameters using the sparse component analysis based underdetermined blind source separation. Mech. Syst. Signal Process. 45, 302–316 (2014) 4. Wang, H., Li, R., Tang, G., Yuan, H., Zhao, Q., Cao, X.: A compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition. PLoS ONE 9(10), 1–13 (2014) 5. Tang, G., Luo, G., Zhang, W., Yang, C., Wang, H.: Underdetermined blind source separation with variational mode decomposition for compound roller bearing fault signals. Sensors 16, 897 (2016) 6. Sun, J., Li, Y., Wen, J., Yan, S.: Novel mixing matrix estimation approach in underdetermined blind source separation. Neurocomputing 173, 623–632 (2016) 7. Hao, Y., Song, L., Cui, L., Wang, H.: A three-dimensional geometric features-based SCA algorithm for compound faults diagnosis. Measurement 134, 480–491 (2019) 8. Li, Y., Liang, X., Xu, M., Huang, W.: Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform. Mech. Syst. Signal Process. 86, 204–223 (2017) 9. Ren, B., Hao, Y., Wang, H., Song, L., Tang, G., Yuan, H.: A sparsity-promoted method based on majorization-minimization for weak fault feature enhancement. Sensors 18, 1003 (2018)

Chapter 8

Detection Method of Contact-Type Failure Based on Nonlinear Wave Modulation Utilizing Ultrasonic Vibration Driven by Self-excitation Takashi Tanaka , Yasunori Oura, and Syuya Maeda

Abstract This paper introduces the concept of new detection method of contact-type failure based on nonlinear wave modulation utilizing ultrasonic vibration driven by self-excitation. It is difficult to detect the contact-type failure in standard inspection utilizing linear ultrasonic vibration. The constructional element of infrastructure is excited by environmental disturbance or forced excitation. In this situation, the contact condition of the failure fluctuates in synchronization with vibration. As this result, local stiffness in the vicinity of failure location fluctuates at the vibration frequency. Thereby, the amplitude and phase of ultrasonic vibration are modulated caused by local stiffness fluctuation (nonlinear wave modulation). This phenomenon can be expressed by a linear time-varying system caused by fluctuation of the natural frequency. In this paper, the new detection method of contact-type failure utilizing self-excited ultrasonic vibration is introduced. Firstly, the concept of detection method of contact-type damage based on nonlinear wave modulation is proposed. It is indicated from time history response analysis using single degree-of-freedom model of nonlinear wave modulation that the fluctuation amplitude of the amplitude and phase of ultrasonic as failure index varies depending on viscous damping. Secondly, the self-excited method utilizing local feedback control and characteristics are introduced. This method realizes oscillator which start to oscillate at the natural frequency automatically. Thus, the frequency of oscillation signal controlled by local feedback control is fluctuated in synchronization with fluctuation of natural frequency when nonlinear wave modulation occurred. Lastly, it is proved that the fluctuation amplitude of frequency of oscillation signal is the failure index independent of viscous damping.

T. Tanaka (&)
Y. Oura University of Shiga Prefecture, 2500 Hassaka, Hikone, Shiga, Japan e-mail: [email protected] S. Maeda Graduate School of Engineering, The University of Shiga Prefecture, 2500 Hassaka, Hikone, Shiga, Japan © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_8

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Recently, the deterioration of the infrastructure is one of the serious problems for various countries. To detect faults and maintain it at an early stage is important to extend the life span of the structure. Therefore, the concept of structural health monitoring that is the continuous monitoring for the structural failure using sensors embedded in the structure is emerged to reduce the maintenance cost and time in the 1990s [1, 2]. In this concept, detection method of minor failure is one of the important techniques. Even among them, detection methods based on measuring linear ultrasonic vibrations or waves can detect minor failure. On the other hand, these methods cannot detect the contact-type failure, e.g. early stage of fatigue crack, delamination of composite material and adhesive fatigue. Inspection method based on nonlinear wave modulation is proposed for detection of contact-type failure [3, 4]. When the constructional element of infrastructure is excited by environmental disturbance, e.g. vibration when vehicles pass infrastructure, vibrations due to wind or forced excitation, the pair of contact surfaces are breathing due to vibration. In this condition, the scatter characteristics of ultrasonic vibration are fluctuated in synchronization with vibration of infrastructure. Therefore, the amplitude and phase of ultrasonic vibration are fluctuated. This fluctuation amplitude of amplitude and phase is related to the fluctuation of local stiffness caused by contact condition of failure. On the other hand, this detection method has two problems. As one of them, to choose natural frequency to the ultrasonic frequency is necessary to detect failure high sensitivity. As further of them, the evaluation index of failure level presented by previous studies depends on the viscous damping. So, the measurement of natural frequencies and viscous damping before every inspection timing is required. To resolve the first problem, Masuda et al. presented excitation method of ultrasonic vibration utilizing feedback control using piezo-electric elements which are attached at two places [5]. This research was made in reference to self-excitation method utilizing local feedback control to excite all natural vibration [6, 7] which was suggested by the authors of this paper. In this method, structure is driven by self-excitation as natural vibration and changes excitation frequency following change of natural vibration frequency. From these paper, we got the idea of a new failure detection method using frequency following of ultrasonic vibration driven by self-excitation. In this research, nonlinear wave modulation is modelled as a time-varying linear system. In this modeling, nonlinear wave modulation can explain as linear system that the natural frequencies are fluctuated in synchronization with vibration. The authors considered that frequency fluctuation is occurred in nonlinear wave modulation utilizing ultrasonic vibration driven by self-excitation. It is assumed that the amplitude of frequency fluctuation of excitation frequency is independent of the viscous damping.

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Nonlinear Wave Modulation Utilizing Ultrasonic Vibration Driven by Self-excitation

In this section, phenomenon called “nonlinear wave modulation” and previous detection method utilizing ultrasonic vibration driven by forced excitation are introduced. Firstly, overview of nonlinear wave modulation is shown to explain the relationship between contact-type failure and phenomenon. Secondary, single degree-of-freedom (SDOF) model of nonlinear wave modulation is shown and its dimensionless equation of motion is shown is derived. Lastly, the result of the time history analysis of nonlinear wave modulation is shown and the influence of viscous damping is explained.

8.2.1

Overview of Nonlinear Wave Modulation

Firstly, overview of nonlinear wave modulation and detection method utilizing ultrasonic vibration is expressed. Figure 8.1 shows a conceptual illustration of nonlinear wave modulation. The structure which has a contact-type failure is vibrating in vibration which frequency is sufficiently lower than the frequency of ultrasonic vibration. In this condition, contact surfaces are tapping and clapping of low-frequency vibration and the characteristics of ultrasonic vibration are fluctuated in synchronization with low-frequency vibration. Herewith, the amplitude and phase of ultrasonic vibration of measuring signal are fluctuated. When natural vibration is chosen as ultrasonic vibration, nonlinear wave modulation can be attributed to time-varying linear system. Figure 8.2(a) shows transfer function of ultrasonic vibration when viscous damping is small. Figure 8.2(b)

Fig. 8.1 Conceptual illustration of nonlinear wave modulation [8]

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shows the transfer function when viscous damping is large. In this model, it is considered that the contact stiffness built on Hertz’s contact theory exists between contact surfaces of failure. In Fig. 8.2(a), contact stiffness is reduced when contact-type failure receives tensile load. Then, natural frequency of ultrasonic vibration reduces (Green line of Fig. 8.2). On the other hand, contact stiffness increases when contact-type failure receives compressive load. Then, natural frequency xp of ultrasonic vibration increases (red line of Fig. 8.2). In this result, the amplitude and phase of ultrasonic vibration driven by forced excitation at a natural frequency in static condition are fluctuated. In addition, the peak of the gain is decreased and the change of phase is gentle. In this result, the fluctuation of the amplitude and phase of ultrasonic vibration. So, the failure evaluation index using the fluctuations of the amplitude and phase is dependent on viscous damping.

Fig. 8.2 Transfer function of time-varying linear model of nonlinear wave modulation

(a) In the case of small damping

(b) In the case of large damping

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Self-excitation Method by Local Feedback Control

The authors developed the self-excitation method at resonance point using the transfer characteristics of the structure [7]. In this subsection, the overview of self-excitation method controlled by local feedback control is introduced. Firstly, the transfer characteristics of the structure are explained. Figure 8.3(a) shows the 3 degrees of freedom system and its vibration characteristics. u is force and h is displacement. When the actuator and sensor are the same location (collocation), the resonance point and the anti-resonance point alternately appear. Thus, the phase of all natural frequencies in the resonance point is always 90° delays. The local feedback control can be generated self-excited vibration. The mean of “local” is that the location of the sensor and the actuator for feedback control are collocation. If the controller becomes delay, open loop transfer function is at all natural frequencies. In this condition, the self-excited vibration generates at a natural frequency when the loop gain is larger than 0 dB. We call self-excitation that the structure is excited by actuator force using the self-excited vibration. This self-excitation has auto-oscillation at natural frequencies and traceability for change of a natural frequency. Firstly, this excitation method is oscillated at only

Fig. 8.3 Transfer characteristics of linear and 3 degree of freedom system [7]

(a) Linear and 3 degree of freedom system

(b) Transfer characteristics of 3 degree of freedom system

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natural frequencies. So, it does not need to investigate the natural frequencies to determine the excitation frequency before the inspection. Therefore, excitation frequency changes in synchronization with change of natural frequency.

8.2.3

Dimensionless Equation of Motion of Nonlinear Wave Modulation Utilizing Ultrasonic Vibration Driven by Self-excitation

In this subsection, the SDOF model of nonlinear wave modulation and dimensionless equation for time response analysis are produced. At first, SDOF model which fluctuation of contact condition of failure is expressed in fluctuation of spring coefficient is introduced in Fig. 8.4. This model is focused on single natural vibration mode of ultrasonic vibration. The dimensionless equation of motion of nonlinear wave modulation utilizing ultrasonic vibration driven by self-excitation. The differential equation of motion for this SDOF model is described by: m€xðtÞ þ c_xðtÞ þ kðtÞxðtÞ ¼ K x_ ðtÞ

ð8:1Þ

where t is the time; xðtÞ is the deflection of mass point; m is the modal mass of ultrasonic vibration; kðtÞ is the spring coefficient as modal stiffness including fluctuation caused by vibration; c is the damping coefficient of ultrasonic vibration; K is feedback gain. Moreover, k ðtÞ is expressed by: kðtÞ ¼ k0 þ k0 sin Xt

ð8:2Þ

where k0 is the stiffness when the structure is healthy; k0 is the amplitude of the stiffness fluctuation caused by vibration; X is the frequency of vibration. We transform the variables t, xðtÞ to t , x ðt Þ respectively shown in Eq. (8.3). t ¼ Tt ; xðtÞ ¼ Xx ðt Þ

Fig. 8.4 Single degree-of-freedom model of nonlinear wave modulation

ð8:3Þ

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pffiffiffiffiffiffiffiffiffiffi  where T ¼ m=k0 ¼ 1 xp is the representative time and X is the representative length. By this transformation, the dimensionless equation of motion is obtained: d 2 x ð t  Þ dx ðt Þ dx ðt Þ þ 2f þ x ðt Þ þ a sinðX t Þx ðt Þ ¼ G ð8:4Þ 2  dt dt dt pffiffiffiffiffiffiffiffi where f ¼ c mk0 is the damping ratio; a ¼ k0 =k0 is the stiffness ratio;  X ¼ X xp is the frequency ratio of the frequency of the stiffness fluctuation and the natural circle frequency of structure at ultrasonic frequency bandwidths; pffiffiffiffiffiffiffiffi G ¼ 2K mk0 is the dimensionless feedback gain.

8.3

Detection Method of Contact-Type Failure Using Fluctuation of Excitation Frequency

In this section, the model, the condition and the result of time history analysis are introduced. From result, the relationship of fluctuation amplitude of excitation frequency of ultrasonic vibration as an evaluation index and viscous damping is explained.

8.3.1

Simulation Model and Condition of Time Response Analysis

The time response analysis is done by numerical simulation. We use Matlab/ Simulink for numerical simulation. At first, analysis model referring to the dimensionless equation of motion is shown in Fig. 8.5. In Fig. 8.5, the saturation element is existed to prevent the explosive of controlling force. In addition, fixed step ordinary differential equation solver Ode4 based on a Runge-Kutta formula was applied. The simulation time is 10000 s. The time resolution is 10−4 s. The

Fig. 8.5 Block diagram of nonlinear wave modulation

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time part when the vibration system became the steady state of vibration is shown as a result. Firstly, the simulation result when X ¼ 0:001, f ¼ 0:01, a ¼ 0:1 is shown in Fig. 8.6 as example. Figure 8.6(a) is time response waveform of x ðt Þ. The amplitude seems to be modulating. This amplitude fluctuation occurred because the synchronous tracking performance of self-excited vibration. So, this modulation is not the essence of nonlinear wave modulation. Figure 8.6(b) shows frequency demodulation wave form of x ðt Þ. Short time Fourier transformation is used for the demodulation process. Period of frequency demodulation waveform is that is period of stiffness fluctuation. This modulation can be explained by transfer function shown in Fig. 8.2. The natural frequency is decrease when contact-type failure receives tensile load and increase when contact-type failure receives compressive load. Thus, frequency of frequency fluctuation is coincident with the frequency of stiffness fluctuation.

8.3.2

Damping Independency of Exciting Frequency

In addition, the influence of viscous damping in the detection method using forced excitation is investigated. The simulation conditions are written. The frequency difference of ultrasonic vibration and low-frequency vibration X ¼ 0:001 was fixed. The stiffness ratio a was changed from 0.01 to 0.1 and damping ratio f was changed 0.01, 0.05 and 0.1. The controlling force after saturation element as an evaluation index of failure level is shown in Fig. 8.7. Firstly, the linearity between the stiffness ratio to express failure level and the amplitude of frequency fluctuation

(a) Wave form of time response

(b) Wave form of frequency demodulation

Fig. 8.6 Result of time response analysis of nonlinear wave modulation utilizing self-excited ultrasonic vibration

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is confirmed from Fig. 8.7. The amplitude of frequency fluctuation can be the evaluation index of contact-type failure level. Thus, dependency of the amplitude of frequency fluctuation is investigated. The inclination of the linearity is almost the same at the all result shown in Fig. 8.7. Slight difference of inclination is caused by the saturation element. From these results, the damping independency of the novel evaluation index is confirmed by time response analysis of nonlinear wave modulation.

(a)

=0.01

(b)

(c)

=0.1

Fig. 8.7 The damping independency of estimation of failure level

=0.05

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Conclusion

In this paper, the concept of novel detection method of contact-type failure based on nonlinear wave modulation utilizing ultrasonic vibration driven by self-excitation is presented. Firstly, the linear time-varying model of nonlinear wave modulation is introduced. Secondary, the dimensionless equation of introduced model is derived. Finally, the time response analysis of derived equation is done. From this simulation, three knowledges are gotten. (1) Self-excitation using local feedback control can be excited the natural vibration automatically. (2) The excitation frequency is fluctuation in synchronization with the frequency of stiffness fluctuation. The fluctuation of excitation frequency can be detected by short time Fourier transformation. From this result, the nonlinear wave modulation can expresses by fluctuation of natural frequency when the ratio of vibration and stiffness fluctuation is large enough. (3) The amplitude of frequency fluctuation as the novel index of failure level is proposed. This index is independent of the viscous damping. Acknowledgements This research was supported by JSPS KAKENHI Grant Number 18K13716. Additional support was provided by a travel grant from Kansai Research Foundation for technology promotion.

References 1. Maaskant, R., Alavie, T., Measures, R.M., Tadros, G., Rizkalla, S.H., Guha-Thakurta, A.: Fiber-optic Bragg grating sensors for bridge monitoring. Cem. Concr. Compos. 19(1), 21–33 (1997) 2. Giurgiutiu, V., Redmond, J.M., Roarch, D.P., Rachow, K.: Active sensors for health monitoring of aging aerospace structure. In: SPIE’s 7th International Symposium on Smart Structures and Materials and 5th International Symposium on Nondestructive Evaluation and Health Monitoring of Aging Infrastructure, no. 294. SPIE, Newport Beach (2000) 3. Masuda, A., Aoki, J., Shinagawa, T., Iba, D., Sone, A.: Nonlinear piezoelectric impedance modulation induced by a contact-type failure and its application in crack monitoring. Smart Mater. Struct. 20(2), 025021, 1–11 (2011) 4. Abeele, K.E.A.V.D., Johnson, P., Sutin, A.: Nonlinear elastic wave spectroscopy (news) techniques to discern material damage, part I: nonlinear wave modulation spectroscopy (NWMS). Res. Nondestr. Eval. 12, 17–30 (2000) 5. Masuda, A., Ogawa, Y., Sone, A.: Detection of contact-type damages by utilizing nonlinear piezoelectric impedance modulation of self-excited structures. In: ASME Dynamic Systems and Control Conference 2013, no. DSCC2013-4058. ASME, Buffalo (2013) 6. Kurita, Y., Oura, Y., Matsuda, S., Nishide, H.: Driving at resonance point of multi-degree-of-freedom system by decentralized control (development of control method and verification of basic performance). J. Syst. Des. Dyn. 5(1), 180–191 (2011)

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7. Tanaka, T., Nakamura, H., Oura, Y., Kurita, Y.: Measurement of natural vibration of acoustic space by multi-point excitation using decentralized control. In: 25th International Congress on Sound and Vibration, no. T01-RS1-174. IIAV, Hiroshima (2018) 8. Tanaka, T., Masuda, A., Sone, A.: Localization of contact-type failure in beam structure based on reflectivity modulation. In: 12th International Conference on Motion and Vibration Control, no. 2B22. JSME, Sapporo (2014)

Chapter 9

Online Condition Monitoring of Engines by a Deep Analysis of the Electrical Conductivity and Relative Permittivity Changes of the Lubricant Manfred R. Mauntz and Jörn Peuser

Abstract The requirements of renewable energy for large industrial gearboxes as installed in wind turbines on and off shore rise. The same applies for efficient gas and diesel engines. A larger flexibility is required of these devices such as maximum operational reliability and a long lifetime. Thus, the requirements for oil and oil condition monitoring grow correspondingly. This presentation provides information about a novel online oil condition monitoring system that gives a solution to the mentioned priorities in the energy sector. The different mechanisms of oil parameter variation in gearboxes and engines are addressed; the data interpretation has to be redefined to the dominating effect. From this, the very sensitive measurement of conductivity j, relative permittivity er and temperature, T, enables the detection of small changes in the conductivity and dielectric constant of the corresponding oil composition. Therefore, the sensor system effectively controls the proper operation conditions of engines and gearboxes. 24/7 monitoring of the asset during operation enables specific preventive and condition-based maintenance, which is independent of rigid inspection intervals.

9.1

Introduction

The standard procedure in gas and diesel engines is to exchange the oil depending on a clear schedule based on operating hours of the corresponding application. During this time, weekly or monthly oil samples are taken manually with all the correlated problems of the extraction point, environmental conditions, the transportation time, the laboratory personal. It always takes a certain time to get a

M. R. Mauntz (&)
J. Peuser cmc Instruments GmbH, Eschborn, Germany e-mail: [email protected] J. Peuser e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_9

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feedback to the operators of the engine. In case of onsite or laboratory spot measurements, it is not possible to distinguish between the two possible scenarios: Scenario 1: Normal oil aging Scenario 2: Critical changes in oil composition The offline/onsite measurements can’t differ between the both scenarios as this is depicted in the Fig. 9.1. Only an online system, which monitors the trend of one or several parameters, can help at the identification of normal or abnormal behavior. Figure 9.2 shows the compact base sensor. The values electrical conductivity j and relative permittivity er are determined independently. Inorganic compounds occur at contact surfaces from the wear of parts, broken oil molecules, acids or oil soaps. These all lead to a change in the electrical conductivity, which correlates directly with the wear. In oils containing additives, changes in dielectric constant infer the chemical breakdown of additives. The determination of impurities, a reduction in the lubricating ability of the oils, the continuous evaluation of the wear of bearings and gears and the oil aging all together follow the holistic approach of real-time monitoring of changes in the oil-machine system. Fig. 9.1 Three spot measurements by laboratory analysis or onsite analysis do not reveal the different scenarios

Fig. 9.2 Compact base sensor

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Introduction

With the WearSens® WS 3000 sensor system, components of the complex impedances X of oils, in particular the specific electrical conductivity j and the relative permittivity er as well as the oil temperature T are measured [1–3]. Both values j and er are determined independently of each other. Oils are in general electrical non-conductors. The electrical residual conductivity of pure oils is in the range below 1 pS/m as they are used in HV transformers. Figure 9.3 illustrates conductivities of various materials. The conductivity range of the WearSens® sensor system is marked in green. It starts below the conductivity of the distilled water. For comparison, the electrical conductivity of the electrical non-conductor distilled water is larger by six orders of magnitude. Increasing ion concentration and mobility results in an increase in the electrical conductivity. The electrical conductivity of almost all impurities and active additive components is high compared with the extremely low corresponding property of original pure oils. A direct connection between the electrical conductivity and the degree of contamination of oils is found. An increasing trend of the electrical conductivity of the oil in operation can thus be interpreted as increasing wear (on a short time scale) or contamination of the lubricant (instantaneous or slow proceeding contamination). The aging of the oil is also evident in the degradation of additives.

Fig. 9.3 Conductivities of liquids and solids, measurement range of the presented sensor system marked in green

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Ion mobility and thus, electrical conductivity are dependent on the internal friction of the oil and therefore, also on its temperature. The conductivity j of the oil increases with temperature. The type of contamination and its temperature dependence cannot be assumed to be known. To improve the comparability of measurements, a self-learning adaptive temperature compensation algorithm is necessary. A change of the oil quality can then be assessed by the temperature compensated conductivity value, even though the specific contamination is not determinable. The relative permittivity is measured with the same basic sensor arrangement as used for the determination of the electrical conductivity.

9.3

Parameter Change on Different Events

During the oil operation time the oil composition will continuously change depending on the dominating effect: additive consumption, contamination or aging processes. Figure 9.4 below illustrates the effect on the different branches on the electrical conductivity and relative permittivity at a constant oil temperature. The parameter space of the fresh oil (shown as a purple disc) is moving on the different branches over time. Whereas additive consumption, oil aging and contamination are on the mid to long term time scale, the critical lubrication conditions—occurring only in operation—are in the seconds to minute regime; when the critical event is over the parameters are going back to the actual initial oil condition. Ion Additive Consumption The additive consumption in lubrication oil has a higher impact on both parameters compared to insulation oils; this mainly depends on the additive composition of the particular oil and its higher contribution to the initial conductivity and permittivity level.

Fig. 9.4 Conductivity and relative permittivity variation for different kind of events

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Contamination, Acidification, Oil Aging The contamination of engine oils with the wrong/incompatible oil type, impurities, fuel will result in a permanent increase of both parameters. An increased acidity level will also rise er and j. The oil oxidation/oil aging is increasing the electrical conductivity. Water Contamination (After Additives Can’t Handle the Amount of Incoming Water) First of all, incoming water (er = 80) will react with the corresponding additives, which take care of emulsification of water, so the conglomeration of water droplets is minimized and a blank metal surface without lubrication film/tribological layer protection is prevented. er is decreasing a bit. After the additives for water emulsification are depleted, er will start to increase again, due to the higher value er of water, compared to oil (er between 2 to 3). Overload Condition (Conductivity Peak, only on a Short Time Scale) The base conductivity level will rise for a short time during the load/overload event due to the increased charge carrier generation of the system oil-machine.

9.4

Application Example: Gas Engine

The focus of this section is a real life application with field data, showing the impact on continuous process related contamination of a gas engine burning landfill gases with varying contamination content. The installation is show in the picture 5 below. The sensor system has been installed in a small bypass line at a caterpillar gas engine (Fig. 9.5).

Fig. 9.5 Installation of the WearSens sensor system at a Caterpillar gas engine for the energy production by burning landfill gases

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The recorded data is visualized in the Figs. 9.6 and 9.7. The graph in the upper picture 6 shows the measured electrical conductivity j over time and the graph in the lower picture 7 depicts the relative permittivity er over time. The data shows for both parameters an increasing trend due to the ongoing slow but continuous contamination coming from the landfill gases. Usually, the oil change interval in this application is about 28 days on a fixed time schedule. Together with the installed oil sensor system the total oil operation was increased by a factor of 1.6 using the online monitoring. The offline oil laboratory report has shown a healthy oil condition at day 45, a safety buffer was implemented. The online diagnostics system measures components of the specific complex impedance of oils. The indication of forming stage of damage and wear is measured as an integral factor of, e.g., the degree of pollution, oil aging and acidification, water content and the decomposition state of additives, which is correlated to the changes in the electrical conductivity and relative permittivity. By the adaptive temperature compensation of the measured values, it is possible to identify even small variation in the actual charge carrier generation and additive consumption. For an efficient machine utilization and targeted damage prevention, the WearSens® online condition monitoring system offers the prospect to carry out timely preventative maintenance on demand rather than in rigid inspection intervals. The benefits of an extended oil change interval are reduced costs, preservation of the environment and resource protection. The data from the sensor system installation at a Caterpillar gas engine showed the significant contamination process. The WearSens® online oil sensor system enables condition based oil samples on demand instead of rigid oil sampling for the offline laboratory reports, which might be too late for same cases.

Fig. 9.6 Increase in the electrical conductivity over time due the incoming contamination

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Fig. 9.7 Increase of the relative permittivity over time due to increasing contamination elements

References 1. Gegner, J., Kuipers, U., Mauntz, M.: Ölsensorsystem zur Echtzeitzustands-überwachung von technischen Anlagen und Maschinen. Tech. Mess. 77, 283–292 (2010) 2. Mauntz, M., Gegner, J., Klingauf, S., Kuipers, U.: Continuous wear measurement in tribological systems to control operational wear damage with a new online oil sensor system. In: TAE Technische Akademie Esslingen, 19th International Colloquium Tribology, Esslingen, 21–23 January 2014 (2014) 3. Mauntz, M., Kuipers, U., Peuser, J.: Continuous, online detection of critical operation conditions and wear damage with a new oil condition monitoring system. In: WearSens®, 14th International Conference on Tribology—SERIATRIB 2015 Proceedings, Belgrad, Serbian Tribology Society Kragujevac, University of Belgrade, Faculty of Mechanical Engineering, Belgrade, pp. 283–288 (2015). (ISBN 978-86-7083-857-4) 4. Mauntz, M., Kuipers, U., Peuser, J.: New oil condition monitoring system, WearSens® enables continuous, online detection of critical operating conditions and wear damage. In: Malaysian International Tribology Conference 2015—MITC2015, Penang, Malaysia on 16–17 November 2015, Conference Proceedings, pp. 179–180 (2015). (ISBN 978-967-13625-0-1)

Chapter 10

Are We Ready for Industry 4.0? Abdu Shaalan , David Baglee , and Michael Knowles

Abstract A significant number of manufacturing organisations are showing interest in Industry 4.0 due to the support it can provide for processing and visualising manufacturing data in real-time. Industry 4.0 techniques can be used to provide an assessment of machine condition by detecting and processing internal and external data of critical machine components. Currently, a few Small and Medium Enterprises (SME’s) still use ageing and non-computer numerical control, manufacturing assets are operated and maintained without the use of digital technologies to monitor and report operating problems before they occur. Which in return, creates a significant barrier to the implementation of Industry 4.0 applications. In order to facilitate the implementation of Industry 4.0, on ageing, manual manufacturing assets, certain technologies associated with the third industrial revolution, including electronics and information technology, should be examined. This paper presents the implementation process of an automation system for monitoring and control of a hydraulic press by firstly examining the required electronics and information systems for processing data and secondly by defining the needed tools and techniques associated with Industry 4.0 applications and the related implementation barriers.

10.1

Introduction

Through the decades, the manufacturing industry placed a heavy focus on the continuous introduction of technological advancements in the sector, aiming to improve production quality, speed and have more reliable machines with high availability and efficiency indexes. Recently the term Industry 4.0 started to emerge widely in different industries, gaining significant attention and interest from organisations [1]. The term Industry 4.0 was introduced in 2011 by the German government, placing the foundation stone for the fourth industrial revolution. A. Shaalan (&)
D. Baglee
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Table 10.1 Industrial revolutions Revolution

First introduced

Main novelty

1st 2nd 3rd

1780 1870 1960

4th

2011

Steam engines and mechanical loom Electric motors and petroleum fuel Computerised systems and robots in the industrial production Computers, Internet and automation combined

To understand the reason behind marking the term Industry 4.0 as the fourth industrial revolution, the history behind it needs to be understood first. Initially, the first industrial revolution took place in England between the 1780s and the 1870s. The first revolution was based on the introduction of steam engines, and for the first time, a different power source than the manpower was introduced to the Industry. The Second industrial revolution took place from the 1870s to the 1960s in a broader range of countries including Britain, Germany and the United States. The second revolution involved a wide range of development and advances to Industry. Such advancements included the introduction of Electricity and Fossil fuels as different power sources and the introduction of mass production, which triggered the implementation of production lines and automatic operations by using electric circuits to increase productivity rates. Aiming for more advancements in machine control, the Third industrial revolution began in the 1960s and introduced the use of electronic circuits and computer programs for machine control, aiming to increase productivity and Machine efficiency alongside decreasing the workforce required for operation. The first three industrial revolutions had a significant effect on the manufacturing sector that always targeted increasing productivity with the highest

Chart 10.1 Industry 4.0 engines

Big Data Cyper security

CPS

IoT

Clouds

Additive manufacturing

Augmented Reality

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quality within controlled cost. The Fourth industrial revolution (Industry 4.0), introduces digitalisation into the manufacturing sector, Introducing technological advancements in connectivity and data processing techniques [1–3] (Table 10.1). Industry 4.0 presents areas of interaction with the manufacturing process from different focuses through the introduction of Cyber-Physical Systems (CPS), the Internet of Things (IoT), Big Data, Cybersecurity, Clouds, Additive manufacturing and Augmented Reality (AR) [4] (Chart 10.1). This new paradigm introduces a new manufacturing environment that includes high connectivity between systems and machine independency in decision-making processes. However, to facilitate the use of the new industrial advancements, significant changes in the manufacturing process and existing machinery must take place, which is associated with the high-cost impact that is not feasible for most manufacturers. Individual aspects of Industry 4.0 could be integrated with existing manufacturing equipment based on the areas of attention of different manufacturers. Internet of Things (IoT) provides a high level of connectivity and data exchange process between different machines in a local network level or over the Internet. (IoT) Is considered one of Industry 4.0’s main enablers for further implementation of different Industry 4.0 technologies. However, (IoT) applications require a minimum level of technology and connectivity that must exist at machines’ level prior its implementation [5]. Condition Based Maintenance (CBM), is an advanced maintenance optimisation strategy that uses different sensing tools. Such tools varies between temperature, vibration, pressure, humidity, speed, etc., based on the machine’s types to monitor the state of different parts of the system. Sensing tools for CBM developed over the years from using Hand-Held Devices (HHD) to apply checks on a scheduled basis, towards the use of continuous monitoring tool permanently attached to the equipment, continuously collecting data about the Machine’s state and condition. Such technology was introduced as part of the third Industrial revolution using sensing devices to continuously send data to the primary computing device, creating a smart system capable of processing data that monitors different parts of the equipment, analyse them, and take the proper actions that suits the manufacturing process [6, 7]. Given the significant interest from Small and Medium Enterprises (SME) in Industry 4.0 applications as a condition monitoring enabler, this paper will presents the implementation of an automation system for monitoring and control of a hydraulic press. The application is considered the initial integration phase to adapt the integration of Industry 4.0 technologies. Such work will be carried out by firstly, examining the required electronics and information systems for processing data, by defining the required tools and techniques associated with Industry 4.0 applications and the associated implementation barriers.

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10.2.1 Industry 4.0 A technological advancement introduced as a complex system aims for connectivity between different devices, collecting real-time data from sensory devices, using machine intelligence to create Machine’s decision-making applications based on the collected data analysis [8]. Elements that characterised the fourth industrial revolution were the integration of Information and Communication Technologies (ICT), Cyber-Physical Systems (CPS) and the Internet of Things (IoT) [9]. Continuous improvements in digitisation in the manufacturing process was the main contributor to Industry 4.0 existence [10]. Industry 4.0 aims to facilitate flexible, controlled manufacturing processes with real-time data collection and analysis aiming for improvements on the decision-making process on both strategic and operational levels [11]. Developments in ICT and the availability of affordable sensing tools facilitated their spread around the manufacturing sector, forming interconnected systems [12]. Such systems provide continuous monitoring and machine’s control alongside the production processes with continuous updated virtual models about the current status of the Machine and its internal parts [13]. Industry 4.0 was predominantly created by the CPS integration in the manufacturing process as well as the industrial processing using the IoT connectivity [14]. In order to establish efficient Industry 4.0 environment, Frank [15] presented six different principles highlighted as crucial enablers for Industry 4.0 as follows (Table 10.2);

Table 10.2 Industry 4.0 principles Principle

Description

Interoperability

System, people and information connected to allow information exchange between machines, processes, interfaces and people On time data exchange mechanism to enable immediate actions to take place High use of sensor allowing remote monitoring and tracking of all processes with machines allowed to send, receive commands and process information regarding their work cycle forming a decentralised work environment The use of service-oriented software architectures in alignment with the Internet of Things (IoT) Allows tasks changing for the machines in an easy and flexible way

Real-time operation capability Virtualisation Decentralisation

Service orientation Modularity

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10.2.2 Industry 4.0 Vision The technological aim of Industry 4.0 is to connect all existing machines in the manufacturing process together in a network of exchanging data, taking decisions and triggering actions independently without any human interaction through decentralised systems creating smarter production processes [8]. The industrial paradigm is described as a result of the improvement of the in digitisation and manufacturing automation linked with various communication methods developed [16]. According to Pereira [8] from different researchers’ and companies’ perspective, Industry 4.0 vision towards the manufacturing industry included Smart factories and Smart products. Smart, in terms of different researchers’ points of view, indicates machine’s independence and self-awareness of its own state with real-time communication between it’s different sensing and actuation components and other smart machines, deciding and acting independently based on the exchanged data [14]. Additionally, Smart factories are associated with the use of digitisation and the advancements in ICT to create smart machines aware of their own state (self-controlled) and other smart machines connected, forming a smart factory. Addressed as key technological enablers of Industry 4.0, CPS and IoT are considered base requirements for the adaptation and use of Industry 4.0 applications. Cyber Physical System (CPS). Cyber Physical Systems is an emerging term that is widely used as one of Industry 4.0’s key enablers that represents technological advancement in ICT. Based on the interaction between the physical and virtual worlds, CPSs provides machine’s self, controlling and monitoring functions, and coordinate the manufacturing process. CPS connects machine’s automations systems with programming and analytical software to continuously study the machine’s current status and control the operation based on the existing conditions. Monostori [17] described CPS as an embedded system that exchange data in a smart network. When the different CPS systems are connected in a higher-level network, it is frequently known as the Internet of Things (IoT). Different communication protocols connects different industrial devices together. OPC-UA, MODBUS, PROFIBUS and CANopen are examples of existing protocols. IoT enabler device, Industrial Gateway, is able to communicate through a wide range of the existing protocols, allowing it to process data from wide range of systems to form into one system [18]. Ferreira [19] presented CPS as a system that consists of the real time levels from the Industrial automation, Field, Direct control and supervisory control levels, with real time data processing software developing more machine’s intelligence and on time assessment of collected data by system. Internet of Things (IoT). A common term that highlights the connectivity between the different actual things and the internet. Continuous developments in computers’ interconnections became widely emerged, taking it to the next level of creating smart objects. CPS as an industry 4.0 engine, creates smart systems capable

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of monitoring and control of its process, taking it to the next level. IoT facilitates the connection between different CPSs in a smart network sharing information together. In addition, IoT adapts wide range of communication protocols that suits the nature of any CPS for data exchanging process [8]. Moreover, integrating all physical objects into the information network assist providing services to the smart objects connected to the internet for information exchanging process [20].

10.2.3 Industrial Automation Described as the third industrial revolution engine, that is identified as an arrangement of a set of technologies resulted in industrial machines’ monitoring and control with minimum human interaction with the process [20]. According to Omer [21], Industrial automation consists of five different levels which are; Field Level. Contains two major areas, sensing devices and actuators. Sensing devices carry out different monitoring applications of the system’s different parts using different sensing devices. Actuators, on the other hand, converts the controller’s signal into physical output. Direct Control Level. The sensory data and the actuation signals gets processed using generic software. The software process the logic operations by operating on industrial hardware like PLC and Industrial PC. Supervisory Control Level. Provides real-time monitoring and control of machines. Supervisory systems can provide variable Machine’s setpoints capability, which provides flexibility to the operating conditions. Production Control Level. Fully covers the manufacturing operations from production, quality, inventory management and maintenance perspective. Enterprise Control Level. Management focused operation for scheduling manufacturing operations. Field, Direct control and supervisory control are real-time machine operation levels. They provide instant feedback and decision-making process within machines. On the other hand, Production and Enterprise control levels are management applications which focus on data processing and analysis for process and services optimisation applications. However, they are not real-time applications like the first three levels. Advancement in Communication and services technologies resulted in a Service Oriented Architecture (SOA) paradigm. SOA applications link all the Automation systems levels together in a real-time process, providing interoperability and interaction among different industrial levels [22]. OPC-UA is an SOA for industrial automation that focuses on machine-to-machine communication, providing scalability, data automatic buffering, device discovery and security [23].

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The current study presents the implementation phase of Automation System on Field, Direct control and supervisory control levels on a hydraulic press machine exists at a Medium size company from the Aerospace Industry. The system aims to use Programmable Logic Controllers (PLC) and Human Machine Interface (HMI) integrating number of sensory devices aiming to implement the foundation for CPS with communications capabilities for data exchanging with supervisory systems or different machines.

10.2.4 Condition Based Maintenance (CBM) The development of maintenance strategies over the years has focused on studying a machine’s condition, history, its operating conditions, etc., all in pursuit of the optimum maintenance schedule for the Machine. Aiming to eliminate breakdowns, excessive maintenance applications and increase availability and efficiency within controlled cost. CBM methods and tools that have been developed had different Analytical and non-Analytical elements to identify and reach the desired output [24]. CBM on the other hand, presented a totally new perspective to develop the desired maintenance schedule for a given asset. CBM relies on modern technology in sensing and identifying the current condition of an asset. Modern technology provides different sensing tools including Vibration, Temperature, Pressure, Noise, etc. These tools suit different machines depending on their criteria and operating condition [7]. Condition monitoring applications in-case of using HHD takes place on a scheduled basis using special sensing tools or on a continuous basis in-case of using fixed sensory devices on the machines. In addition to CBM advantages, it helps reducing Machine’s breakdown rate and downtime using early warning system. CBM can provide a safety measure to warn the operators and stop the Machine when it reaches certain predefined levels of the existing sensory devices. which in return, helps the maintenance team to take the necessary precautions to avoid Machine’s breakdown. According to Shin [7], maintenance is associated with three main aspects that are considered the main pillars that define the necessary maintenance schedule; 1. Detection, Occurred failures identification. 2. Prognosis, failure prediction over a certain time. 3. Diagnostics, deteriorating components and/or their root cause identification.

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Currently Small and Medium Enterprises (SMEs) experiences the ownership of machines with different ages that were bought over the decades. Such machines have different control systems that vary between manually operated, semi-automated and fully automated systems, with various operation’s technologies. To establish a sufficient ground for Industry 4.0 applications to take place, a certain level of technologies inherited from the third industrial revolution, Automation systems, have to exist. Automation systems application is conducted on aged manufacturing asset, Hydraulic press, that lacks for any sort ICT technology for monitoring or control. The hydraulic press under study relies on technology from the second industrial revolution for its operation of using mechanical relays and electrical switches for operation. The applied Automation system is associated with the HMI screen for condition monitoring and control of the press. All the existing control devices that exists on the Machine whether they were buttons, Indicators or switches, are replaced with the suitable alternatives from Automation perspective. Mainly, the press’s monitoring and control applications are designed to take place using physical buttons and attached HMI screen (Fig. 10.1).

Fig. 10.1 Press machine

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10.3.1 Phase 1 Investigating Investigating the hydraulic press understudy in depth. initially, studying the Machine from Mechanical, Electrical, Hydraulic and Control perspective. Mechanical area included, inspecting the press chassis for any visible cracks or breaks, the ram state and motion with the associated parts, Electrical area included the existing motors, pumps, fans, sensors and switches for their integrity, their specifications and their connections’ requirements, Hydraulic area included hydraulic fluid, Directional Control Valves (DCV), fluid lines and the hydraulic operation sequence. Control area, focused on Automation system requirements from Actuators, sensory devices from control and monitoring perspective. Additionally, investigating the quality and integrity of all Machine’s parts is essential to ensure the Machine is within the operating condition to ensure the highest safety measures.

10.3.2 Phase 2 Guard System Design As safety is an essential consideration during the Automation system planning phase. Aiming for highest safety precautions, a pneumatic operated, PLC controlled guard door designed for the press, isolating the press machine from any human intervention during operation to ensure maximum safety and security of the press. The door pneumatic position detector fixed inside the pneumatic cylinder for continuous feedback about its locations whether it was open or closed.

10.3.3 Phase 3 Requirements Identifications Following studying the press in-depth, investigating all related aspects from an operational requirements point of view, suitable electrical components that facilitates the delivering the designated power for each component of the press were selected. From the Automation control point of view, Selected PLC and HMI screen for machines monitoring and control were selected supporting OPC-UA communication protocol for future integration with other software or systems.

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10.3.4 Phase 4 Monitoring Tools Focusing on having deep monitoring practices about the press’s different parts conditions for condition monitoring and early failure detection, existing sensory devices at the Machine were used alongside in addition of extra sensory devices for more insight about different parts of the Machine. For the press under study, Hydraulic’s fluid pressure and temperature sensors originally existed with fixed operation setpoints, without feedback mechanism. Further sensory devices including temperature and detection sensors were selected for addition on the main motor and around the Machine for monitoring the main motor’s temperature and the surrounding area around the Machine. In terms of Machine’s condition and operability; differences in pressure or temperature at different parts can work as an indicator of unusual behavior, which if spotted on early stages, failures could be prevented. Monitoring the guard door status is Included in the HMI screen program to increase security level through continuous feedback on its location.

10.3.5 Phase 5 Programming PLC and HMI programming were undertaken using Ladder Logic Diagram (LLD) language. System program is the system’s intelligence that facilitates Machine’s self-control and monitoring applications without human interactions. Programming phase creates the Machine’s operation sequence by controlling the existing actuators, motors in the current cases, based on different sensory data collected from different parts of the Machine. Virtual version of the press was created on the HMI screen, presenting the status of different parts of the press including, hydraulic fluid pressure and temperature, the status of different safety measures regarding including the guard system, the modification ability for the threshold of the different measures undertaken.

10.4

Results

Studying the Press machine in-depth resulted in an understanding of all the related aspects of the Machine and allowed examining the integrity of different parts individually. Recommendations for the full replacement of the hydraulic sealings, hydraulic lines and hydraulic fluid were presented to ensure high operation quality. The main barrier faced at the initial stage was the low background information and data about the press from a technical point of view. Machine’s documentation usually includes the different parts of the Machine and the required power loads for each motor, pump, valve, sensor, etc. Unfortunately, no documentation presented or existed for the press due to its old age. Investigation into all different components

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Fig. 10.2 Developed Control panel

and their specifications through the collection of the information plates pinned on each component individually to overcome such issue. Gathering all the required data about every part at the Machine allowed the choice of suitable power supply and connectivity requirements. The choice of certain models of PLC and HMI was carried out based on suitability to integrate all different parts of the Machine and connectivity capabilities to exchange data between different systems using advanced communication protocol including OPC-UA. Programming carried out using LLD language of programming as a simple graphical language chosen due to its ease to use to allow staff future engagement with the Machine in failure detection or development phases. Virtual image of the press was created on the HMI screen for different monitoring and control applications. Figure 10.2 presents the Electrical Control panel (ECP) developed for the press machine. Designed specifically based on the Machine’s components specifications, ECU connects all machine parts together, sensors and actuators, to monitor and control the Machine’s operation cycle. The ECP includes the PLC that controls all different parts via a series of connections. Figure 10.3 presents the replacement of the original control system for the Machine (Right), with the developed HMI screen that facilitates wide range on monitoring and control applications for the press with customisable operations’ conditions. The developed system presents continuous feedback regarding the status of the guarding system, the hydraulic system components including, valves

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Fig. 10.3 Integrated HMI screen (left) and old control system (right)

Fig. 10.4 Developed control system on HMI screen

actuation and status, hydraulic fluid pressure and temperature and hydraulic ram position. Alarming system monitors the status of all Machine’s parts included in the developed system. Two different modes of operations were included, testing mode and normal mode, testing mode is used for maintenance applications by sending feedback about all the different related parts individually using low power and speed. Normal mode uses the user-defined pressure and speed data. Figure 10.4 presents a different page of the program developed shows different indicators of the Machine’s parts. In addition, communication setup to Supervisory Control and Data Acquisition (SCADA) or IoT gateway through OPC-UA communication protocol was considered and included in the programming phase for following development phases of connecting the Machine to different systems or apply machine-to-machine connectivity for data exchange.

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The image above is the developed system replaced all the existing control and power control systems for the Machine. Figure 10.3 shows the old and initial control system that existed, and the new developed system using the HMI screen on the left. The developed system facilitated monitoring and control applications for the status of the different actuation positions, temperature, pressure, guard door status, variable pressure and temperature threshold setups for alarming and operation requirements included in the system. The use of HMI screen provides insight of the machine condition with continuous monitoring for the hydraulic pressure and temperature in the system, and ability to change operating conditions based on the required setups rather than one operation setup previously. The system designed and programmed to construct a fully automated system that performs the pressing task individually based on a sequence of checks to ensure suitable operation condition without human interaction. Pressure and temperature values are stored internally inside the system using storage space for further analysis.

10.4.1 Application Barriers Initially, data collection phase was extremely delicate process. That was due to the lack of documentation existence, which added further investigations and research about the different Machine’s part for proper allocation of the required electrical and control related parts for the Machine. Deep experience level from the engaged staff from different levels was highlighted as almost to non-experience about the machine or automation systems, which created many difficulties across the phases to direct the engaged staff to the right path. The company aimed to designate a low fund for purchasing of the press machine, which resulted in different areas needed refurbishing and examination for quality before carrying out the work. Suppliers issues in the delivery delays of the required parts to develop the system.

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Future Work

The developed system integrated the base requirement for the integration of Industry 4.0 applications. Data analysis programs and communications applications to different systems are now feasible to take place on the system.

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High interest in Industry 4.0 and its applications in machines’ real-time condition monitoring of their internal parts is widely established among SMEs from various manufacturing industries. However, to facilitate the applications of Industry 4.0, a certain level of technologies associated with the third industrial revolution needs to exist in the manufacturing assets. a significant number of aged, manually and semi-automated machines exists on various production sites and have high importance to the organisations. An application for the integration of Automation system in a Hydraulic press at Aerospace components company was conducted using different sensing tools, pressure, temperature, objects presence for continuous monitoring and control applications of the different parts of the Machine using PLC and HMI screen. The system can detect any anomalies in the status of the connected components form the normal conditions using the sensory devices and react by sending alarms and bring the Machine to a stop to prevent breakdowns. The developed system can connect to different data analysis programs for the formation of a full CPS system, and the communication to supervisory systems or different systems for machine-to-machine applications. The implementation of such application could have a positive financial impact only if the machine’s is within good operation conditions prior the upgrade. In addition, a sufficient background examination and identification of machine’s different parts’ specifications and the proper choice of suitable electronics is vital to ensure sufficient implementation. For the consideration of future Industry 4.0 applications, spacing for the associated electronics e.g. Industrial Gateway and Network router, connectivity mediums e.g. Internet cables, Wireless transmitter should be given attention in future planning.

References 1. Vaidya, S., Ambad, P., Bhosle, S.: Industry 4.0–a glimpse. Procedia Manuf. 20, 233–238 (2018) 2. Schuh, G., Potente, T., Wesch-Potente, C., Hauptvogel, A.: 10.6 sustainable increase in overhead productivity due to cyber-physical-systems (2013) 3. Weyer, S., Schmitt, M., Ohmer, M., Gorecky, D.: IFAC-PapersOnLine 48(3), 579–584 (2015) 4. Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent manufacturing in the context of Industry 4.0: a review. Eng. 3(5), 616–630 (2017) 5. Xia, F., Yang, L.T., Wang, L., Vinel, A.: Internet of things. Int. J. Commun. Syst. 25(9), 1101 (2012) 6. Ignat, S.: Power plants maintenance optimization based on CBM techniques. IFAC Proc. Vol. 46(6), 64–68 (2013) 7. Shin, J.H., Jun, H.B.: On condition based maintenance policy. J. Comput. Des. Eng. 2(2), 119–127 (2015) 8. Pereira, A., Romero, F.: A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manuf. 13, 1206–1214 (2017) 9. Schwab, K.: The Fourth Industrial Revolution. Currency, Redfern (2017)

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10. Schumacher, A., Erol, S., Sihn, W.: A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia Cirp 52, 161–166 (2016) 11. Porter, M.E., Heppelmann, J.E.: How smart, connected products are transforming competition. Harvard Bus. Rev. 92(11), 64–88 (2014) 12. Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M.: How virtualisation, decentralisation and network building change the manufacturing landscape: an Industry 4.0 perspective. Int. J. Mech. Ind. Sci. Eng. 8(1), 37–44 (2014) 13. Gilchrist, A.: Industry 4.0: The Industrial Internet of Things. Apress, Berkeley (2016) 14. Henning, K.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0 (2013) 15. Frank, A.G., Dalenogare, L.S., Ayala, N.F.: Industry 4.0 technologies: implementation patterns in manufacturing companies. Int. J. Prod. Econ. 210, 15–26 (2019) 16. Oesterreich, T.D., Teuteberg, F.: Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry. Comput. Ind. 83, 121–139 (2016) 17. Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., Ueda, K.: Cyber-physical systems in manufacturing. CIRP Ann. 65(2), 621–641 (2016) 18. Kim, W., Sung, M.: OPC-UA communication framework for PLC-based industrial IoT applications. In: 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI), pp. 327–328. IEEE, April 2017 19. Ferreira, L.L., Albano, M., Silva, J., Martinho, D., Marreiros, G., Di Orio, G., Maló, P., Ferreira, H.: A pilot for proactive maintenance in Industry 4.0. In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), pp. 1–9. IEEE, May 2017 20. Dalenogare, L.S., Benitez, G.B., Ayala, N.F., Frank, A.G.: The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ. 204, 383–394 (2018) 21. Omer, Abdu Idris, Taleb, M.M.: Architecture of industrial automation systems. Eur. Sci. J. ESJ 10(3), 273–283 (2014) 22. Ferreira, L.L., Albano, M., Silva, J., Martinho, D., Marreiros, G., Di Orio, G., Maló, P., Ferreira, H.: A pilot for proactive maintenance in Industry 4.0. In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), pp. 1–9. IEEE (2017) 23. Hannelius, T., Salmenpera, M., Kuikka, S.: Roadmap to adopting OPC.UA. In: 6th IEEE International Conference on Industrial Informatics, INDIN 2008. IEEE (2008) 24. Sakib, N., Wuest, T.: Challenges and opportunities of condition-based predictive maintenance: a review. Procedia CIRP 78, 267–272 (2018)

Chapter 11

Operational Modal Analysis in the Presence of Pulse Train and Harmonics Based on SSI Fulong Liu , Jiongqi Wang , Miaoshuo Li , Fengshou Gu , and Andrew D. Ball Abstract Operational Modal Analysis (OMA) is a popular and effective method to identify the dynamic characteristics of a structure for Condition Monitoring (CM). It is well known that most of OMA methods are under the assumption that the excitation loads are stationary white noise. In practice however, this is not true, the excitation with pulse train and harmonic loads are common for mechanical systems with rotation parts, such as wind turbine and vehicle tested on the roller rig. In order to investigate the effects of pulse train and harmonic loads on the OMA, a quarter vehicle model was developed to simulate a Y25 bogie tested on the roller rig. Moreover, Correlation signal Subset based Stochastic Subspace Identification (CoS-SSI) was employed as the OMA technique in this study. The simulation results indicated that pulse train excitation has no effects on the OMA, whereas harmonic loads have significant effects. On the one hand, harmonic loads will result in false modes, on the other hand, the harmonic frequency will overwhelm the true modes of tested systems when the harmonic frequency is close to system resonance frequency. Therefore, cepstrum editing process was introduced in detail, and employed to filter out the harmonic effects before the OMA process. It has been proved that cepstrum editing is an easy but powerful approach to address the challenge of OMA in the presence of harmonics. J. Wang (&) College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, People’s Republic of China e-mail: [email protected] F. Liu
M. Li
F. Gu
A. D. Ball Centre for Efficiency and Performance Engineering (CEPE), School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK e-mail: [email protected] M. Li e-mail: [email protected] F. Gu e-mail: [email protected] A. D. Ball e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_11

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In recent years, Operational Modal Analysis (OMA) techniques [1, 2] are becoming increasingly popular to monitor the condition of vital components in a system, since they are robust, efficient and achievable. It is well known that most of the current OMA approaches are developed based on the assumption of stationary white-noise inputs. However, the harmonic inputs are quite common in practice, especially in the rotating machine like wind turbine, gearbox and roller rig. The robustness of OMA techniques will decrease on account of the harmonic excitations [3], which is why a great many of researches have been conducted to deal with this issue. Particularly, the harmonic inputs could result in three different failure cases during the process obtaining accurate modal parameters via OMA approaches [4]. Firstly, the harmonic frequency of inputs could be identified as a structural mode, which is difficult to distinguish from the true modes. Secondly, the harmonic input has significant effects on the structural modes when the harmonic frequency is close to a natural mode, which could result in a poor damping estimation. Thirdly, the false mode resulted from harmonic excitation could overwhelm true modes due to the main power of system responses are caused by the harmonic inputs. In order to handle the issue of OMA in the presence of harmonics, numerous extended OMA techniques were developed. A decent review about these techniques can be found in [4], which clustered them into four categories. The first category is the statistics based methods, which means the statistics parameters are adopted to verify the identified modes either false or not. For instance, the Probability Density Function (PDF) was employed in [5] to filter out false modes resulted from harmonic loads. The reason for PDF has this capability is because the stochastic properties of a structural mode responses which are caused by the harmonic inputs or the narrow band stochastic inputs are completely different from the responses resulted from stationary white-noise excitation. Similarly, the Kurtosis was employed in [6] to filter out the effects of harmonic inputs. However, the statistics based methods will fail if the harmonics are damped too heavily, or the harmonic frequency is close to system natural frequency. The second category methods to suppress the harmonic effects are directly modifying the existed general OMA methods by taking harmonic excitation into consideration in the algorithm. In [3], the least-square complex exponential identification procedure was modified to properly identify the modal parameters even under harmonic excitations. In addition, the same research team proposed the modified Eigensystem Realisation Algorithm (ERA) [7], the modified Ibrahim time domain algorithm [8] and the modified Single Station Time Domain (SSTD) method. Besides, a modified Stochastic Subspace Identification was proposed in [9]. However, most of the modified methods have to know the harmonic frequencies exactly beforehand. Besides, the third group methods are denoted as Transmissibility based OMA (TOMA) [10, 11] since they are based on the transmissibility measurements. The TOMA method is inherently suitable for OMA in the presence of harmonics

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since the inputs for this can be arbitrary. However, the inputs have to be persistently exciting in the interested frequency range [12, 13]. Last but not least, the fourth category methods to eliminate the harmonic effects in advance are denoted as signal pre-process techniques, which means a pre-process procedure will be conducted to the response signals beforehand the OMA process. There are several techniques can be used to perform this pre-process procedure, such as non-parametric removal method [14], Time Synchronous Averaging (TSA) [15] and cepstrum editing [16–18]. Among these three approaches, cepstral methods are the easiest and cheapest in technique but robust. Even compared with all of the previous referred techniques, cepstrum editing is outstanding considering the computation cost, robustness, effectiveness and implementability. In this study, the primary purpose is monitoring the primary suspension of Y25 bogie via OMA. However, the Y25 bogie was tested on a roller rig, which involved pulse train and harmonic excitations from the roller. Therefore, a cepstral method was developed to remove the effects from harmonics. Moreover, the employed OMA approach is denoted as Correlation signal Subset based SSI (CoS-SSI), which can be found in [19, 20]. This paper is focusing on the harmonic elimination and therefore, the CoS-SSI will not introduce in detail. This paper is organized as follows: Sect. 11.2 will introduce the cepstral method in detail; Sect. 11.3 will present the OMA results when the vehicle model under various excitation situations through simulation. Section 11.4 will verify the proposed method by experiment study and Sect. 11.5 will present the conclusions based on previous study.

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Cepstrum Editing for OMA

The idea of cepstrum editing to eliminate the harmonic effects during OMA was firstly proposed in [16]. The cepstrum was defined as the inverse Fourier transform of the log power spectrum [16, 21], shown as Eq. (11.1). Cc ðsÞ ¼ F 1 flnðX ð f ÞÞg ¼ F 1 flnðAð f ÞÞ þ juð f Þg

ð11:1Þ

Where X ð f Þ is the frequency spectrum of raw signals x½k: X ð f Þ ¼ F fx½kg ¼ Að f Þejuð f Þ

ð11:2Þ

The real cepstrum can be obtain by setting the phase in Eq. (11.1) to zero: Cr ðsÞ ¼ F 1 flnðAð f ÞÞg

ð11:3Þ

Where s is the frequency. The cepstrum is reversible to power spectrum after editing, while the harmonic effects can be removed by the editing process. The cepstrum editing process is

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Fig. 11.1 Cepstrum editing procedure

conducted to the real cepstrum, where the detail of cepstrum editing is shown as Fig. 11.1. Firstly, the raw signals are transferred into frequency domain by Fast Fourier Transform (FFT). Then, the phase information u is stored for later recovering the signals, and the Inverse FFT (IFFT) is applied on amplitude signal with log operation log½jX ð f Þj to obtain the real cepstrum Cr ðsÞ: Subsequently, a short-pass lifter [16] is applied to real cepstrum Cr ðsÞ; due to the modal information is kept at the start and ends of the cepstrum. Then the edited cepstrum ECr ðsÞ is convert to frequency domain through FFT, and then combined with the original phase u to obtain the edited log spectrum EX ð f Þ. Later, the edited log spectrum EX ð f Þ has to recover to the complex spectrum by setting it as the index of an exponential function considering the previous logarithmic operation. Finally, the filtered time-domain signals ^x½k can be obtained by carrying out IFFT to the complex spectrum obtained in previous step, and the filtered signals ^x½k  can be employed in the following OMA. The main drawback of cepstrum editing to OMA is an extra damping will be added to the identified results if the short-pass lifter is an exponential function. However, a piecewise defined function will be implemented as the short-pass lifter which can overcome this shortage. The effectiveness of cepstrum editing will be evaluated by simulation and experimental study.

11.3

OMA of System Under Different Excitation Cases

11.3.1 Quarter Vehicle Model A quarter vehicle model was developed, shown as in Fig. 11.2. It is developed to simulate Y25 bogie tested on the roller rig, and therefore including the carbody (bogie frame), wheel, primary suspension, rail drum (roller), and contact stiffness between wheel and rail drum. It is worth to notice that the rail drum is composed by four parts of arc rails, so there are four junctions on the rail drum which will introduce pulse train excitation during the test. Moreover, the rail drum is a rotating part, therefore the harmonic excitation is inevitable because of the installation and

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Fig. 11.2 Schematic of 2-DOF quarter vehicle model

Fig. 11.3 System frequency response function

manufacture errors. As a primary study, this paper will present the simulation results of the OMA in the presence of pulse train and harmonic loads. The frequency response function of this model has been obtained using the parameters presented in Fig. 11.2 and shown as in Fig. 11.3. It can be seen from Fig. 11.3 that the first resonance frequency is 13.21 Hz and the second one is 56.35 Hz. Besides, the damping ratios for the two modes are 13.10% and 6.33%, respectively.

11.3.2 Excitation Signal Model The rail drum is a rotating part which will generate excitations to the system. In order to better illustrate the characteristics of excitation loads, a model of the

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excitation signal was developed. Initially, as referred earlier, the rail drum is composed by four parts of quarter circle rails, so four joints are existed on the rail drum which will result in period pulse excitations, shown as Fig. 11.2. In addition, the rotating part will introduce harmonic excitation due to installation and manufacture errors. Besides, random excitation is inevitable caused by the roughness of the wheel and rail drum surfaces. Therefore, the excitation signals can be modelled as: yðtÞ ¼ A1 y1 ðtÞ þ A2 y2 ðtÞ þ A3 y3 ðtÞ

ð11:4Þ

Where A1 , A2 and A3 are the amplitudes of pulse train excitation y1 ðtÞ, harmonic excitation y2 ðtÞ and random excitation y3 ðtÞ, respectively. The pulse train can be presented as:
y 1 ðt Þ ¼

1 t ¼ n  1=fex ; 0 others

n ¼ 1; 2; 3. . .

ð11:5Þ

Where fex is four times of the rotation frequency of the rail drum. The harmonic excitations can be modelled as: y2 ðtÞ ¼

XN i¼1

sinð2pfi t þ ui Þ

ð11:6Þ

Where N is the total number of harmonics; fi and ui are the frequency and phase of ith harmonic load. The random excitation can be modelled as: y3 ðtÞ ¼ randnðtÞ

ð11:7Þ

11.3.3 OMA Results of System Responses Under Different Excitation Cases The quarter vehicle model under different excitation situations were explored to investigate the effects of pulse train and harmonics on the OMA. Four cases were examined, including random excitations, random added with pulse train excitations, random added with pulse train and one harmonic excitations, and the last case is random added with pulse train and two harmonics excitations. All cases were simulated a data length of 60 s with sampling rate of 1000 Hz, and each case generated 10 datasets for the OMA via CoS-SSI. Case 1: random excitation only The first case is the quarter vehicle model under random excitation only. According to the excitation model, the amplitudes of the three parts were set as follows:

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Fig. 11.4 Quarter vehicle model responses under random excitation only

A1 ¼ 0; A2 ¼ 0; A3 ¼ 0:0001m; The displacement responses of the carbody under random excitations were calculated. An example of the responses and the corresponding frequency domain signal were presented in Fig. 11.4. It can be seen that the response magnitudes around 13 Hz and 56 Hz are superior, where are close to the resonance frequencies. Then, the 10 datasets of carbody displacement responses were employed to identify system’s modal parameters via CoS-SSI. Because the quarter vehicle model is developed as an ideal linear system, the correlation signal calculated from displacement responses was classified into a single subset [19, 20]. The identified Stabilization Diagram (SD) was presented in Fig. 11.5. Two stable modes are identified in Fig. 11.5, which are 13.19 Hz and 56.42 Hz, respectively. The errors between two identified natural frequencies and the theoretical ones are 0.15% and 0.12%, respectively. In addition, the identified damping ratios are 13.03% and 6.66%. The errors of identified damping ratios are 0.53% and 5.21%. However, only the carbody responses were considered during the OMA process, therefore, the mode shape is meaningless. Based on the previous analysis, it can conclude that the CoS-SSI has the capability to accurately identify the modal parameters of system when the excitations are stationary white noise.

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Fig. 11.5 SD identified by CoS-SSI for system under random excitation only

Case 2: random and period pulse train excitations The second case is the model under pulse train and random excitations. The amplitude of the pulse train A1 is 0.005 m and the amplitude of white noise excitation is same as in case one which is 0.0001 m. The pulse amplitude is much higher than the white noise according to the system features. The frequency of the pulse train fex is set at 3.65 Hz. In short, the excitation signal parameters are given as below: A1 ¼ 0:005 m; A2 ¼ 0; A3 ¼ 0:0001 m; fex ¼ 3:65 Hz: An example of the carbody responses and the matching frequency domain signal are presented in Fig. 11.6. As can be seen from the time domain signal, there are apparently pulse train responses which are resulted from the pulse train excitation. Moreover, it can be seen from the frequency domain signal that higher peaks appeared at the frequencies of integral multiples of 3.65 Hz. Moreover, it can be seen the peaks around the resonance frequencies (13.21 Hz and 56.35 Hz) are superior than others. The SD identified by CoS-SSI when the vehicle model is under white noise and pulse train excitations are shown as Fig. 11.7. It can be seen that two extreme stable modes were identified which is same as the previous case. In other word, the pulse train excitation has no effects on the modal identification process. Particularly, the identified frequency of the first mode is 13.20 Hz, which is nearly exact the same as theoretical result (13.21 Hz). The identified damping ratio for the first mode is 12.11%, and the error is 7.56%. In addition, the identified frequency of the second order is 56.12 Hz, which is also quite close to the theoretical value (56.35 Hz). The damping ratio for the second mode is 6.40%, which identification error is 1.11% compared with theoretical value. It can be seen that the modal parameters were identified accurately as well. Therefore, we can conclude that although the pulse train excitation resulted in period peaks in the frequency domain signal, no effects on the OMA results.

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Fig. 11.6 Quarter vehicle model responses under random and pulse train excitations

Fig. 11.7 SD identified by CoS-SSI for system under random and pulse train excitations

Case 3: random, pulse train and one harmonic excitations In this case, one harmonic signal ðN ¼ 1Þ was added based on the excitations which were generated in the second case. The harmonic excitation is to simulate the eccentric load which is resulted from manufacture and installation errors of rotating part, such as rail drum in this study. In particular, the amplitude of the harmonic signal A2 is same as the random signal’s, and the harmonic frequency f1 is equal to the pulse train frequency fex . The phase of the harmonic signal u1 is set at p=3. The detail of parameters for the signal model is given as follows:

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Fig. 11.8 Responses of quarter vehicle model under random, pulse train and one harmonic excitations

A1 ¼ 0:005 m; A2 ¼ 0:0001 m; A3 ¼ 0:0001 m; fex ¼ 3:65 Hz; f1 ¼ 3:65 Hz; u1 ¼ p=3; The carbody responses under this excitation signal are shown as in Fig. 11.8. It can be seen that the time domain signal is similar to the responses without harmonic excitation, shown as Fig. 11.6. However, the frequency domain signal has a little bit difference where the amplitude at 3.65 Hz is greater than the responses without harmonic load, because of the harmonic frequency is at 3.65 Hz. The SD identified from the response with one harmonic excitation is presented in Fig. 11.9. It can be seen that three stable modes were identified in the SD. The frequencies for these three modes are 3.56 Hz, 13.14 Hz and 56.37 Hz, and the corresponding damping ratios are 1.71%, 13.06% and 6.43%. Compared with the theoretical results, it can be seen that the first mode is a false one, which is caused by the harmonic excitation. Moreover, it can observe that the stable points of mode at 13.14 Hz are less than the points obtained in the previous two cases due to the harmonic effects. Apparently, the harmonic load has significant effects on the OMA results.

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Fig. 11.9 SD identified by CoS-SSI for system under random, period pulse and one harmonic excitations

Fig. 11.10 Responses of quarter vehicle model under random, pulse train and two harmonics excitations

Case 4: random, pulse train and two harmonics excitations In order to investigate the effects of harmonic loads to the OMA in a further step, a second harmonic signal ðN ¼ 2Þ was added to the excitation signal in Case 3. The second harmonic frequency is 17 Hz and phase is p=4. The details of the excitation signal model parameters are given as follows: A1 ¼ 0:005 m; A2 ¼ 0:0001 m; A3 ¼ 0:0001 m; fex ¼ 3:65 Hz; f1 ¼ 3:65 Hz; u1 ¼ p=3; f2 ¼ 17:0 Hz; u2 ¼ p=4; The responses of the quarter vehicle model in such excitation loads are shown as in Fig. 11.10. It can be seen that the time domain signal is almost the same as the

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Fig. 11.11 SD identified by CoS-SSI for system under random, pulse train and two harmonics excitations

responses in Case 2 and 3. However, it is obvious that an extraordinary peak appeared at 17 Hz in the frequency domain which resulted from the second harmonic load. The identified SD in this case is shown as Fig. 11.11. It can be seen that three relative stable modes are identified. Their frequencies are 3.56 Hz, 17.37 Hz and 56.39 Hz, and their damping ratios are 17.74%, 5.64% and 6.37%, respectively. First of all, it can be seen that two false modes, the first two modes, are identified, which are caused by these two harmonic loads. Secondly, it has to notice that the first theoretical mode which is around 13.21 Hz was failure to be identified. This could be due to the second harmonic frequency (17.0 Hz) is close to the first natural frequency of the system. Based on the results in Case 3 and 4, it can draw the conclusion that the harmonic loads will lead to false modes which are difficult to distinguish from true modes. Furthermore, the harmonic loads could overwhelm system’s modes when the harmonic frequencies are close to the natural frequency. Therefore, the harmonics have to be removed beforehand the OMA process.

11.3.4 OMA Results Using Filtered Response Signal by Cepstrum Editing This section will employ the cepstrum editing method which has been introduced in Sect. 11.2 to filter out the harmonic effects in Case 3 and Case 4. Firstly, it is worth to state the mechanism of cepstrum editing again that the modal information will be kept in the beginning and end of the cepstrum. Therefore, a short-pass lifter window function was employed to retain the beginning and the end of the cepstrum, and the middle part was set to zero. The length of the retained cepstrum is around 1 s at the beginning and end.

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Case 1: One harmonic in the excitation signal The first case is to filter out the harmonic effects when the excitation loads contained one harmonic frequency, which is the response signal obtained in Case 3 of Sect. 11.3.3. An example of the real cepstrum Cr ðsÞ, edited cepstrum ECr ðsÞ and

Fig. 11.12 Cepstrum and window function

Fig. 11.13 Raw and filtered signal (one harmonic)

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window function of the short-pass lifter is shown as Fig. 11.12. It can be observed that only the beginning and end of the cepstrum was reserved after the editing by the window function of short-pass lifter. Figure 11.13 is the raw time domain signal and the corresponding filtered signal via cepstrum editing, and the frequency domain signal is presented below. First, it is obvious that the amplitudes of filtered signal are smaller than the raw signal. Second, it can observe that the harmonic frequency and frequencies resulted from pulse train are filtered out through the cepstrum editing. The filtered signal was used as the inputs for OMA, and the SD identified by CoS-SSI is presented in Fig. 11.14. Compared with Fig. 11.7, it can be seen that the false mode caused by harmonic has been eliminated and only two stable modes were identified. The frequencies of the two modes are 13.18 Hz and 56.02 Hz, respectively, and the corresponding damping ratios are 12.37% and 5.76%. The errors for identified frequencies of the two modes are 0.23% and 0.59%, which are pretty small. Moreover, the errors of damping ratios for the two modes are 5.57% and 9.0%, which is also acceptable. Furthermore, it is worth to notice that no additional damping ratio was introduced by the short-pass lifter. Thus, we can conclude that cepstrum editing is an effectiveness and achievable approach to eliminate the harmonic effects during OMA in the presence of harmonics. Case 2: Two harmonics in the excitation signal In order to verify the effectiveness of cepstrum editing in a further step, the responses in Case 4 of Sect. 11.3.3 were filtered through cepstrum editing. The SD identified by the filtered signal is shown as Fig. 11.15. It is noticeable by comparing with Fig. 11.11 that the harmonic effects have been eliminated and two true stable modes were identified. The identified frequencies for the stable modes are 13.15 Hz and 56.53 Hz, and the corresponding damping ratios are 13.01% and 5.65%, respectively. The errors of identified frequencies are 0.45% and 0.32%, and the errors of identified damping ratios are 0.69% and 10.74%. It can be seen that the errors are quite small, which indicated the availability of cepstrum editing to address the challenge of OMA in the presence of harmonics.

Fig. 11.14 SD identified by COS-SSI for system with filtered signal(one harmonic)

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Fig. 11.15 SD identified by CoS-SSI for system with filtered signal (two harmonics)

Fig. 11.16 Photograph of test rig

11.4

Experiment Results and Discussion

11.4.1 Test Rig Introduction The experiments were carried out on a full-size roller rig in the laboratory of Institute of Railway Research (IRR) at University of Huddersfield (UoH). The detail of the test rig can be found in [22]. In this study, an Y25 bogie was installed on the roller rig, shown as Fig. 11.16. The main parts of the test system included a Y25 bogie, load mass, hydraulic drive load cell and rail drum. It is worth to notice that only one wheelset was fixed on the rail drum, and the other wheelset was laid

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Fig. 11.17 Schematic of the test rig

on the test bed which built in two tracks. Figure 11.17 is the schematic of the test system to better illustrate the system. In this test, six accelerometers were employed and their positions can be seen in Fig. 11.17: two of them on the axle boxes and the other four mounted on the bogie frame where upon the axle boxes. The purpose of the test is trying to identify the rigid modes of the bogie frame for condition monitoring of the primary suspension systems, due to the rigid modes are close related to the primary suspension. Therefore, only the bogie responses were adopted in the following identification via CoS-SSI. The measurement frequency ranges of the employed accelerometers are from 0.5 Hz to 5000 Hz, which are sufficient for the test purpose in this study. The sampling frequency rate was set at 1000 Hz, and sampling time is 60 s. Six datasets were obtained to identify the modal parameters. Besides, the rotating frequency of the rail drum is around 0.91 Hz. The characteristics of the obtained signal has been analysed in the next subsection.

11.4.2 Characteristics of Responses An example of the collected bogie frame response at four corners is presented in Fig. 11.18. The FL indicates Front Left of the bogie frame where upon the axle box, and the wheelset on the rail drum is defined as front part. Similarly, FR, RL and RR

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Fig. 11.18 Time-domain signal of bogie frame response at four corners

Fig. 11.19 Frequency-domain signal of bogie frame response at four corners

means Front Right, Rear Left and Rear Right, respectively. The position of sensors can be seen more clear in Fig. 11.17. Figure 11.18 presents the first five seconds of the bogie frame response. It can be seen clearly that the pulse response caused by junctions on the rail drum. Moreover, it can be seen that the response amplitudes of front part are greater than rear part, because front wheelset was directly excited by the rail drum. The corresponding frequency domain signal is presented as Fig. 11.19. As can be seen from Fig. 11.19, the peaks are at the frequency of integral multiples of rotating frequency. Moreover, the superior peaks are at integral multiples of 3.65 Hz which is around four times of rotating frequency. Such result is because the rail drum has 4 junctions. In addition, comparing Figs. 11.8, 11.11, 11.13 and 11.18, we can see the characteristics of simulated signal kept in line with the experiment signal.

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11.4.3 Modal Identification Results Same as the simulated signal, CoS-SSI was employed to identify the modal parameters. The SD identified from the raw response signal is shown as Fig. 11.20. It can be seen that none stable mode was identified. Figure 11.21 is the SD identified from filtered signal via cepstrum editing. It is apparent that several relative stable modes were identified, and the final identified modal parameters are presented in Fig. 11.22. It can be seen from Fig. 11.22 that the first three modes are bounce, pitch and roll, respectively, according to their frequencies and mode shapes. Because of the excitation and load conditions, the identified mode shapes are a little bit different to the standard bounce, pitch and roll mode shapes. However, this result is acceptable and can perform as the base line for condition monitoring of primary suspension systems. Moreover, the experiment results verified the effectiveness of cepstrum editing removing harmonics in a further step.

Fig. 11.20 SD identified from the unfiltered signal

Fig. 11.21 SD identified from the filtered signal

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Fig. 11.22 Modal parameters identified by CoS-SSI

11.5

Conclusions

The effects of OMA in the presence of pulse train and harmonics were investigated in this paper through simulation study. Moreover, the cepstrum editing was introduced in detail to filter out harmonic effects during OMA. Particularly, a quarter vehicle model was developed to simulate the bogie tested on the roller rig. Then, the carbody responses of the quarter vehicle model under various excitation situations were obtained, and adopted to identify the modal parameters via CoS-SSI. In addition, the effectiveness of cepstrum editing to remove harmonics for OMA is verified in a further step through experiment study. The identification results obtained from simulated data showed that the pulse train excitation has no effects for the OMA. However, the harmonics will lead to false mode and even overwhelm the system modes. Therefore, the response signal was filtered by cepstrum editing beforehand OMA to remove the harmonic effects. The results identified from filtered data indicated that cepstrum editing is a powerful and achievable approach to address the challenge of OMA in the presence of harmonics. The experiment study verified this conclusion in a further step. Acknowledgements The author would like to thank the China Scholarship Council (CSC Grant No: 201608060041) for the sponsorship of the project carried out in this study.

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References 1. Peeters, B., Roeck, G.D.: Stochastic system identification for operational modal analysis: a review. J. Dyn. Syst. Meas. Control 123(4), 659–667 (2001) 2. Reynders, E.: System identification methods for (operational) modal analysis: review and comparison. Arch. Comput. Methods Eng. 19(1), 51–124 (2012) 3. Mohanty, P., Rixen, D.J.: Operational modal analysis in the presence of harmonic excitation. J. Sound Vib. 270(1), 93–109 (2004) 4. Motte, K., Weijtjens, W., Devriendt, C., Guillaume, P.: Operational modal analysis in the presence of harmonic excitations: a review. Dyn. Civil Struct. 2, 379–395 (2015) 5. Modak, S.V., Rawal, C., Kundra, T.K.: Harmonics elimination algorithm for operational modal analysis using random decrement technique. Mech. Syst. Signal Process. 24(4), 922– 944 (2010) 6. Dion, J.-L., Tawfiq, I., Chevallier, G.: Harmonic component detection: optimized spectral kurtosis for operational modal analysis. Mech. Syst. Signal Process. 26, 24–33 (2012) 7. Mohanty, P., Rixen, D.J.: Modified ERA method for operational modal analysis in the presence of harmonic excitations. Mech. Syst. Signal Process. 20(1), 114–130 (2006) 8. Mohanty, P., Rixen, D.J.: A modified Ibrahim time domain algorithm for operational modal analysis including harmonic excitation. J. Sound Vib. 275(1), 375–390 (2004) 9. Dong, X., Lian, J., Wang, H., Yu, T., Zhao, Y.: Structural vibration monitoring and operational modal analysis of offshore wind turbine structure. Ocean Eng. 150, 280–297 (2018) 10. Devriendt, C., De Sitter, G., Vanlanduit, S., Guillaume, P.: Operational modal analysis in the presence of harmonic excitations by the use of transmissibility measurements. Mech. Syst. Signal Process. 23(3), 621–635 (2009) 11. Maamar, A., Abdelghani, M., Le, T.-P., Gagnol, V., Sabourin, L.: Operational modal identification in the presence of harmonic excitation. Appl. Acoust. 147, 64–71 (2019) 12. Devriendt, C., Guillaume, P.: The use of transmissibility measurements in output-only modal analysis. Mech. Syst. Signal Process. 21(7), 2689–2696 (2007) 13. Devriendt, C., Guillaume, P.: Identification of modal parameters from transmissibility measurements. J. Sound Vib. 314(1), 343–356 (2008) 14. Pintelon, R., Peeters, B., Guillaume, P.: Continuous-time operational modal analysis in the presence of harmonic disturbances. Mech. Syst. Signal Process. 22(5), 1017–1035 (2008) 15. Peeters, B., Cornelis, B., Janssens, K., Van der Auweraer, H.: Removing disturbing harmonics in operational modal analysis. In: Proceedings of International Operational Modal Analysis Conference, Copenhagen, Denmark (2007) 16. Randall, R.B.: Cepstral methods of operational modal analysis. In: Encyclopedia of Structural Health Monitoring. American Cancer Society (2009) 17. Randall, R.B., Coats, M.D., Smith, W.A.: Repressing the effects of variable speed harmonic orders in operational modal analysis. Mech. Syst. Signal Process. 79, 3–15 (2016) 18. Randall, R.B.: A history of cepstrum analysis and its application to mechanical problems. Mech. Syst. Signal Process. 97, 3–19 (2017) 19. Liu, F., Wu, J., Gu, F., Ball, A.D.: An introduction of a robust OMA method: CoS-SSI and its performance evaluation through the simulation and a case study. Shock Vib. (2019). https:// www.hindawi.com/journals/sv/2019/6581516/abs/. Accessed 15 Mar 2019 20. Liu, F., Zhang, H., He, X., Zhao, Y., Gu, F., Ball, A.D.: Correlation signal subset-based stochastic subspace identification for an online identification of railway vehicle suspension systems. Veh. Syst. Dyn. 58(4), 569–589 (2019) 21. Randall, R.B., Sawalhi, N.: Use of the cepstrum to remove selected discrete frequency components from a time signal. In: Rotating Machinery, Structural Health Monitoring, Shock and Vibration, vol. 5, pp. 451–461 (2011) 22. Facilities: University of Huddersfield. https://research.hud.ac.uk/institutes-centres/institutes/../ irr/facilities/. Accessed 30 Apr 2019

Chapter 12

The Development of a Maintenance Gap Analysis Tool for Use Within the Automotive Supply Chain: A Case Study Perspective Derek Dixon , Kenneth Robson, and David Baglee Abstract Automotive manufacture contributed £82 billion to the UK Economy in 2017. In addition, the production of vehicles within the UK continues to rise, with 1.65 million vehicles produced in 2017. Lean production methods, compounded by synchronous delivery to an Original Equipment Manufacturer (OEM), ensure membership of the automotive supply chain is challenging. To meet this challenge, participants in manufacturing operations within any business must operate both effectively and efficiently. Interestingly, despite the apparent success of the industry, research has revealed that disjointed maintenance practice within the supply chain is evident and augmenting a difficult production environment. This research gathered empirical data from four case study partners who operate at Tier 1 within the automotive supply chain. The findings demonstrate the majority of research participants operate with an underperforming maintenance department due to a number of barriers and constraints. A worrying consequence of poor maintenance execution and an unsupportive maintenance culture has also emerged. To mitigate the risk of poor maintenance performance, manufacturers are retaining excessive safety stock to ensure delivery targets are met. As well as establishing constraints preventing maintenance performance, areas of best practice have been highlighted, with both characteristics integrated into a Gap Analysis Tool. This paper will discuss the development and subsequent testing of the Gap Analysis Tool with one case study participant and the potential impact for a manufacturer within the automotive supply chain.

D. Dixon (&)
K. Robson
D. Baglee Faculty of Technology, University of Sunderland, Sunderland, UK e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_12

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Introduction

Over the previous decade, the automotive industry has experienced an unprecedented period of growth and success. Presently, import and export through the European Union is uncertain and the industry is undergoing new challenges. To place this is context, in 2017 54% of cars assembled in the UK were exported to Europe with around 10% of the UK Automotive workforce originating from EU countries [1]. Moreover, the automotive supply chain occupies a crucial position within this industrial environment, employing over 82,000 people in 2500 suppliers in the UK [1]. The difficulties of current trading conditions combined with the perennial intensity of lean manufacture, have led to an increased pressure on all business stakeholders to operate effectively. Internal stakeholders include the maintenance function and this paper includes evidence and findings from recent doctoral research carried out with 4 case study partners within the automotive supply chain. Findings reveal the impact of contributing factors including poor allocation of resources, ineffective maintenance strategies, negative organisational attitudes and superficial performance measurement. The discussion will focus upon 1 case study partner and expose the difficulties experienced by the maintenance function when suffering inertia at an organisational level.

12.2

Literature Review

The automotive manufacturing industry operates with lean production principles, where the aim is to eliminate waste and reduce cost, by maximising resources and efficiency [2–4]. Lean production can be characterised by concepts such as Just in Time (JIT), Total Productive Maintenance (TPM) and Total Quality Management (TQM) [5]. [3, 6] begin to highlight some challenges of this production methodology, where participating suppliers may feel pressurised into accepting the responsibilities of additional inventory, to ensure consistent on time delivery. Importantly, [6] conclude that the reduction of inventories and thus waste, can expose numerous issues within the business that were previously masked by stock. The attraction towards a using a TPM led strategy within a lean production environment is high, as [7] identifies that TPM can maximise the performance of manufacturing equipment. The success of this strategy requires organisational participation, including production staff and senior management engagement [4]. [8] discuses some of the wider issues, which affect the success of initiatives such as TPM. This includes the difficulty incorporating varying degrees of degradation and wear which may appear in production equipment. The degradation and wear can be accelerated due to extreme process loading for increased production requirements. Consequently, the anticipated gains TPM may offer, do not materialise.

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12.2.1 Maintenance Strategy Fundamentals Although the automotive manufacturing industry may aim to operate with lean principles through all aspects of the plant, initial research findings confirm the maintenance strategy can operate in a distinct and separate way. TPM may have specific characteristics, yet there are common features which form the foundation of the majority of maintenance strategies. Reviewing these features can lead to an understanding of the complexity and organisational input required to formulate any maintenance plan [9]. The categorisation of these maintenance strategy components varies throughout scholarly work, though elements remain relatively common. Table 12.1 identifies fundamental elements which may form part of a maintenance strategy. A maintenance concept may be defined as a preventative or aggressive plan [19] or indeed a more prescribed direction such as TPM [4]. Table 12.1 also includes performance measurement which is essential for improving maintenance performance [28]. Furthermore, [28] also recognises the need to measure human features of maintenance, albeit in a qualitative way. Other elements of Table 12.1 include industrial context, training and staff engagement which are human in nature and will vary between plants. Yet the inclusion of these elements is an essential aspect of a successful maintenance strategy. As a result, there remains a significant challenge in addressing these features [29]. The human elements as well as structural features such as performance measurement and management information also demonstrate the importance of the behaviour and working practices of the wider organisation. As discussed by [30–32] the behaviour and working practices of an organisation represent the belief and values of the business. These beliefs and values may be defined as the organisational culture.

Table 12.1 A summary identifying common elements of a maintenance strategy

Characteristic requirement

Reference

Maintenance concept Performance Measurement Management information system Budget and cost management Maintenance capacity (workforce) Maintenance facilities (Tools, equipment) Industrial context Skills and Training Staff engagement

[4, 7, 9–12] [11, 13–15] [9, 16–18] [7, 9, 12, 19, 20] [9, 19, 21] [9, 19, 22] [17, 23–25] [18, 20, 26, 27] [9, 18, 24]

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12.2.2 Organisational Culture The culture within any organisation has a dramatic impact on the success of the business strategy [29, 33]. [34] defines culture as linking the beliefs and values of employees as well as the organisation, which result in specific modes of behaviour. As a result, the importance of organisational culture becomes apparent. The modes of behaviour are further defined by [30], who associates the beliefs and values with communication and interaction between participants. Alongside these [31, 32] debate the relevancy of the beliefs and values being visible in the form of artefacts. Artefacts include everyday business items such as technology, equipment, physical environment as well as modes of behaviour. In this way, artefacts also act as an indicator of the culture of an organisation. The link described by [29, 33] between strategy and culture is important and could be described as an enabling characteristic of a positive organisational culture. These include leadership and staff engagement, communication, resources, motivation, staff autonomy and training. The significance of these enablers may be demonstrated by the success of a manufacturing strategy requiring a supportive culture [31, 33, 35, 36]. Importantly, these enablers reflect the human characteristics recognised by [9, 18, 24] as being of fundamental importance within maintenance management. In this way, there exists a clear link between a positive organisational culture and successful maintenance management [37].

12.2.3 Summary The success of the automotive manufacturing industry is strongly linked to the use of lean production methods throughout the business. Consequently, the use of a TPM strategy becomes attractive, yet the required business commitment is extensive and not always realistic. As a result, addressing site specific issues when developing a maintenance strategy become essential. Additional contributing factors are more human and include communication, staff training and workforce engagement. These elements are also fundamental features which represent the culture of an organisation. Moreover, [31, 32] identify the importance of symbols or artefacts in demonstrating the value an organisation may place on a certain person, department or even position. As a result, how the maintenance function is overtly and physically represented becomes important. Consequently, the requirement for a successful maintenance strategy to have a supportive culture becomes clear. In addition, understanding the elements which contribute towards an effective maintenance strategy but also have an effect on the culture of the organisation may lead to improved effectiveness [37]. Conclusively, considering all factors which contribute towards the success of a maintenance plan is of increased importance.

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Methodology

The rich data assimilated by the case study work was subsequently coded and categorised. This was combined with a series of propositions from the literature review, forming the basis of a gap analysis tool. The tool contains a specific area of investigation which exist within each coded category. The specific areas of investigation varied between four and nine for each category. A score was awarded by the assessor for each point of investigation based upon the response and evidence produced. The response was scored on a sliding scale from 4 for good practice to 1 for very poor practice.

12.4

Case Study Findings

This section will begin by discussing the background and industrial context of the case study partner. Following this, the findings which emerged from the interview stage are reviewed. These findings are important as they contributed towards the development of the Gap Analysis Tool and provide useful perspective when examining the Gap Analysis results.

12.4.1 Research Partner The partner is part of a global company operating in multiple countries throughout the world. The plant is approximately 30 years old with 560 employees, of which around 20% are temporary production staff. There are 12 maintenance technicians, operating over a three-shift system. A large majority of the originally installed manufacturing equipment is still in use at the site and being used daily. The plant produces two typical components for use within the automotive industry and supplies two OEM’s at the time of this investigation. The plant has held supply contracts with each OEM for a number of years but experienced some quality issues in the past. This has resulted in an ongoing tension with one particular OEM. The supply contract with a very important OEM contains an annual financial condition where costs must be reduced by 4% to 5% for the duration of the contract. This has a restrictive impact on all business functions.

12.4.2 Case Study Interviews What follows is a synopsis of the transcribed interviews which occurred at Director; Executive Manager; Middle Manager and Maintenance Operative level.

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Interviews revealed the nature of the maintenance strategy for the site was reactive, with minimal preventative maintenance activities. Technicians excelled in deploying reactive maintenance techniques but were limited in other areas. Consequently, reactive maintenance tasks occupied the majority of maintenance activities. Preventative maintenance consisted of a visual inspection of scheduled areas only. There was recognition at middle management level that the maintenance approach needed to be more proactive, requiring production staff to become maintenance active. The annual budget for maintenance activities was approximately £700,000 per annum, yet all respondents agreed the sum was inadequate for empowering maintenance development and improvement. This included training being inhibited by business wide, cost down initiatives. The age of the site and associated equipment was acknowledged by all participants as an ongoing issue, negatively affecting maintenance performance. Moreover, the consistent high loading of production machinery due to OEM demands was damaging to older equipment and resulted in frequent failures. Approximately 40% of production equipment was over 15 years old and subsequently operating beyond the recommended life cycle. Furthermore, any new equipment which was purchased was of relatively low quality. In the view of the maintenance operative, this low quality provided an additional burden to the maintenance function. The consistent purchase of equipment which was the cheapest option, led to a diverse range of machine manufacturers being deployed by production. Consequently, the variety of spare parts grew constantly and became difficult to manage. As a result, spare parts worth approximately £400,000 were stored at the plant at the time of the discussion. The range of maintenance Key Performance Indicators (KPI’s) were limited, staff identifying Original Equipment Effectiveness (OEE), break down rate and preventative maintenance task completion rate. All managers revealed concern for the accurate recording of data to inform KPI’s, which was a manual and often disputed process. Interestingly, middle management reflected on the stability and relative satisfaction with plant OEE from senior managers at group level. Apparently this facilitated a persistent under investment in maintenance. Moreover, it also demonstrated the lack of importance and urgency senior management held for improved maintenance activities. The input and influence of Senior Managers in Maintenance management appeared in conflict and contradictory at times. Three out of four respondents agreed that the maintenance strategy needed to move from being reactive, to planned and preventative. The conduit for this move would be ‘Production Led Maintenance’ (PLM). This concept was rejected at Director level, due to a lack of trust in the ability of production staff to complete any maintenance task competently. Indeed, it was insisted tasks for production staff should be restricted to simple, repetitive manufacturing operations. The use of buffer, or break glass stock was discussed at Director level and interviews revealed the primary purpose was to ensure the consistent delivery of product to the customer. Responses also indicated the level of break glass stock was

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greatly increased due to the ineffectiveness of the maintenance plan. Tellingly, the cost of held stock at that moment ran into tens of millions of euros.

12.5

Gap Analysis Results

This section begins with a radar diagram (Fig. 12.1) summarising the category scores for the case study Gap Analysis test. Subsequently, the section discusses the characteristic diagram (Fig. 12.2) representing the detailed results from the Gap Analysis test. The radar diagram in Fig. 12.1 provides an average score for each category and describes a profile across all maintenance constraints. This score allows the opportunity for further investigation and must be understood as having the ability to contain both good practice (4) and poor practice (1). Most categories are self-explanatory, though Integration may be defined as working relationships with business stakeholders.

12.5.1 Discussion The characteristic diagram in Fig. 12.2 offers extended detail of good and poor practice in each category. Categories of constraints headline each section and the

Fig. 12.1 Radar Diagram representing the profile of the case study maintenance Gap Analysis test

Fig. 12.2 A characteristic score diagram representing Gap Analysis results for the case study partner

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diagram represents the score achieved for each characteristic within the category. Areas which require attention may be noted from their position, score and colour. This maybe demonstrated by a review of ‘Skills and Training’ in Fig. 12.2, which identifies no evidence of a training plan, analysis of staff skills or effectiveness of completed training activities. The Gap Analysis tool exposed further detail surrounding the reactive maintenance strategy. Most work orders were recorded for completion time, yet there was no recording system in place for any other information. As a result, there could be no analysis completed in areas such as maintenance type, maintenance rework, or resource utilisation. Conclusively, this led to a reliance on anecdotal evidence for further planning activities. Moreover, any information recorded by the department was manual and stored within a spreadsheet. Consequently, the maintenance function could only utilise narrow and unreliable KPI data. Worryingly, the performance statistics of the department were not displayed or communicated to maintenance operatives or passing employees. The information and their method of display are artefacts and represent the culture of the department [31, 32]. This resulted in the lowest score for this category, due to this being a strong indicator of the department not placing sufficient value upon the information [28, 31, 32]. Moreover, this is in conflict with the working practice of the production department, where performance tracking is evident in several areas of the shop floor. These differences directly affect the working relationship or integration between maintenance and other departments. In addition, the test also revealed differing standards and working practices in areas such as housekeeping and levels of communication. Interviews revealed a concern regarding the quality of production machinery and this was explored by the Gap Analysis test. Results indicated the plant does not have a stores person or an established spare part control system in operation. As a result, routine and critical spare parts are missing on a regular basis. The high score established in this category from the existence of a critical spare parts list, is undermined by the effectiveness of critical parts management. An appraisal of the data from interviews and the Gap Analysis test, demonstrate that accurate management of information is a gap in the infrastructure of the organisation. The lack of an appropriate CMMS system can be seen to have a negative effect in numerous areas of the business. As well as providing the ability to accurately analyse performance, a maintenance management system would provide a platform for planning, resources and spare part management.

12.6

Summary

The age and poor working condition of some production machinery has had a damaging effect on the maintenance function. The older equipment appeared to enforce the need for constant breakdown work, subsequently draining resources. Combined with the lack of preventative work and no operator involvement in

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maintenance activities, the department is in a spiral which is preventing strategic improvements. The current leadership confusion on a progressive maintenance strategy augments this situation. Clarity at leadership level would be beneficial, including recognition of the consequences of the current purchasing strategy. This cost driven strategy has a damaging effect on the maintenance budget and spare parts management. The concern becomes more important due to customer demands, which require high levels of production loading. Furthermore, the excessive safety stock held by the plant continues to mas some of the existing maintenance problems. The operational issues which are present from the test results, such as a lack of any training plan or insufficient planning capacity may be understood by an experienced maintenance leader. What is to be considered by all maintenance stakeholders are the underlying characteristics which affect the culture of the organisation and maintenance department. This Gap Analysis test identified characteristics such as methods and frequency of communication, display and accuracy of KPI’s, as well as workshop housekeeping standards. As well as having an operational impact, these factors are visible and can alienate the department from colleagues who operate in a different way. This separation can lead to tension and difficult working relationships. As a result, the importance of visible symbols and artefacts must be recognised as having an impact on working relationships. Conclusively, this has a direct impact upon maintenance effectiveness. Communication and engagement are crucial for teamwork as well as understanding the beliefs and values of cooperating staff [30]. Committing to a maintenance strategy which encourages communication and some degree of teamwork would improve maintenance perception, working relationships and help enable a supportive working culture [31]. Moreover, addressing the site specific, process and workforce issues is crucial to improving maintenance performance from an operational and cultural perspective. This research has revealed the current maintenance department is operating with under investment, a regressive maintenance plan and poor maintenance working relationships. Conclusively, recognising and working towards solving these operational and cultural issues becomes important in addressing the challenging trading conditions within the automotive supply chain.

References 1. SMMT Motor Industry Facts 2018 [Internet]. SMMT; 2018 May, cited 2 May 2019. [Motor Industry Facts]. https://www.smmt.co.uk/reports/smmt-motor-industry-facts-2018/ 2. Thun, J-H., Druke, M., Hoenig, D.: Managing uncertainty - an empirical analysis of supply chain risk management in small and medium-sized enterprises. Int. J. Prod. Res. 49(18), 5511–5525 (2011) 3. Womack, J.P, Jones, D.T., Roos, D.: The Machine That Changed the World. New edn., 352 p. Simon & Schuster, London (2007) 4. Wireman, T.: Total Productive Maintenance. 2 edn., 224 p. Industrial Press Inc., New York (2004)

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5. Moyano‐Fuentes, J., Sacristán‐Díaz, M., Martínez‐Jurado, J.P.: Cooperation in the supply chain and lean production adoption: evidence from the Spanish automotive industry. Int. J. Oper. Prod. Manage. 32(9), 1075–1096 (2012). Sousa, R., (ed.) 6. Jacobs, F.R., Chase, R.B.: Operations & Supply Chain Management with Student OM Video DVD, 13th edn., p. 793. McGraw-Hill Higher Education, New York (2010) 7. Kelly, A.: Maintenance Strategy: Business-centred Maintenance, 276 p. Butterworth-Heinemann, Oxford (2012) 8. Murthy, D.N.P., Atrens, A., Eccleston, J.A.: Strategic maintenance management. J. Q. Maintenance Eng. 8(4), 287–305 (2002) 9. Pintelon, L., Pinjala, S.K., Vereecke, A.: Evaluating the effectiveness of maintenance strategies. J. Q. Maintenance Eng. 12(1), 7–20 (2006) 10. Taylor, D., Brunt, D., Taylor, M.R.: Manufacturing operations and supply chain management: the lean approach, 400 p. United States: Cengage Learning, Australia (2010) 11. Parida, A., Kumar, U.: Maintenance performance measurement [MPM]: issues and challenges. J. Q. Maintenance Eng. 12(3), 239–251 (2006) 12. Stenström, C., Parida, A., Kumar, U., Galar, D.: Performance indicators and terminology for value driven maintenance. J. Q. Maintenance Eng. 19(3), 222–232 (2013) 13. Muchiri, P., Pintelon, L.: Performance measurement using overall equipment effectiveness [OEE]: literature review and practical application discussion. Int. J. Prod. Res. 46(13), 3517– 3535 (2008) 14. Muchiri, P., Pintelon, L., Gelders, L., Martin, H.: Development of maintenance function performance measurement framework and indicators. Int. J. Prod. Econ. 131(1), 295–302 (2011) 15. Salonen, A., Bengtsson, M.: The potential in strategic maintenance development. J. Q. Maintenance Eng. 17(4), 337–350 (2011) 16. Faccio, M., Persona, A., Sgarbossa, F., Zanin, G.: Industrial maintenance policy development: a quantitative framework. Int. J. Prod. Econ. 147(Part A), 85–93 (2014) 17. Garg, A., Deshmukh, S.G:. Maintenance management: literature review and directions. J. Q. Maintenance Eng. 12(3), 205–238 (2006) 18. Tsang, A.: Strategic dimensions of maintenance management. J. Q. Maintenance Eng. 8(1), 7–39 (2002) 19. Swanson, L.: Linking maintenance strategies to performance. Int. J. Prod. Econ. 70(3), 237– 244 (2001) 20. Tsang, A.: A strategic approach to managing maintenance performance. J. Q. Maintenance Eng. 4(2), 87–94 (1998) 21. Wireman, T.: Benchmarking Best Practices in Maintenance Management. 2nd edn., 256 p. Industrial Press Inc., New York (2010) 22. Campbell, J.D., Reyes-Picknell, J.V.: Uptime: Strategies for Excellence in Maintenance Management, 3rd edn., p. 533. Productivity Press, Boca Raton (2015) 23. Al Turki, U.: A framework for strategic planning in maintenance. J. Q. Maintenance Eng. 17 (2), 150–162 (2011) 24. Waeyenbergh, G., Pintelon, L.: A framework for maintenance concept development. Int. J. Prod. Econ. 77(3), 299–313 (2002) 25. Wit, B.D., Meyer, R.: Strategy: An International Perspective. 5th edn., 928 p. Cengage Learning EMEA (2014) 26. Moubray, J., Network, T.A., Lanthier, J.R.P.: Reliability-Centered Maintenance. 3rd edn., 512 p. Butterworth-Heinemann Ltd, Oxford (2016) 27. Doran, D.: Rethinking the supply chain: an automotive perspective. Supply Chain Manage. Int. J. 9(1), 102–109 (2004) 28. Kumar, U., Galar, D., Parida, A., Stenström, C., Berges, L.: Maintenance performance metrics: a state‐of‐the‐art review. J. Q. Maintenance Eng. 19(3), 233–277 (2013). Kumar. U., (ed.) 29. Pakdil, F., Leonard, K.M.: The effect of organizational culture on implementing and sustaining lean processes. J. Manuf. Technol. Manage. 26(5), 725–743 (2015)

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30. Keyton, J.: Communication and Organizational Culture: A Key to Understanding Work Experiences, 2 edn., vol. 2, 232 p. Sage Publications, Inc., Los Angeles (2010) 31. Brown, A.: Organisational Culture, 2nd edn., p. 336. Financial Times/Prentice Hall, Harlow (1998) 32. Schein, E.H., Schein, P.: Organizational Culture and Leadership. 5th edn., 416 p. Wiley, Hoboken (2017) 33. Bititci, U.S., Mendibil,. K, Nudurupati, S., Garengo, P., Turner, T.: Dynamics of performance measurement and organisational culture. Int. J. Oper. Prod. Manage. 26(12) 1325–1350 (2006) 34. Hitt, M.A., Miller, C.C., Colella, A.: Organizational Behavior, 4th edn., p. 544. Wiley, Hoboken (2014) 35. Losonci, D., Kása, R., Demeter, K., Heidrich, B., Jenei, I.: The impact of shop floor culture and subculture on lean production practices. Int. J. Oper. Prod. Manage. 37(2) 205–25 (2017) 36. Smith, M.E.: Changing an organisation’s culture: correlates of success and failure. Leadersh. Organ. Dev. J. 24(5), 249–261 (2003) 37. Dixon, D., Robson, K., Baglee, D., Wheatley, A.: The role of cultural development when improving maintenance practice in the automotive supply chain. In: COMADEM 2017. University of Central Lancashire, p. 8 (2017)

Chapter 13

Feature Subset Selection Using Sparse Principal Component Analysis and Multiclass Fault Classification Using Selected Features Biswajit Sahoo

and A. R. Mohanty

Abstract Feature selection is one of the most important aspects of condition monitoring. The condition or health of a machinery is manifested in the form of changes to its signal features. Traditional approaches for feature selection involve collecting an exhaustive list of features from different domains, all of which may not be significant. The objective then is to find a subset of features that reveal the changes in a significant way. The usual approach to select important features is by applying thresholding to loading scores of variables obtained from principal component analysis (PCA). However, there is not enough study as to the effectiveness of selected features in classifying multiple faults. In this paper, we show that by applying sparse principal component analysis (SPCA) to wavelet packet features, we obtain a subset of features that are interpretable and classification accuracy obtained in multiclass classification by using only those features is encouraging. This classification accuracy is compared to that of features obtained from conventional PCA by taking top four features with maximum absolute loading scores. It is observed that, in the particular application concerned, SPCA based features perform at least as good as PCA based features. The method has been applied to a real-world bearing data set that is widely used in condition monitoring.

13.1

Introduction

Identification of faults in mechanical systems is of utmost importance in many applications. This is even more pertinent for critical components like bearings [1–3]. Over the years, several techniques have been developed for this purpose. Broadly, these techniques can be divided into two categories, viz. model-based techniques and B. Sahoo (&)
A. R. Mohanty Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India e-mail: [email protected] A. R. Mohanty e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_13

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data-driven techniques. Model-based techniques focus on formulating an accurate model of the system and thereby proceeding to fault identification. However, formulation of an exact model becomes difficult as it is susceptible to variations due to several external factors. Therefore, data-driven techniques provide a convenient alternative as they rely solely on operational data collected from the system. Fault diagnosis is done by analyzing that data. The quest for automating the whole process has led to the application of several machine learning techniques to this problem. But most machine learning algorithms don’t operate on raw data. Rather a set of relevant features form either time domain, or from frequency domain, or time-frequency domain are calculated with the hope that these features contain almost all the information of the raw data. Once features are calculated, the dataset can be represented as a set of vectors. The number of components in each vector is equal to the number of features calculated. Following this convention, the whole dataset can be represented as a data matrix with number of rows being equal to number of data points and numbers of columns being equal to the number of features. These feature vectors are fed into the algorithm which then does the necessary classification. As bearing faults generate nonstationary signals, time-frequency analysis techniques are better suited for fault diagnosis. Wavelet analysis is one of the most popular time-frequency analysis techniques that localizes both time and frequency fairly accurately [4]. Wavelet analysis has been applied successfully to solve different condition monitoring problems [5, 6]. Wavelet packet features, such as wavelet packet energy and wavelet packet entropy, have also been applied for several applications [7]. In the feature selection step, many features are selected as no single feature is capable of fully representing the information in the data. This sometimes leads to the selection of redundant features. Training machine learning algorithms with redundant features may lead to the problem of overfitting. So usually the common practice is to first reduce the dimensionality of the feature vectors and then apply classification algorithms. Principal component analysis (PCA) [8] is a popular dimensionality reduction technique that finds a lower dimensional subspace of the data matrix. Invariably, loading scores of principal components contain contributions from all features, thus making it less interpretable. In order to interpret the components, a few features are selected empirically based on their weight in principal components, i.e. features for which magnitudes of loadings scores are high [9]. This technique is known as thresholding whereby features with loading values higher than the threshold are selected as important and other features are discarded. However, this method doesn’t always select important features and becomes susceptible to redundancy if many features contribute nearly equally towards principal components. In those applications, it also becomes very difficult to interpret the principal components. Several attempts have been made to interpret principal components. One of the techniques used to interpret principal components is by rotation [10]. Rotation is applied with respect to loading matrix to obtain principal components that are interpretable. The problem with rotation is that either the uncorrelated property of

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principal components is lost or the orthogonality of loading vectors or both are lost. Another approach to force interpretability is by restricting the loading vectors to have components of only −1, 0, or 1. Yet another approach is to use thresholding, the process of setting the variables to zero whose loadings are below a threshold, but it is not that effective. Jolliffe et al. [11], introduced a method named SCoTLASS (Simplified Component Technique LASSO) to select a sparse set of features. SCoTLASS uses LASSO (Least Absolute Shrinkage and Selection Operator) that uses L1 norm penalty as a constraint to select a sparse subset but is computationally intensive. Zou et al. [12], proposed sparse principal component analysis (SPCA) where the problem of PCA is reformulated as a regression problem and by applying the sparsity constraint, a sparse loading vector is obtained. A sparse vector is a vector with only a few nonzero entries and rest of its entries are zero. SPCA finds a sparse loading vector from which it is convenient to get a subset of features that have nonzero loadings. That subset of features along with a classification algorithm, like support vector machine (SVM), can be used for classification purpose.

13.2

Theory

If p features are computed from the signal, after the computation of features, it can be represented as a vector in Rp . If features are computed from n measurements, i.e. from n different signals, the vectors thus generated can be represented as an n  p matrix, say X. Rows of the data matrix correspond to number of data vectors and columns correspond to number of features calculated. In condition monitoring applications, since data are available in abundance, usually n [ p.

13.2.1 Principal Component Analysis (PCA) Given a data matrix X, we can assume without any loss of generality that the columns of X are centred, i.e., column means are zero. Centring doesn’t affect the result of PCA as it involves only shifting of whole data without changing their relative locations. If the data matrix contains features that are measured in different units, to reduce the effect of a particular feature on the principal components, the columns of the matrix are also scaled to have unit variance. When PCA is performed on a centred matrix without scaling its columns, it is called covariance PCA. On the other hand, when PCA is performed on a mean shifted and scaled matrix, it is called correlation PCA. In the current study, only correlation PCA has been used as different time domain features are calculated in different units. The objective of PCA is to find directions that are uncorrelated, i.e., orthogonal, to each other and also maximize variance successively. The first principal component explains maximum variance followed by the second component and so on. These directions of maximum variance are obtained by taking linear combinations of features of the original matrix, i.e., columns of the original matrix. In the process

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of transforming the data, total variance of all data remains the same before and after transformation. Mathematically, the problem is to find a linear combination of columns Xa, where a is a loading vector of size ðp  1Þ such that its variance, given by 1 T T n1 a X Xa is maximized. Defining the covariance matrix as CovðX Þ ¼ S ¼

1 XT X n1

ð13:1Þ

the variance can be represented as aT Sa. As a is a representative loading vector, it can be normalized to get a well-defined solution to the maximum variance problem. It can be shown that when a is the eigenvector corresponding to the maximum eigenvalue of the covariance matrix, Xa gives the first principal component [8]. The components of vector Xa are called PC scores. Successive principal components can be found by applying successive eigenvectors of S as loading vectors to the data matrix X. The eigenvectors can be chosen to be orthogonal and a full set of n eigenvectors are guaranteed as the covariance matrix is symmetric. Matrix P, formed by the eigenvectors as its columns is orthogonal. Columns of P are arranged corresponding to decreasing eigenvalues. P is also known as orthogonal loading matrix. The columns of Y, obtained after the transformation Y ¼ XP, are called principal components with the first column representing first principal component and so on. Premultiplying both sides by 1T , where 1 is an n  1 column vector of all ones, we get 1T Y ¼ 1T XP. But 1T X ¼ 0 as X is mean shifted. Hence, this shows that the transformed Y is also mean shifted. Total variance of features before transformation is trace
of the covariance tr ðSÞ. Total
of principal  
1 matrix,
variance  1 components is tr n1 Y T Y ¼ tr n1 PT X T XP ¼ tr PT SP ¼ tr ðSPÞPT ¼ tr ðSÞ: Last argument used the fact that trace is commutative. Hence, total variance remains unchanged after transformation. The fact that the transformed features are uncorrelated can be seen from the fact that CovðY Þ ¼

1 PT XT XP ¼ PT SP n1

ð13:2Þ

Matrix PT SP is diagonal as P is the eigenvector matrix of S and its columns are chosen to be orthogonal. The diagonal entries represent the eigenvalues S that represent variances of features. Diagonal covariance matrix means that covariance between different features of Y is zero ensuring uncorrelated transformed features. The loading matrix P can also be obtained from the singular value decomposition (SVD) of the data matrix without even forming the covariance matrix. Given a real matrix X of size n  p, its SVD is given by, X ¼ URV T

ð13:3Þ

Where U is an orthogonal matrix of size n  n, R is diagonal of size n  p, and V is an orthogonal matrix of size p  p. The right singular vector matrix V is the eigenvector

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matrix of X T X ¼ ðn  1ÞCovðXÞ. Because of computational difficulty concerning calculation of eigenvectors of covariance matrix, SVD is the preferred choice to find loading matrix. Thus, using PCA it is possible to select a lower dimensional representation of data involving only first few principal components. However, the loading vectors corresponding to principal components are mostly not sparse making it difficult to select a subset of features. In such a scenario, sparse principal component analysis can be used to obtain a sparse loading vector.

13.2.2 Sparse Principal Component Analysis (SPCA) Sparse principal component analysis finds a subset of features with nonzero loadings in a computationally efficient way. It achieves sparse loading by applying constraint on the loading vector. When the sparsity constraint is removed, results of SPCA and conventional PCA are identical. SPCA is computationally efficient and is capable of handling the case where number of observations is more than number of features as well as the case where number of features is more than number of observations. The problem of PCA can be reformulated as a ridge regression problem. The problem concerns finding the ridge estimate ^ a such that arg min jjZi  Xajj22 þ kjjajj22 a

ð13:4Þ

where k is a positive constant. Zi is the ith principal component obtained from conventional PCA. jjajj is the two norm, also known as the Euclidean norm, of a. ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pq 2 j a j jja2 jj ¼ . It can be shown that the normalized estimate ^a is the ith i i¼1 jj^ajj loading vector corresponding to Zi [12]. When further L1 penalty is added to the above optimization problem we get sparse loading vectors. The optimization problem can be expressed as ^a ¼ arg min jjZi  Xa22 jj þ kjjajj22 þ k1 jjajj1 a

ð13:5Þ

Where jjajj1 is the one norm of vector a and k1 is another constant. P jjajj1 ¼ pi¼1 jai j. Minimizing one norm leads to sparse solution. Equation 13.5 is an optimization problem and its solution finds ^a. Vector ^a is an approximation to jj^ajj   ^a ith loading vector and X is an approximation to the ith principal component. jj^ajj The effect of sparsity in loading vector decreases the variance explained by the approximated principal component as compared to the original principal component with all features. So enhanced interpretability comes at a cost of loss of variance.

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13.2.3 Wavelet Packets Wavelet analysis is a time-frequency signal processing technique that localizes both time and frequency fairly accurately without sacrificing uncertainty principle. This is achieved by analyzing the signal with an analyzing function, also known as mother wavelet, which is scaled and shifted gradually to obtain both low and high frequency information. As the analyzing function has a fixed support, a compressed version of it gives accurate time information [4]. The analyzing function can be compressed and shifted in a continuous manner to obtain an accurate time-frequency representation of the signal. However, this representation is highly redundant. But it is possible to decompose and reconstruct the signal accurately by using scales that are multiples of two. Using this technique, wavelet decomposition can be achieved by passing the signal through a set of filter banks. In conventional wavelet decomposition, the signal is passed through a set of low pass and high pass filters resulting in approximation and details coefficient respectively. In the next level, approximation coefficients are further passed through a set of low pass and high pass filters resulting in next level of coefficients. This process is repeated till the end or to a certain fixed number of levels. In contrast, in wavelet packet decomposition, both approximation and detail coefficients are further passed through filter banks thus resulting in full decomposition tree. Each node, or leaf, of the last level of decomposition is known as a packet. A further down sampling operation is required to keep the length of decomposition coefficients the same as signal length. Thus, wavelet packet decomposition decomposes the signal into a set of frequency bands. It is possible to reconstruct the time domain signal of each band from the wavelet coefficients of that band and measure certain features. Let S be the reconstructed signal from one of the packets and is of length n. Wavelet packet energy (E) can be defined for that packet as E¼

n X

S2i

ð13:6Þ

i¼1

Similarly, wavelet packet entropy (En ) for that particular packet can be defined as En ¼ 

n X


 S2i log S2i

ð13:7Þ

i¼1

This can be repeated for all packets and respective features can be obtained.

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13.2.4 Support Vector Machine Support vector machine (SVM) is a classification algorithm that is suitable for multiclass classification. SVM is the generalization of support vector classifier that itself is the generalization of maximal margin classifier [13]. In a binary classification scenario, if the two classes are linearly separable, maximal margin classifier finds a hyperplane such that the orthogonal distance from the hyperplane to nearest points in each class is maximized. 


 Given a set of n labeled data points xð1Þ ; yð1Þ ; xð2Þ ; yð2Þ ; . . .; xðnÞ ; yðnÞ , it 
ðiÞ ðiÞ n can succinctly be written as x ; y , where each xðiÞ is a ðp  1Þ vector. p is i¼1 the number of features and yðiÞ is the respective label that can take, in binary classification, values from f þ 1; 1g. Maximal margin classifier finds the param

T eters b ¼ b1 ; . . .; bp , such that bT xðiÞ þ b0 belongs to the correct class for each i and exceeds certain margin ðM Þ. When two classes are not strictly separable, maximal margin classifier can still be used to produce a separating hyperplane allowing some misclassification. This is the basis of support vector classifier. Each misclassification will incur some penalty. So support vector classifier finds an optimal solution that allows some misclassification yet remaining within the penalty. The modified quadratic programming problem for support vector classifier becomes

subject to;

n P min 12 jjbjj2 þ C ni i¼1
b0 ;b  yðiÞ bT xðiÞ þ b0  1  ni ; ni  0

i ¼ 1; 2; . . .; n

ð13:8Þ

where C is a parameter that controls the maximum number of allowed misclassifications and ni are slack variables. This optimization problem can be solved by applying convex optimization techniques. When the data are not linearly separable, the principle of support vector classifier can be generalized to obtain nonlinear classification boundary. This can be achieved by using kernels. Using kernels, such as radial basis functions (RBF), the feature space is transformed to a higher dimensional space, sometimes infinite dimensional, assuming that in the higher dimensional space the data is linearly separable. If a solution is found in the higher dimensional space, it is projected back to the original feature space taking the shape of nonlinear classification boundary. Computation with kernels in higher dimensions is manageable because of the fact that kernels require only the inner products between data points. Those inner products are computed in original feature space instead of the transformed high dimensional space. Multiclass classification is handled by SVM by using either one versus one or one versus all approaches.

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Description of Data

The IMS dataset [14] provides three test to failure tests of four Rexnord ZA-2115 bearings mounted on a horizontal shaft. Rotational speed was kept constant at 2000 RPM. Sampling frequency was set at 20 kHz. Accelerometers were attached to bearing casing and data were collected till failure occurred in one of the bearings. For test one, inner race fault occurred in bearing 3 and roller defect occurred in bearing 4. In test two, outer race defect occurred in bearing 1. For this study, normal, inner race fault and rolling element fault data were taken from test 1 and outer race fault data were taken from test 2. Data taken from channel 1 from 00:04:13 to 03:44:13 on 23/10/2003 were considered normal. For inner race fault and rolling element fault, data were taken from 24/11/2003, 00:01:24 to 03:41:24 from channel 5 and channel 7 respectively. Outer race fault data were taken from channel 1 of test 2 from 18:2:2004, 00:02:39 to 03:42:39. Data for each case was segmented into smaller parts of length 1024 without any overlap between different segments. The data were divided randomly into training and test category as mentioned in Table 13.1.

13.4

Results and Discussions

Time domain data were split to eight wavelet packets. Wavelet packet features such as wavelet packet energy and wavelet packet entropy were calculated for each case using MATLAB. “Sym8” analyzing function was used to obtain wavelet coefficients. Principal component analysis was applied to obtain loading scores corresponding to first two principal components. Here correlation PCA was used as it resulted in distinct clusters in projected plane. Out of all features, a subset of features was selected using sparse principal component analysis. Multiclass classification using support vector machines was carried out first by using all features and then using only four features selected from SPCA. Accompanying feature loadings for first and second principal components for each case is tabulated and performance in classification is presented. Taking all features into account, an SVM was trained with radial basis function. The hyper parameters of SVM, the cost value and gamma, were selected form two sets (1, 5, 10, 50, 100) and (0.05, 0.5, 1, 5, 10) Table 13.1 Description of IMS dataset Normal Inner race fault Outer race fault Rolling element fault

Number of training data points

Number of test data points

300 300 300 300

150 150 150 150

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Table 13.2 Classification accuracy taking all features into account for IMS bearing data

155

Fault type

Wavelet packet energy Training Test

Wavelet packet entropy Training Test

Inner Outer Rolling element Normal

100 100 100 100

100 100 100 100

100 100 100 100

100 100 100 100

Fig. 13.1 Projection of IMS bearing data on the plane of first two principal components a using wavelet packet energy features b using wavelet packet entropy features (online version in colour)

Table 13.3 Loading scores of conventional PCA and SPCA with four features for first two principal components for IMS data

Features Packet 1 Packet 2 Packet 3 Packet 4 Packet 5 Packet 6 Packet 7 Packet 8 Cumulative variance explained

Wavelet packet energy PCA SPCA with 4 features PC1 PC2 PC1 PC2

Wavelet packet entropy PCA SPCA with 4 features PC1 PC2 PC1 PC2

−0.273 −0.339 −0.338 −0.249 −0.371 −0.37 −0.439 −0.408 0.590

−0.266 −0.323 −0.256 −0.33 −0.42 −0.441 −0.371 −0.378 0.581

0.447 −0.448 −0.291 −0.589 0.035 0.335 0.156 0.171 0.796

−0.312 0 0 0 0 −0.535 −0.689 −0.377 0.374

0 −0.699 −0.225 −0.679 0 0 −0.001 0 0.592

0.442 −0.487 −0.574 −0.286 0.163 0.15 0.067 0.32 0.793

−0.237 0 0 0 −0.446 −0.653 0 −0.565 0.375

0 −0.69 −0.662 −0.29 0 0 −0.044 0 0.604

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Table 13.4 Classification accuracy taking four features into account for IMS data Wavelet packet energy SPCA features

Fault type

Training

Test

Top 4 PCA features selected by decreasing order of magnitude Training Test

Inner Outer Rolling element Normal

100 99.67 100 100

100 100 100 100

100 99.67 91.67 98.33

100 100 89.33 98

Wavelet packet entropy SPCA features

Training

Test

Top 4 PCA features selected by decreasing order of magnitude Training Test

100 99 100 100

100 100 100 100

100 100 88 97.67

100 100 86 97.33

Fig. 13.2 Frequency of each variable selected when SPCA is run on 100 different samples for IMS bearing data a using wavelet packet energy b using wavelet packet entropy

respectively by using cross-validation. The best hyper parameters thus chosen are used to train models with a subset of features. Statistical computing software ‘R’ [15] was used to find sparse principal components using package ‘elasticnet’ [16] and to perform support vector machine classification using package ‘e1071’ [17]. Accompanying codes to reproduce results of this paper can be found in [18]. When all features are taken, accuracy is perfect as shown in Table 13.2. Projection of data points on first two principal directions is shown in Fig. 13.1. It can be seen that different types of faults form distinct clusters. Loading scores corresponding to first two principal components of both PCA and SPCA are shown in Table 13.3. All the loading scores of conventional PCA are nonzero and some of those are close to each other. Thus selection of a subset of features by using conventional PCA becomes difficult. In contrast, in SPCA we get only four features with nonzero loadings and rest of the features have zero loading. Thus important

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features become interpretable in SPCA. From Table 13.4, it can be seen that accuracy remains same even when only four SPCA features are used for classification. However, when four features with highest magnitudes of loading score form conventional PCA are used, the accuracy decreases considerably for roller element fault. Figure 13.2 shows the number of times each feature is selected by SPCA when a different training set is used. This experiment was carried out 100 times, each time selecting 300 data points randomly from a set of 450 points and performing SPCA on it. It can be seen empirically than in every case, SPCA selects same set of features irrespective of the randomly chosen training data.

13.5

Conclusions

When interpretation of principal components is of importance, SPCA can be used to select a subset of features thereby facilitating the interpretation of principal components. SPCA based features are better at classifying faults than those obtained from PCA with maximum absolute value loading scores. However, this interpretability comes at a cost of a reduction in explained variance. The uncorrelated property of principal components and orthogonality of loading vectors are also sacrificed. But as has been demonstrated in this study, classification performance with selection of only four features is at par with the performance when all features are taken. This feature of SPCA may help in reducing overfitting of classifiers. As the results of SPCA taking all features into account are identical to the conventional PCA, it can be used to find a feature subset that strikes a right balance between an optimum number of features, percentage of variance explained, and classification accuracy.

References 1. Randall, R.B., Antoni, J.: Rolling element bearing diagnostics—a tutorial. Mech. Syst. Signal Process. 25, 485–520 (2011). https://doi.org/10.1016/j.ymssp.2010.07.017 2. Randall, R.B.: Vibration-Based Condition Monitoring: Industrial, Aerospace, and Automotive Applications. Wiley, Chichester (2011) 3. Mohanty, A.R.: Machinery Condition Monitoring: Principles and Practices. CRC Press, Boca Raton (2018) 4. Strang, G., Nguyen, T.: Wavelets and Filter Banks, Rev edn. Wellesley-Cambridge Press, Wellesley (1997) 5. Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process. 18, 199–221 (2004). https://doi.org/10.1016/S0888-3270(03)00075-X 6. Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Sig. Process. 96, 1–15 (2014). https://doi.org/10.1016/j.sigpro.2013.04.015 7. Nikolaou, N.G., Antoniadis, I.A.: Rolling element bearing fault diagnosis using wavelet packets. NDT E Int. 35, 197–205 (2002). https://doi.org/10.1016/S0963-8695(01)00044-5

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8. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002) 9. Malhi, A., Gao, R.X.: PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Meas. 53, 1517–1525 (2004) 10. Vines, S.K.: Simple principal components. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 49, 441–451 (2000). https://doi.org/10.1111/1467-9876.00204 11. Jolliffe, I.T., Trendafilov, N.T., Uddin, M.: A modified principal component technique based on the LASSO. J. Comput. Graph. Stat. 12, 531–547 (2003). https://doi.org/10.1198/ 1061860032148 12. Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. J. Comput. Graph. Stat. 15, 265–286 (2006). https://doi.org/10.1198/106186006X113430 13. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009) 14. These data come from National Aeronautics and Space Administration Website 15. R Core Team R: A Language and Environment for Statistical Computing (2018) 16. Zou, H., Hastie, T.: elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA. R package version 1.1 (2012) 17. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F.: e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.6-8 (2017) 18. Sahoo, B.: https://github.com/biswajitsahoo1111/spca_comadem_codes. Accessed 1 Jul 2019 19. Bartkowiak, A., Zimroz, R.: Sparse PCA for gearbox diagnostics. In: 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 25–31 (2011)

Chapter 14

Vibration-Based Detection of Wheel Flat on a High-Speed Train Ruichen Wang , David Crosbee , Adam Beven , Zhiwei Wang , and Dong Zhen

Abstract Wheel flat is not only commonly unavoidable surface damage in railway wheels, it can result in possible damage and deterioration incurring high risk of running safety and high maintenance costs. Wheel flat is therefore necessary to be detected at an early stage to minimise safety hazard and maintenance work. This study explores the capacity of the vibration-based detection for high-speed train wheel flatness. A more realistic vehicle-track coupling dynamic model (a dynamic model of vehicle systems of 94 degrees of freedom with wheel flat) considering the dynamic factors of traction transmission, gear transmission and the track geometry irregularities, is established to calculate the dynamic responses of axlebox. In this paper, the proposed method is focus on processing the axle box vertical vibration caused by wheel flat in conventional time and frequency domain, as well as the envelope analysis with a band pass filter. Results demonstrate that the wheel flat can be successfully detected in a more realistic vehicle model, provide an efficient way to the wheel flat detection.

R. Wang (&)
D. Crosbee
A. Beven Institute of Railway Research, University of Huddersfield, Huddersfield, UK e-mail: [email protected] D. Crosbee e-mail: [email protected] A. Beven e-mail: [email protected] Z. Wang State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China e-mail: [email protected] D. Zhen School of Mechanical Engineering, Hebei University of Technology, Tianjin, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_14

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Wheel flat, as a common surface defect, is randomly occurs in railway wheels [1]. As a result, it can cause the periodic collisions between wheel and rail (wheel-rail impact) during the vehicle operation, and the coupled vibration from the entire coupled vehicle-track system is also an unavoidable factor. This increases of risks on wheel-rail damage, running safety of ride, therefore, establishing an early stage method of wheel flat detecting and a regular wheel surface monitoring is essential to reduce maintenance cost and operating risk etc., early detection of wheel flat also attracts more research interests in recent. Researches and studies starting with establishing vehicle-track coupling dynamics model, then analysed the dynamic response at different operating speeds and different flat sizes (width) through time domain or frequency domain analysis [2–5]. Pieringer et al. [6] proposed a fast wheel-rail interaction time domain model. In this study, wheel and rail were established as linear systems using impulse response functions which is capable of calculating the vertical contact force due to the small-scale roughness between wheel and rail. A real-time wheel defect monitoring system based on fiber Bragg grating sensor has recently been researched by Wei’s team [7]. The sensor measures the rail strain response of the wheel and rail interaction, and the frequency component alone is able to reflect the quality of the interaction. Liang et al. [8] performed wheel flat and rail surface detection on a 1:5 roller test rig, and three commonly used the frequency domain analysis techniques were applied to process the measured data to find the existence of defects. Both acceleration and acoustic signals were compared and identified which can be considered as an effective method. Li et al. [9] proposed a frequency domain acceleration analysis method, which can improve the detection of rolling noise on the wheel flat and carry out experimental verification on the full-scale roller test rig. Li’s latest approach [10] was to use the axlebox vibration signal processing. In his latest study, a wheel flat monitoring algorithm based on adaptive multi-scale morphological filtering (AMMF) was proposed. Both simulations and experimental studies have identified and validated the results. However, this method only focused on Chinese vehicle of electric multiple units at speeds of 100 km/h and 150 km/h. A better approach for operators and maintainers is applicable to all types of vehicles, especially high-speed train. Nowakowski et al. [11] presented a method based on the vibration signal processing in the time and frequency domain which is possible to detect the wheel flat during tram pass-by. The Hilbert transform was used across all research analysis. The results show that the detecting efficiency of the wheel spot is very high and has been verified in practical applications. The aim of this paper is to present a preliminary investigation of the application of envelop analysis to implement vibration-based wheel flat detection for a high-speed train. In this section, a brief description of fault detection techniques of wheel flat is presented. The rest of the paper is organised as follows. The development of the coupled vehicle-track system including track system and

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traction-gear transmission system is introduced. Wheel flat and track irregularities are also briefly described. Section 14.3 apply the conventional time domain and frequency domain, and envelope analysis to wheel flat detection in developed vehicle. Conclusion are given in Sect. 14.4.

14.2

Vehicle-Track Coupled Dynamics Model

Figure 14.1 and 14.2 demonstrate the three-dimensional model of the coupled vehicle-track system with the track system and traction and gear transmission systems. A novel dynamic model is developed on the basis of the conventional vehicle–track coupled dynamics model, and traction-gear system dynamics are considered as the key novelty of the development of the latest integrated vehicle-track system. The coupled vehicle-track dynamic model is classified into three main parts, including, the vehicle dynamic system, the track system, an improved traction-gear transmission system. The vehicle system is created by the multibody system software SIMPACK [12] which consists of carbody, bogies, wheelsets, gearboxes, and traction motors (see details in Figs. 14.1 and 14.2) as its major components. Additionally, two secondary suspensions are applied to support the carbody on each side of bogie frame, and wheelsets are connected with the bogie frame by primary suspensions. The traction motor, gearbox housing, gearwheel, pinion, four-type bearings and a helical gear pair are carefully studied and modelled into the traction-gear transmission systems.

Fig. 14.1 Vehicle–track coupled dynamics model in elevation views

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Fig. 14.2 Vehicle–track coupled dynamic model, planform and side view [13]

To obtain a more comprehensive coupled vehicle-track system, 19 rigid bodies has been involved in the system and a total of 94 degrees of freedom (DOF) system is established with consideration of the pinion, gearwheel, gearbox housing, and

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traction motor. In traction-gear transmission systems, key components modelled as rigid bodies along with 6 degrees of freedoms for each (6 DOFs: longitudinal (X), lateral (Y), vertical (Z), roll (u), pitch (b) and yaw (w) motions) [13]. The details of the vehicle components and expressions are presented in Fig. 14.2 (Table 14.1).

14.2.1 Track Model The study is carried out on a typical slab track system consisting of rails, rail pads, slabs and subgrades, as shown in Figs. 14.1 and 14.2. A more comprehensive rail model is created based on the theory of the Euler–Bernoulli beams in three-motions, vertical, lateral, and torsional, in order to bring up rail irregularities. Three-dimensional slabs are applied to support rails, and it modelled with a regular elastic rectangular plates on viscoelastic foundation. Since the lateral bending stiffness of the slab is extremely large, the lateral vibration of the slab is set as rigid. In addition, the modal superposition method is used in the calculations of slab-track model. There is a good literature in [14], which is not introduced in this paper.

14.2.2 Traction-Gear Transmission System In Fig. 14.2, a simplified model of the traction dynamics for a typical high-speed train is introduced. Traction drives can be described as follows: Mainly, the traction model transmits the traction torque of the motor rotor to the wheel pair to drive the vehicle forward. The traction motor is coupled to the bogie frame by spring damper elements. During the transmission process, the power of the traction motor to the pinion is then transmitted by a flexible coupling that is modelled as a torsion spring damping element. The structural characteristics and working mechanism of the traction drive system are modelled as vibration-based systems. After that, the power is transmitted through the torsional vibration of the gear transmission.

Table 14.1 Key components and motions of the vehicle model Key components

Longitudinal

Lateral

Vertical

Roll

Yaw

Pitch

Carbody (Only one) bogie (i = 1, 2) Traction motor (i = 1– 4) gearbox (i = 1– 4)

Xc Xbi Xmi Xghi

Yc Ybi Ymi Yghi

Zc Zbi Zmi Zghi

/c /bi – –

wc wbi – –

bc bbi bmi bghi

pinion (i = 1– 4)

Xpi

Ypi

Zpi

/pi

–

bpi

wheelset (i = 1– 4)

Xwi

Ywi

Zwi

/wi

wwi

bwi

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A gear bearing system and a gearbox housing are created in the gear dynamic model as major components. Figure 14.2 shows the gear-bearing model of a high-speed train, consisting of helical gear pairs and four-type of bearing (bearing No. 1–5). Pinion and gearwheel are employed in the model as rigid disks with a central moment of inertia. Both translational and rotational degrees of freedom are applied on the discs to reflect more realistic performance. The wheelset axle is mounted directly through the gearwheel, and the gearbox is mounted through two tapered roller bearings (No. 1&2). The inner ring of bearings No. 1&2 is mounted on the axle, in the meantime, the outer ring is mounted on the gearbox housing. The pinion is attached to the gearbox housing by three-type bearings: the cylindrical roller bearing (No. 4) on the left of pinion bearing No. 3&4 and the four-point contact bearing (No. 5) on the right of pinion bearing No. 3–5 and the outer ring of pinion bearing No. 3–5 are connected to the pinion shaft and the gearbox housing, respectively.

14.2.3 Wheel Flat and Track Irregularity In this study, a wheel flat with 10 mm width (L) is targeted to predict an ideal fault of the leading front left wheel, as shown in Fig. 14.3. It can be assumed that the wheel flat width and the wheel radius are constant, therefore, the depth of the wheel flat can be found in Eq. (14.1) [3]: D¼

L2 16r

ð14:1Þ

where L is the wheel flat width; r is the wheel radius; and D is the wheel flat depth. As mentioned, track geometry irregularity has a significant impact on axlebox vibration responses on a typical high speed train. In the following simulation, the measured vertical and lateral rail-related irregularities are applied on the left and right rails in this study, as shown in Fig. 14.4.

Fig. 14.3 A simplified view of railway wheel with ideal fresh flat

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Simulation, Analysis and Discussion

As motioned above, the track excitation and wheel flat are adopted to vehicle model. Meanwhile, the traction torque characteristic of a typical high speed train is also considered and applied to the simulation to convert the traction torque from motor to rotor. A high-speed train is generally defined as one that is able to run at speeds of at 200 km/h (124 mph) and above [15]; Therefore, the operating speed of the dynamic simulations is set between 200 km/h and 350 km/h with an interval of 50 km/h. The vertical vibration of the axlebox (leading bogie, leading wheelset) will be measured to detect the influences of the targeted wheel flat for all cases.

14.3.1 Conventional Time Domain and Frequency Domain Analysis The conventional time domain and frequency domain simulated results are in Fig. 14.5(a)–(h) respectively. The time domain results show that the faster running speed of the vehicle can effectively reflect the influences of the wheel flat at the same operating conditions and as seen in Fig. 14.5(a)(c)(e)(g), but it is difficult to characterise the existing fault (wheel flat) using general time domain analysis. Additionally, the trending amplitudes of the vibration is also varying at random, therefore, it is making difficult to cope with the changing external conditions. Figure 14.5(b)(d)(f)(h) shows the axlebox vibration response spectrum at different speeds. It can be seen that wheel flat can significantly excite the axlebox frequency response at high running speeds, but simple spectrum analysis is also not a suitable method due to it is less fault detect capability. To further deal with the

Fig. 14.4 The track irregularities used to railway line in the lateral and vertical directions

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Fig. 14.5 Time domain and frequency spectrum of vibration signals at various speeds

14.3.2 Envelope Analysis To improve vibration-based detection technique for a high-speed train with wheel flat, envelope analysis is presented and used to extract the modulated signal from an amplitude-modulated signal. Envelope analysis can be split into 3 key stages; first, signal filtering with a band pass filter; second, envelope extraction of the filtered signal using Hilbert transform; third, frequency spectrum extraction of the enveloped signal using fast Fourier transform (FFT) [16]. Hilbert transform which is used to extract the filtered vibration signal can be expressed as:

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xðtÞ ¼

1 1 Z xð sÞ ds p 1 t  s

167

ð14:2Þ

The critical role of Hilbert transform is to simplify the convolution of input signal xðtÞ through generalised impulse function pt1 . The sensor of the axlebox measures a series of vibration responses primarily as a reflection of wheel flat influences. Envelope analysis is therefore used to cope with the vibration responses in the following study to extract recurring impacts at the characteristic frequencies. Envelope analysis is then applied to analyse the vibration signal, and the relevant normalised envelope spectrum is show in Fig. 14.6(a)–(e). Comparing to the results of time domain and frequency domain in Sect. 3.1, it is obvious that the envelope analysis results show the characteristics frequencies and the harmonic feature are effectively detected for a high-speed train with wheel flat. The characteristic frequencies for each running speed of the train are also show at the bottom of Fig. 14.6(e).

Fig. 14.6 Filtered envelope analysis of vibration signals

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Conclusion

A developed coupled vehicle-track dynamics model considering the effect of the traction-gear transmission system and the track system was created for a high-speed train. According to the comprehensive vehicle model, this study concentrates on the vibration-based detection for high-speed train wheel flatness. Firstly, conventional signal processing technique, time domain and frequency domain, were used to process the vertical vibration signal of axlebox, but it is difficult to characterise wheel flat. Further analysis was therefore carried out and proposed the use of envelop analysis for fault detection on axlebox vibration signal caused by wheel flat. The results show that, envelope analysis can detect and identify wheel flat with high efficiency and reliability. The characteristic frequencies and its harmonics can be clearly found by using the FFT of the enveloped signal. Acknowledgements The authors would like to thank the Institute of Railway Research (IRR), and the Centre for Efficiency and Performance Engineering (CEPE) at The University of Huddersfield and the State key laboratory of Traction power at Southwest Jiaotong University for the technical supports.

References 1. Remennikov, A.M., Kaewunruen, S.: A review of loading conditions for railway track structures due to train and track vertical interaction. Struct. Control Health Monit. 15, 207– 234 (2008). https://doi.org/10.1002/stc.227 2. Bracciali, A., Cascini, G.: Detection of corrugation and wheelflats of railway wheels using energy and cepstrum analysis of rail acceleration. Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit 211, 109–116 (1997). https://doi.org/10.1243/0954409971530950 3. Zhai, W.M., Wang, Q.C., Lu, Z.W., Wu, X.S.: Dynamic effects of vehicles on tracks in the case of raising train speeds. Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit 215, 125–135 (2001). https://doi.org/10.1243/0954409011531459 4. Wu, T.X., Thompson, D.J.: A hybrid model for the noise generation due to railway wheel flats. J. Sound Vib. 251, 115–139 (2002). https://doi.org/10.1006/jsvi.2001.3980 5. Madejski, J.: Automatic detection of flats on the rolling stock wheels 6. Pieringer, A., Kropp, W.: A fast time-domain model for wheel/rail interaction demonstrated for the case of impact forces caused by wheel flats. In: 7th European Conference on Noise Control 2008, EURONOISE 2008, Paris, France; 29 June 2008 through 4 July 2008, pp. 2643–2648 (2008) 7. Wei, C., Xin, Q., Chung, W.H., Liu, S., Tam, H., Ho, S.L.: Real-time train wheel condition monitoring by fiber bragg grating sensors. Int. J. Distrib. Sens. Netw. 8, 409048 (2012). https://doi.org/10.1155/2012/409048 8. Liang, B., Iwnicki, S.D., Zhao, Y., Crosbee, D.: Railway wheel-flat and rail surface defect modelling and analysis by time–frequency techniques. Vehicle Syst. Dyn. 51, 1403–1421 (2013). https://doi.org/10.1080/00423114.2013.804192 9. Li, Y., Liu, J., Wang, Y.: Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition. https://www.hindawi.com/journals/sv/2016/4879283/

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10. Li, Y., Zuo, M.J., Lin, J., Liu, J.: Fault detection method for railway wheel flat using an adaptive multiscale morphological filter. Mech. Syst. Signal Process. 84, 642–658 (2017). https://doi.org/10.1016/j.ymssp.2016.07.009 11. Nowakowski, T., Komorski, P., Szymański, G.M., Tomaszewski, F.: Wheel-flat detection on trams using envelope analysis with Hilbert transform. Latin Am. J. Solids Struct. 16 (2019). https://doi.org/10.1590/1679-78255010 12. GmbH, S.: Multi-body simulation. SIMPACK MBS Software. http://www.simpack.com/ 13. Wang, Z., Mei, G., Xiong, Q., Yin, Z., Zhang, W.: Motor car–track spatial coupled dynamics model of a high-speed train with traction transmission systems. Mech. Mach. Theory 137, 386–403 (2019). https://doi.org/10.1016/j.mechmachtheory.2019.03.032 14. Zhai, W., Wang, K., Cai, C.: Fundamentals of vehicle–track coupled dynamics. Vehicle Syst. Dyn. 47, 1349–1376 (2009). https://doi.org/10.1080/00423110802621561 15. Zicha, J.H.: High-speed rail track design. J. Transp. Eng. 115, 68–83 (1989). https://doi.org/ 10.1061/(ASCE)0733-947X(1989)115:1(68) 16. Tse, P.W., Peng, Y.H., Yam, R.: Wavelet analysis and envelope detection for rolling element bearing fault diagnosis—their effectiveness and flexibilities. J. Vib. Acoust. 123, 303–310 (2001). https://doi.org/10.1115/1.1379745

Chapter 15

Piezoelectric Energy Harvesting System to Detect Winding Deformation in Power Transformers Guillermo Robles , Mariano Febbo , Sebastián P. Machado , and Belén García Abstract One common use of energy harvesting systems is the installation on applications where the access to conventional sources of energy is difficult due to availability, space constraints, environmental hazards or sealed equipment. In this work, we propose an alternative use of an energy harvesting system based on a piezoelectric that takes the vibration of a transformer tank due to winding deformations and hence helps to monitor the condition of the equipment. The system consists on a cantilever piezoelectric beam with a mass tuned to the resonant frequency of the vibration. The output of the piezoelectric is connected to a quadrupler, a low-drop regulator and a capacitive storage. The harvested voltage is planned to supply a low power microprocessor that detects changes in the vibration measurements to determine an abnormal behavior of the transformer. This work introduces the causes of abnormal vibration of transformers, describes the installation of the piezoelectric on a model that generates the same acceleration as the vibration of a transformer tank and studies the capability of charging capacitors to determine the feasibility of the method.

G. Robles (&)
B. García Department of Electrical Engineering, Universidad Carlos III de Madrid, Avda. Universidad, 30, Leganés, Madrid, Spain e-mail: [email protected] B. García e-mail: [email protected] M. Febbo Instituto de Física del Sur (IFISUR), Universidad Nacional del Sur (UNS), CONICET, Avenida Alem 1253 CP 8000 Bahía Blanca, Argentina e-mail: [email protected] S. P. Machado Grupo de Investigación en Multifísica Aplicada (GIMAP), CONICET y Universidad Tecnológica Nacional Facultad Regional Bahía Blanca, 11 de abril 461, B8000 Bahía Blanca, Argentina e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_15

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Over the last two decades, several authors have proposed the evaluation of the transformers health by means of vibration analysis [1–3]. Although transformers are static machines, the effect of electrodynamic and magnetostrictive forces produces vibrations. These vibrations, which have their origin on the windings and the core, are transmitted to the transformer tank where they can be measured. The analysis of the vibration patterns recorded on the transformer tank can help to detect changes of geometry on the transformer such as deformations of the windings or loose of clamping pressure of the windings or the core. Additionally, some works propose the application of vibration analysis to the detection of faults in the transformer on-load tap changer [4]. Although the measuring techniques proposed in this work could be either applied to the analysis of the vibrations of the main tank or to the analysis of the vibrations of the on-load tap changer, the main frequencies and vibration patterns of both elements are different and require a separate analysis. Thus, in this work we will focus on the application of the method to the analysis of the vibrations of the main tank and the detection of geometrical changes in the transformer active part. The main idea of this work is to test the feasibility of attaching an energy harvesting system based on a piezoelectric beam to store electrical energy in a capacitor and supply it to an acquisition system based on a low power microprocessor and the piezoelectric beam itself. The piezoelectric effect is the capability of certain materials to polarize when subjected to mechanical deformation [5, 6] and hence create an electrical field that would eventually move charges. Piezoelectricity was discovered in the late XIX century though it wasn’t until the 1950s that a major breakthrough brought new materials such as lead zirconate titanate (PZT) and barium titanate that boosted the interest in energy harvesting [7]. New materials with improved characteristics have been designed since then improving their performance and applicability. In the last decade, the interest in piezoelectric energy harvesting has increased further with the massive deployment of connected devices to the Internet of Things. Our proposal can be included in this field with the final goal of having an inexpensive device to monitor power transformers.

15.2

Transformer Vibrations

One of the main difficulties to monitor vibrations in a transformer is related with the unfeasibility of installing sensors inside the transformer; for that reason, it is a common practice to measure the vibrations on the transformer tank instead. Tanks vibrations are transmitted from the transformer windings and core through the insulating oil and the support elements. However, it has been shown that a good correlation can be obtained between the internal vibrations and the vibrations measured in some regions of the tank [2, 8]. For the interpretation of the measurements, it

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is important to analyse which are the main sources of the vibrations inside a transformer, which are the typical vibration patterns as a function of the transformers operating conditions and how these patterns would be modified in case of a geometry change. There are two main sources of internal vibration in a transformer: – Winding vibrations, which are due to the electrodynamic forces caused by the interaction between the current carried by the windings and the leakage flux. These forces have components in axial and radial directions. Axial forces tend to compress the winding vertically, while radial forces, tend to compress the internal winding and to expand the external one (as currents in both windings flow in opposite directions). Electrodynamic forces and winding vibrations are proportional to the current squared and, thus the main harmonic component of the winding vibrations is 100 Hz. – Core vibrations, which are caused by magnetostriction and magnetic forces. Magnetostriction is a property of the magnetic materials that produces small variations on their dimensions when subjected to a magnetic field. Although magnetostriction is a non-linear phenomena, the elongation is approximately proportional to the squared flux density on the magnetic material which on its turn is proportional to the primary voltage of the transformer. The vibrations of the core have components in longitudinal and axial directions; it should be considered that the core is constituted by sheets of magnetic material with what causes irregularities in the flux distribution and the apparition of a vibration signal with several vibration harmonics. In a previous work a study was carried out to investigate how the internal vibrations are transmitted to the transformer tank, proving that the vibrations generated inside of the transformer can be measured at the tank, although the correlation between the internal and external vibrations depends strongly on the chosen measuring point [8]. A transformer of 1500 kVA was monitored with fourteen accelerometers and the internal and external vibrations of the transformer were measured under a variety of transformer operating conditions. Figure 15.1a shows the spectrum of the vibrations measured at the bottom part of the tank when the transformer works on no load conditions, for different applied voltages. Under no load conditions the current carried by the windings is very low and then the measured vibrations have been originated on the core. As can be seen the vibration level rises with the applied voltage, as expected. Figure 15.1b shows the vibrations measured at the same point of the tank when variable load is applied to the transformer. As can be seen, the vibration level is much higher in this case, as the measured vibration is the sum of the vibrations of the core and the windings. The main harmonic of the vibration signal is 100 Hz and that it rises with the applied load. In the same work a deformation was induced in the winding of the transformer under test. Several spacers on one of the windings were removed simulating a winding’s bending. The change in the vibrations can be seen in Fig. 15.2; as it is shown, the magnitude of the main harmonic of the vibrations changes significantly.

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Fig. 15.1 Vibrations in a transformer tank in healthy conditions

3

Fig. 15.2 Vibrations measured at the bottom of the tank after simulating a winding bending

Normal conditio n Induced winding deformation

2

Vibration (m/s )

2.5 2 1.5 1 0.5 0

0

200

400

600

800

Frequency (Hz)

15.3

Piezoelectric Model and Setup

This section introduces the design of the vibration energy harvesting device and the experimental setup in order to test the analytical predictions. The vibration energy harvesting device comprises a piezoelectric beam (MIDE S118-J1SS1808YB model) bonded over a stainless steel beam in a cantilever boundary condition. To tune the system to the main harmonic component of the winding vibrations at 100 Hz (as introduced in Sect. 15.2) a tip mass of variable value is introduced at the end of the cantilever system. The approximate value of the natural frequency of the whole system can approximate be calculated as [9]

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sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3kef f x¼ L3 ð0:24mLT þ MT Þ

175

ð15:1Þ

where keff is the effective flexural rigidity of the whole system which considers the flexural rigidity of the piezoelectric beam and the stainless steel beam as can be given for example in [10], m is the mass per unit length of the whole system, LT is the total length and MT is the tip mass. Equation 15.1 gives a value of 98.19 Hz which is very approximate to the experimental value obtained from experiments, 98.77 Hz, in a short circuit condition, very close to the 100 Hz of the winding vibrations. Figure 15.3 shows the experimental setup to evaluate the dynamic behavior of the piezoelectric energy harvester. The prototype is excited using an electrodynamic shaker whereas the input signal is generated by a Rigol DG4062 wave generator. Then, the signal is amplified using a home-built amplifier and the base acceleration signal is measured using a PCB accelerometer with a sensitivity of 98.7 mV/g (model 352C68). Voltage signals are acquired via a National Instruments NI9230 data acquisition module (with an input impedance of 326 kΩ) and post-processed by a Matlab code. The materials properties of the piezoelectric material (MIDE S118-J1SS-1808YB model) can be found in [11], and that of the stainless steel beam and tip mass MT are given in Table 15.1.

Fig. 15.3 Experimental setup for the piezoelectric vibrating beam

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Table 15.1 Materials constants of the stainless steel beam and tip mass

Stainless steel beam

Value

E Density Length Width Thickness Tip mass MT

200109 Pa 7850 kg/m3 68.8 mm 24.6 mm 0.7 mm 2.84 gr

Fig. 15.4 Output voltage obtained with the piezoelectric versus acceleration

The piezoelectric beam is then subjected to different base accelerations ranging from 0.02 to 0.2 g (where g is the acceleration of gravity, 9.8 m/s2) at a frequency of 100.4 Hz and the output voltage is registered to characterize the energy harvesting system. The load resistance is set R = 326 kΩ which is the input impedance of NI acquisition module.

15.4

Signal Conditioning and Energy Storage

The maximum peak voltage at the output of the piezoelectric has been plotted versus the acceleration of the sample in Fig. 15.4 where it can be seen that the relationship can be considered linear almost up to the maximum value of acceleration. This Figure can help to set the sensitivity of the system to store energy. Considering that there must be more than 3.3 V at the input of the low-dropout (LDO) regulator, it would be necessary to include a rectifying and multiplying stage between the piezoelectric and the LDO. Figure 15.5 shows the schematic of a voltage quadrupler which also rectifies the output of the piezoelectric. The forward voltage of the diodes is critical to have the possibility of reaching the low levels of acceleration. Therefore, two double diodes 1SS384 with a forward voltage as low as VF = 0.18 V for a current of IF = 1 mA have been chosen. The current through the diodes will be even lower than 1 mA since the piezoelectric is a high impedance source. Figure 15.6 shows the output of the quadrupler as well as other intermediate voltages when the input is a sinusoidal source 1 V in amplitude and 100 Hz simulated with LTSpice with the actual models of the diodes. It can be observed that

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the output when the transient is over 3.45 in average V with a small ripple which is precisely the input times four minus the voltage drop in the diodes. The blue signal corresponds to the double of the input whereas the red and dark green signals correspond to nodes N001 and N003 in Fig. 15.5. The next simulation includes the low-dropout regulator at the output of the quadrupler, Fig. 15.7. We have chosen an TPS78333 because it draws very small quiescent current of only 500 nA and a drop-out of 130 mV at 25◦ C and 150 mA. The output is 3.3 V as long as the input is larger than that in 130 mV. Figure 15.8 shows the input of the LDO in blue and the output in green for a source 1 V in amplitude and 100 Hz. It can be seen three clear time sections: the first one up to 10 ms is the transient of the quadrupler until its output reaches 1.2 V, then the enable signal of the LDO initializes it and starts drawing current. This stage finishes when the output levels with the input and the current draw is reduced, then, the input can continue to raise until it reaches its top value at 3.25 V and the output at 3.1 V. The input source simulating the piezoelectric is again 1 V peak and 100 Hz. The capacitors of the quadrupler are 100 nF to minimize the current draw at that frequency ensuring that the output is close to 3.3 V. The input impedance of two 100 nF capacitors in series at 100 Hz is |Z| = 1/ Cx = 1/50  10−92p100 = 31.8 kΩ, which is sufficiently high. Under these circumstances, the peak current is below 100 microamperes while the LDO does not follow the input, and below 60 lA in steady state. The simulation shows that the output can reach the maximum value in less than 2 s for a capacitor at the output of the LDO of 1µF. This capacitance is the minimum necessary to have a stable voltage at the output of the regulator. If we are going to supply energy to a microprocessor this capacitance would be larger and the charging time would increase accordingly. We have also checked the performance of the circuit for voltages of the piezoelectric below 1 V and found that this is precisely the minimum value for the equivalent source, otherwise, the output of the regulator will not reach a value high enough to drive a microprocessor. Alternatively, we can use another regulator from the same family to have an output of 1.8 V that can be used with an STM32L476 ARM microprocessor. In this case, the input of the LDO cannot be below 2.2 V. This discussion sets the sensitivity of the harvester. If we assume that the minimum voltage of the piezoelectric is 1 V, from Fig. 15.4 we can see that the minimum acceleration we can detect to have an output of 3.25 V is 0.11 g. This acceleration in the surface of the transformer tank is a symptom of problems in the winding so we will continue with the test of the harvesting circuit in the next section.

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Fig. 15.5 Schematic of a quadrupler in LTSpice

Fig. 15.6 Voltages in a quadrupler. V(n002) is the voltage at the output, V(n004) is the doubled voltage and V(n001) and V(n003) are voltages at the ends of the first capacitance in the positive cycle

Fig. 15.7 Schematic of a quadrupler in LTSpice with a low-drop regulator

15.5

Experimental Results

The circuit with the quadrupler and the LDO was manufactured in a printed circuit board (PCB). The preliminary setup to test the circuit used a signal generator to simulate the output of the piezoelectric. As explained in the former section, the input impedance of the quadrupler is high so the drawn current is low when the actual piezoelectric is connected. During the tests, the output of the quadrupler

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Fig. 15.8 Voltages at the input of the LDO, blue plot or V(n002) and output, green plot or V (n004)

when connected to the LDO has an average value of 3.69 V with an input of 1 V, so the real circuit behaves better than in the simulation where the output was 3.25 V. Finally, the output of the LDO shows a steady voltage at 3.1 V even with different input voltages as long as they were larger than 1 V. In the final design, the output of the regulator has a capacitance of 2.2 µF. If this capacitance is increased to store as much energy as possible to supply to a microprocessor, the behavior of the circuit changes. Some tests with different values have been conducted. The results with a capacitance of 100 µF are shown in Fig. 15.9. The oscilloscope shows a vertical scale of 1 V per division and a horizontal scale of 20 s per division. The output of the quadrupler is connected to channel 1 (yellow plot) whereas the output of the regulator is measured in channel 2 (blue plot). During the first seconds the input is set to 1 V and the circuit is not able to reach 3 V that would drive the LDO correctly during the time of the experiment though a steady increase was observed. After 60 s, the input is manually increased to 1.35 V and there is an increase in both outputs, quadrupler’s and regulator’s, until a steady state is swiftly achieved with 4.3 and 3.1 V respectively. Another test was conducted with a capacitance of 4700 µF. In this case, the input voltage had to be increased further to have a fast increase in the output so this results not recorded since they were above the maximum voltage that we get with the actual piezoelectric. Tests for several hours are scheduled to measure the actual charging time at lower voltages. Alternatively, the charging time without the regulator with an input of 1 V and 100 Hz was over 90 min reaching a voltage of 3.6 V. Assuming that the capacitance connected to the LDO is 100 µF, the stored energy would be 0.48 mJ. This means that the energy harvesting system connected to an ultra-low power microprocessor such as an STM32L476 would be able to acquire a vibration signal during several cycles, at least 50 ms, and supply power to send an alarm via Bluetooth or saving it in an NFC card. Therefore, a larger

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Fig. 15.9 Output of the quadrupler in channel 1 (yellow plot) and the regulator with a capacitance of 100 µF in channel 2 (blue plot). The change in voltage corresponds to an increase in the input to 1.35 V

capacitance is needed to comply with these requirements. In the case of having a capacitance of 4700 µF, the stored energy would be able to supply roughly 30 mA during 242 ms which would be perfectly suitable.

15.6

Conclusions

Exploiting the vibration in transformers to store energy and then use it to determine the level of vibration with a microprocessor and a sensor seems feasible with the preliminary results presented in this paper. The vibration levels correspond to a relatively small transformer, higher accelerations are expected in power transformers in substations. Therefore, the setup, electronics and results of the paper are more on the side of the worst case scenario. Moreover, the frequency of oscillation of the transformer tank would vary with the grid frequency changing the vibration mode of the cantilever beam with the piezoelectric yielding different voltages. It has been found that oscillating at other frequencies close to 100 Hz can create larger voltages that would help to charge the storage capacitors faster. Several ideas are considered to improve the response of the piezoelectric setup in a wider range of

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frequencies. There is still some work to do testing all the system together with the mechanical energy harvesting system and the electrical side of the setup connected together. Similar results as the presented now are expected since special care has been put in drawing low currents from the source. The programming of the microprocessor is also in progress though the consumption data is in the same range as that shown in the paper from the experience acquired with other harvesting systems. Acknowledgements M. Febbo and S. P. Machado thank the financial support from the National Scientific and Technical Research Council of Argentina (CONICET), Universidad Nacional del Sur (UNS) and Universidad Tecnologica Nacional, Facultad Regional Bahia Blanca (UTN-FRBB).

References 1. Garcia, B., Burgos, J., Alonso, A.: Transformer tank vibration modeling as a method of detecting winding deformations-part II: experimental verification. IEEE Trans. Power Delivery 21(1), 164–169 (2006) 2. Wu, X., Zhou, N., Shi, Y., Ji, S.: Vibration distribution characteristics on oil-tank surface of a single-phase transformer. In: 1st International Conference on Electrical Materials and Power Equipment (ICEMPE), pp. 344–348. IEEE (2017) 3. Li, Y., Wang, K., Shi, T., Zhu, X., Yu, H.: Study on spectrum eigenvalues of transformer vibration and its application on winding deformation detecting. In: International Conference on Mechatronics and Automation, pp. 2024–2029. IEEE (2016) 4. Seo, J.: A practical scheme for vibration signal measurement-based power transformer on-load tap changer condition monitoring. In: Condition Monitoring and Diagnosis (CMD), pp. 1–4. IEEE (2018) 5. Liu, H., Zhong, J., Lee, C., Lee, S.W., Lin, L.: A comprehensive review on piezoelectric energy harvesting technology: materials, mechanisms, and applications. Appl. Phys. Rev. 5 (4), 041306 (2018) 6. Priya, S., Inman, D.J.: Energy Harvesting Technologies, vol. 21. Springer (2009) 7. Narita, F., Fox, M.: A review on piezoelectric, magnetostrictive, and magnetoelectric materials and device technologies for energy harvesting applications. Adv. Eng. Mater. 20(5), 1700743 (2018) 8. Garcia, B., Burgos, J., Alonso, A.: Winding deformations detection in power transformers by tank vibrations monitoring. Electr. Power Syst. Res. 74(1), 129–135 (2005) 9. Erturk, A., Inman, D.J.: Piezoelectric Energy Harvesting. Wiley (2011) 10. Machado, S.P., Febbo, M., Rubio-Marcos, F., Ramajo, L., Castro, M.: Evaluation of the performance of a lead-free piezoelectric material for energy harvesting. Smart Mater. Struct. 24, 115011 (2015) 11. Piezo.com: S118-J1SS-1808YB. Piezoelectric bending transducer datasheet. Mide Technologies, June 2019. https://piezo.com/collections/piezoelectric-energyharvesters/ products/piezoelectric-bending-transducer-s118-j1ss-1808yb

Chapter 16

Digital Asset Management: New Opportunities from High Dimensional Data—A New Zealand Perspective Marianne Cherrington , Zhongyu (Joan) Lu , Qiang Xu , Fadi Thabtah , David Airehrour , and Samaneh Madanian Abstract As a testing ground for technology, the New Zealand microcosm is a stand out. In an age where many industries are vulnerable to disruptive innovation, a sustainable, integrated and entrepreneurial vision of digitally enabled asset management is vital. New Zealand is like a bridge between maturing Western economies and emerging Asian markets; with environmental values, a culture of sustainability and kaitiakitanga (stewardship), innovations that thrive in the diverse New Zealand paradigm are worth emulating. In our connected world, 5G and the internet of things are poised to explode the volume, velocity and variety of data. Survival may depend on an organisation’s ability to action knowledge from noisy, high dimensional data using machine learning. This is not as easy as it sounds; many challenges and barriers exist. This research proposes that by studying the emerging digital asset management trends in an educated and tech-savvy nation such as New Zealand, a clearer vision of how to allocate scarce resources can result. New value and exhilaration within organisations can be created so as to realise a myriad of sustainable opportunities.

M. Cherrington (&)
Z. (Joan) Lu
Q. Xu University of Huddersfield, Huddersfield HD1 3DH, UK e-mail: [email protected] Z. (Joan) Lu e-mail: [email protected] Q. Xu e-mail: [email protected] F. Thabtah Manukau Institute of Technology, Auckland 2104, New Zealand e-mail: [email protected] D. Airehrour Unitec Institute of Technology, Auckland 1025, New Zealand e-mail: [email protected] S. Madanian Auckland University of Technology, Auckland 1010, New Zealand e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_16

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Introduction

16.1.1 The New Zealand Microcosm ‘Kiwis’ love to quip ‘for such a small country, we punch above our weight’. From a microcosm and test market perspective, New Zealand has many pluses; the population is educated, receptive to technology, diverse and well connected. With fewer than five million people, even global firms use New Zealand as a test lab; it is also a perfect proving ground for software firms, social networks or app and gaming developers [1]. It is easy to start a business in entrepreneurial New Zealand, so lean start-ups desiring a conduit between western economies and vast, emerging Asian markets take hold here. New Zealand has some distinctive features, worthy of scrutiny. With a unique and treasured environment, culture of sustainability and Maori tradition of kaitiakitanga (stewardship), one might argue that culturally, there exists an embedded long-term mindset [2]. These attributes combine to make New Zealand case studies valuable for their insight and conjecture, if not emulation.

16.1.2 High Dimensional Data Organisations once apprehensive about the speed of change, now face disruption and the oxymoron that is ‘creative destruction’. Data are increasing in volume exponentially, whilst growing in variety and complexity. Connectivity will hit new heights with 5G and the internet of things (IoT), to bring new, evolving opportunities, and various risks. Survival may depend on emerging techniques such as unsupervised distillation of knowledge from data assets or adapting, innovating and creating digital products with greater efficiencies. Incumbents currently own 80% of the commercial world’s data along with vast resources [3]. Startups may be nimble and tech-ready, but the behemoths are awakening to the value that data and digital assets hold. New business models are creating the means to monetise digital activity. But turning that treasure trove into digital capital often requires not only tangible resources behind the processes (servers, platforms and software) but also a means to access diverse and multi-featured intangible data assets such as those found in consumer behaviour and employee interaction analyses [4]. In the mix, emerging methods of amalgamating and interpreting high dimensional data are opening up a myriad of possibilities.

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16.1.3 Burgeoning Opportunities With a wealth of data and digital assets, many firms are already seeking digital asset management (DAM) opportunities; return on investment is likely to be mid-to long-term. But data are becoming ultra-high dimensional; by exploring multi-dimensional digital assets now, visionary CEOs (be they from startup or incumbent firms) will have a first mover advantage, the compelling story and greater reward. Therefore, this paper examines high dimensional DAM opportunities from a New Zealand perspective, where some complexities diminish and new trends, barriers and challenges are exposed. Given that technological solutions created in one industry often converge and impact in a myriad of industrial applications, highly specialised solutions can create an edge in a global economy. With a population of fewer than five million people, New Zealand has spheres of embedded or niche specialisations within a few world-class firms. In parallel is the upsurge in thoughtful, responsible investing, commonly viewed as a mix of environmental, social and governance (ESG) factors with financial relevance, for analysis and decision-making [5]. Fragmented data has been a barrier to sustainable choices, but now machine learning and big data are exposing more holistic insights. In this paper, some societal trends of high dimensional data use in New Zealand are in section two. Section 16.3 views digital asset management from an environmental perspective in Aotearoa. Section 16.4 considers governance viewpoints regarding high dimensional data assets. This paper makes a unique contribution by illuminating how high dimensional data technologies are currently interacting and converging, as well as likely business impacts along emerging paths within the highly connected microcosm that is found in New Zealand. The paper closes with a vision that high dimensional digital assets hold in management’s quest for new and sustainable value opportunities.

16.2

The Human+ Era

16.2.1 Human-Machine Cooperation Digital humans are proliferating and taking jobs that deliver customer experience (CX). Machine learning techniques drive their functionality. The New Zealand founded firm Soul Machines is “revolutionizing human-to-machine interactions” [6]. With neural networks models for autonomous interaction in real-time, these digital humans utilise hyper-real technologies, a virtual nervous system and facial animation, to redefine the customer experience. The Digital DNA Engine™ uses deep AI, computational models and experiential learning and ground-breaking

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Digital DNA™ can now replicate real interfaces in record time, at scale [7]. High dimensional data provide responsive human communication and 5G will accelerate feature identification, processing and complex reaction processes [8] with greater automation. Soul Machines began as commercialised university research, but also benefitted from collaborations; Air New Zealand, has a holistic view of CX and sustainability within the larger Aotearoa context [2]. For more than 75 years, Air New Zealand has connected their Pacific islands; their vision for at least another 75 years aims to “supercharge New Zealand’s success socially, environmentally and economically” [9]. Soul Machine’s ‘Sophie’ has been showcasing Air New Zealand and Aotearoa overseas. Fintech is an industry using high dimensional data, but traditional banks are investing in emerging technologies too. ANZ bank uses their Soul Machines digital assistant ‘Jaimie’ to evolve from customer query data whereas the digital human ‘Kash’ will support services for Collection House in Australia [8]. Soul Machines announced the world’s first digital teacher in 2018, the next step in disruption of the education industry [10]. In the context the health system, digital teachers can support public health initiatives like MAiHEALTH that provide virtual medical centers [11] or iMoko, where digital instrument data is stored in the cloud for diagnosis, to deliver a multi-faceted treatment plan [12]. Add in continuous data from wearables, and mHealth initiatives could bring down the cost of healthcare, increase wellness for society as a whole and create targeted campaigns for vulnerable demographics. Even AIA and Sovereign (as the country’s largest insurers) will offer substantial rebates and benefits for members using wearables in exchange for their data that is collected; it will undoubtedly be subjected to machine learning for insights and prediction [13]. It is not solely altruistic; it is shrewd. Developments such as high dimensional DNA sequencing and micro-array analysis will dramatically impact the personalised healthcare market. In New Zealand, the ecosystem is very integrated and leveraged, yet resources (and digital assets) must be carefully deployed as ROI can be years in the offing.

16.2.2 VR-AR-MR: Extended Reality (XR) Computer-altered reality is changing the way businesses collaborate, retrain or retain talent and improve customer experience. Waxeye is a digital service agency combining culture, creativity and craftsmanship for engaging content. In conjunction with Air New Zealand and Te Papa, the museum of New Zealand, Waxeye created a virtual flight lab where an aircraft cabin transformed into a kiwi forest and passengers lounged on deckchairs [14]. It was a very clever 75th anniversary marketing tool for the airline. The technology sector is New Zealand’s third largest export sector (behind dairy and tourism); high-tech is also the fastest growing [15]. To help promote New Zealand’s technology capability overseas, Waxeye created The Upstarters, a digital

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activations corpus with AR and VR experiences available on an app to showcase New Zealand’s technology innovation expertise to a global audience [14]. High dimensional XR is used increasingly for training, marketing and operational efficiency. Jobs using these skills are on the rise and in New Zealand; the video game development industry is one of the fastest creative industries for both employment and revenues as the appetite for interactive entertainment continues to soar [16]. In the meantime, Weta Workshop continues to win awards for visual effects, create ground-breaking sci-fi games and has recently opened a world-leading dedicated mixed reality facility [17].

16.2.3 AI and High Dimensional Machine Learning AI machine learning methods are valued in retail, especially for high dimensional social media marketing which requires image recognition, language processing, audio, video and text recognition capabilities. Intela AI uses machine learning to help firms gain insights quickly. Powered by Farrago, a world first ‘automated domain expert,’ Intela AI can make predictions cost effectively ‘in 7 clicks not 7 weeks’ within government organisations and other firms [18]. The solution provides intelligent digital asset management from a single source of truth, reducing the need for unwieldly or the highly context-specific and expensive solutions that currently exist. In a world-first, AUT researchers have created NeuCube, a spiking neural network architecture that predicts a personal choice before a decision is actually made [19]. The opportunities for application are particularly promising in the fields of psychology, neuro-marketing, and criminal investigation.

16.3

Environmental Assets and High Dimensional Data

16.3.1 Climate Change There are more, and more-extreme climate events globally. In New Zealand, sea-level rise is a major concern and the effects are already being witnessed in the Pacific islands. The latest state of the environment report provides clear evidence that climate, freshwater and marine systems are changing [20]. Traditional ways of life and assets, for Māori, Pakeha and Polynesians are under threat but differing attitudes to planning and risk can complicate response [21]. A quantum leap in supercomputing power can help. The new High Performance Computing Facility (HPCF) is capable of processing two thousand trillion calculations per second and is being used to forecast weather

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events, monitor freshwater resources and ocean systems and detect seismic risk with algorithms and AI networks. Entirely new experiments are underway [22]. From a climatic perspective, New Zealand monitors change with NIWA’s supercomputer but also plays a vital role due to rare geographic position. Earth observation (EO) technology provides high dimensional data from satellite imagery, invaluable for monitoring and modelling climate change [23]. Pacific diplomacy creates a presence in global forums due to a 200 nautical mile exclusive economic zone [24]; Antarctica New Zealand supports New Zealand’s science and environmental protection activities in the world’s most southerly continent and The Deep South organisation collaborates to model how the Antarctic and the Southern Ocean affect the world’s changing climate and environment [25]. Digital conservation is emerging as a tool to assess and verify data and models regarding global climate patterns with machine learning methods [26].

16.3.2 Ecology New Zealand is well known for its unusual flora and fauna. Colloquially her citizens are called Kiwis (a flightless bird) and the extinct moa is renowned. Conservation, environmental protection and kaitiakitanga (Maori stewardship) are critical factors in decision making; an example is recently granted legal personhood of the Whanganui River, entrenching its encompassing, life-giving nature in the ecosystem. Yet Tourism New Zealand’s 100% Pure New Zealand campaign drew public flack due to mounting evidence of environmental degradation. Unique biota and ecosystems are very fragile. With new methods of sensing and monitoring our natural world, data are proliferating; we must have algorithms and AI so as to review and analyse data effectively [18]. Startups are addressing such issues. Halter is a fenceless farming system using GPS tracking that generates data about dairy herd behaviour, health and even emotions [27]. Dairy is New Zealand’s largest export sector [15]; cows produce methane (a greenhouse gas that contributes to global warming) and their runoff pollutes waterways. Halter technology supports farmers with valuable big data on their herd, and moves cows away from embankments to protect waterways as well as saving farmers hours every day. Technology impacts in a myriad of industrial applications in a precessional way. Halter founder, Craig Piggott worked at Rocket Lab when he conceived the plans for his firm; experiencing the high-tech startup motivated creation of his ‘billion dollar idea’ [27]. In Christchurch, Orbica are using aerial and satellite imagery to automate waterway assessments. Their deep learning algorithms analyse in minutes, not days, and correct errors with geodata using cloud computing and open-access Google Earth Engine [28]. An Intela AI—government collaboration created a machine learning image labelling platform to identify features and classify animals from a million + hours of video [6]. These technique do not require humans to review footage [29]; they

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have the added bonus that they are faster, more accurate and ease the navigation of privacy legalities. Similarly, video surveillance is replacing human observers on fishing vessels [30]; Trident Systems is working with Dragonfly Data Science to protect species bycatch.

16.3.3 Genomics Genomics Aotearoa is a New Zealand research alliance, mindful of Māori perspectives in genomics research practices. Projects such as kakapo genome collaborative research helps conservation of endangered species. Environmental metagenomics, protecting taonga (treasured) species and high quality genome assembly are projects underway; they use large, variable or very complex genomic data of high dimension [31]. Through partnership with New Zealand eScience Infrastructure (NeSI), Genomics Aotearoa has enhanced capability for world-class research in a dynamic field affecting health, environment and primary production. With virtual lab facilities, visualisation tools and XR, New Zealand’s unique conditions with Te Ao Maori are researched [32]. Genomic capabilities also build human health initiatives and New Zealand is uniquely placed to develop these technologies (with longitudinal research like ‘The Dunedin Study’ and over twenty years of electronic health records); machine learning and AI are uncovering unique insights and benefits. An example is Pinnacle Ventures launch of a pharmacogenomics programme to drive personalised prescribing decisions with genetic testing and biomarker information on electronic health records [33]. AI is everywhere and Precision Driven Health is focusing healthcare decisions [34]. Medical researchers can auto-diagnose with a world-first AI platform created by oDocs [35]. With these types of collaborations, New Zealand can play an important role on the global AI stage [36].

16.3.4 Kaitiakitanga The Treaty of Waitangi aligns the pathway for environmental progress in Aotearoa. Not only is Maori consultation required in many initiatives, it is important to have Maori-led capacity in genome research to protect the rights, taonga (treasures) of Maori, which ultimately protects the interests of all. Such support navigating debatable ethical and cultural issues is critical in these emerging frontiers [37]. A balance of vision and care is essential. There is a dearth of knowledge about genetic variants for the unique Polynesian and Maori populations which is already limiting access to equitable healthcare and

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visions of personalised healthcare possible with genetic research [38]. Taonga genomes, alive within the evolutionary makeup of indigenous people of New Zealand are inextricably linked to the distinct environment and ecology of Aotearoa.

16.4

Governance

16.4.1 Kaitiakitanga Stewardship of data, along with care in its use, is entwined in evolving debate. In the rush to apply the benefits of technology, New Zealand can lead the way in exhibiting a culturally aware and collaborative model of how to approach these treacherous waters using generational, legacy values [38]. Maori indigenous knowledge, ethics and values can provide a framework within modern practices regarding issues such as ownership, privacy, consent and legal or policy guidelines for bio-banking and genetic research practices [39]. Indigenous involvement and contribution in these debates is needed more than ever; Aotearoa, with its unique partnership with Maori through the Treaty of Waitangi is evolving, showing an evolving method of collaboration.

16.4.2 The Story of Us Maori recitation of genealogy (whakapapa) is a sacred taonga, weaving both Maori and Pakeha into the story of Aotearoa and its place and way of being in the world. It traces descent through past generations, directing movement and future evolution [40]. High dimensional data is now creating an expanded vision within the heritage sector with digital asset management systems recording archives, library exhibits and museum taonga into living creative forms and constructs for dissemination. These digital assets have the unique feature of being a product and an asset, with images, video and audio; digital use does not reduce the value of the actual asset [41]. From ANZAC history, Maori taonga to historical specimens, the high dimensional data available through new digitisation methods can secure the future of museums in the world of big data. Firms such as Dexibit are using technologies to democratise collections that curate our cultural future [42]. Te Papa, the museum of New Zealand, is leading initiatives with the award-winning Dexibit procedures and machine learning techniques to create and forecast visitor engagement [43].

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16.4.3 Rocket Lab Beck-ons A global leader in small satellite technology, New Zealand’s rocket man, Peter Beck, founded Rocket Lab and launched the county into spaceflight [44]. Rocket Lab brings a turnkey solution and affordability to satellite technologies such as remote sensing [45] and earth observation (EO), techniques that require high dimensional data analysis [46]. Beck’s visionary leadership and ability to secure NASA collaboration is extraordinary and exemplary. Clients such as DARPA, indicate that these Rocket Lab payloads are used not only for climate data and environmental preservation in New Zealand [47], but for defence purposes. There have already been spinoffs, research and expertise capabilities that are continuing to evolve from Rocket Lab initiatives and partnerships [27]. One of the biggest global projects in which New Zealand scientists and IT specialists is involved is the Square Kilometer Array (SKA); it requires ongoing governmental commitment yet the Ministry of Business Innovation and Employment is querying these funding allocations. Lack of governance continuity can put such projects in jeopardy [48].

16.5

Conclusion

16.5.1 A Vision of High Dimensional Data as a Manageable Asset New Zealand has a society and connectedness that enables social cohesion and focus; the culture produces world-class, scalable solutions [15]. By studying ground-breaking New Zealand firms using high dimensional data, digital asset management trends and challenges are revealed. An eminent Kiwi, Sir Ernest Rutherford famously said ‘We haven’t the money, so we’ve got to think’. By taking a deeper look at high dimensional data assets, valuable and untapped resources can be revealed; innovations and collaborations can leverage investment. In several key industries, digital asset management is promising that visionary organisations can deliver new evidence-based and sustainable opportunities that solve problems to create new value for society.

References 1. Kiwis as Guinea Pigs. The Economist, 23 May 2015. www.economist.com/news/business/ 21651858-small-technophile-country-great-place-test-digital-products-kiwis-guinea-pigs 2. Ericksen, N., Berke, P., Dixon, J.: Plan-Making for Sustainability: The New Zealand Experience. Routledge, London (2017)

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3. The Year of the Incumbent. The Economist, 6 January 2016. www.economist.com/business/ 2018/01/03/2018-will-be-the-year-that-big-incumbent-companies-take-on-big-tech 4. Bughin, J., Manyika, J.: Measuring the Full Impact of Digital Capital. McKinsey & Company, Chicago (2018) 5. Kell, G.: The remarkable rise of ESG. Forbes, 47, 11 July 2018 6. A Digital Brain that Autonomously Animates AI Homepage. https://www.soulmachines.com/. Accessed 9 May 2019 7. Strang, E.: Soul machines unveils its first emotionally intelligent, lifelike avatar. Idealog (2017). https://www.soulmachines.com/blog/2017/2/18/idealogconz-soul-machines-uneilsits-first-emotionally-intelligent-life-like-avatar. Accessed 15 May 2019 8. Rowell, A.: Brain Power: Adding the Soul to Machines. M2 Magazine, 1 March 2019 9. Air New Zealand (2018). Sustainability Report 2018. Retrieved from Air New Zealand website. https://p-airnz.com/cms/assets/PDFs/2018-Sustainability-Report.pdf 10. PRESS: World’s first digital teacher starts work teaching kids about energy, 18 September 2018. https://www.soulmachines.com/news/2018/09/18/press-worlds-first-digital-teacherstarts-work-teaching-kids-about-energy/ 11. Butler, R.: Virtual Health: Rapid review of evidence and implications (2018) 12. EHealth News Features. iMOKO tackling issue of inequitable access to healthcare. Health Informatics New Zealand, Albany, Auckland, 18 February 2019 13. AIA, Sovereign and You: Championing a healthier, better protected NZ, 20 May 2019. https://www.aia.co.nz/en/campaign/championing-a-healthier-more-protected-nz.html 14. Virtual Flight Lab. 8 Mat 2019. https://www.waxeye.co.nz/work/airnz-virtual-flight-lab 15. Technology Investment Network. TIN 100 report (2011). http://www.tinet-work.co.nz/ TIN100+Report.html 16. Smith, C.: NZ Gaming Studio Industry Booming. The New Zealand Herald, Auckland (2018) 17. News. Weta Workshop, 30 April 2019. https://www.wetaworkshop.com/news/latest/ 18. Mack, B.: Intela’s Asa Cox on Farrago and “unsexy” (but still very cool) AI. Idealog, 30 January 2018. https://idealog.co.nz/tech/2018/01/idealog-podcast-intelas-asa-cox-farragoand-unsexy-ai. Accessed 15 May 2019 19. Kasabov, N., Scott, N., Tu, E., Marks, S., Sengupta, N., Capecci, E., Othman, M., Doborjeh, M.G., Murli, N., Hartono, R., Espinosa-Ramos, J.I.: Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Netw. 78, 1–14 (2016) 20. New Zealand’s Environmental Reporting Series: Environment Aotearoa 2019 (ME 1416). Retrieved from Ministry for the Environment & Stats NZ website (2019). www.mfe.govt.nz/ publications/environmental-reporting/environment-aotearoa-2019 21. Manning, M., Lawrence, J., King, D.N., Chapman, R.: Dealing with changing risks: a New Zealand perspective on climate change adaptation. Reg. Environ. Change 15(4), 581–594 (2015) 22. High Performance Computing Facility, 23 November 2018. https://www.niwa.co.nz/ourservices/high-performance-computing-facility 23. Guo, H.D., Zhang, L., Zhu, L.W.: Earth observation big data for climate change research. Adv. Clim. Change Res. 6(2), 108–117 (2015) 24. Fry, G., Tarte, S. (eds.): The New Pacific Diplomacy. Anu Press, Canberra (2015) 25. Saunders, C., Guenther, M., Dalziel, P.: The Contribution of Antarctic-Related Activities to the Canterbury and New Zealand economy. Agribusiness and Economics Research Unit, Lincoln University, Lincoln (2016) 26. Gillies, T.T.: Digital conservation in Antarctica (2018) 27. Strang, E.: Meet the young entrepreneur at the helm of Halter, the NZ agritech company backed by Peter Beck, Data Collective and Peter Thiel. Idealog, 22 June 2018 28. Big data is revolutionising NZ, 1 April 2018. https://www.newsroom.co.nz/2018/04/01/ 100376/big-data-is-revolutionising-nz

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29. Cherrington, M., Thabtah, F., Lu, J., Xu, Q.: Feature selection: filter methods performance challenges. In: 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1–4. IEEE, April 2019 30. Abraham, E.R., Richard, Y., Berkenbusch, K., Thompson, F.: Summary of the capture of seabirds, marine mammals, and turtles in New Zealand commercial fisheries, 2003 to 2012– 13. New Zealand Aquatic Environment and Biodiversity Report No. 169 (2016) 31. Projects, 15 April 2019. https://www.genomics-aotearoa.org.nz/projects#on-this-page–1 32. Genomics Aotearoa, 3 April 2019. https://www.mbie.govt.nz/science-and-technology/ science-and-innovation/funding-information-and-opportunities/investment-funds/strategicscience-investment-fund/funded-infrastructure/genomics-aotearoa/ 33. Genetic testing to drive prescribing, 5 May 2019. https://www.hinz.org.nz/news/449084/ Genetic-testing-to-drive-prescribing.htm 34. AI Day: Building trustworthy AI for the future, 2 May 2019. https://orionhealth.com/nz/ knowledge-hub/blogs/ai-day-2019-building-trustworthy-ai-for-the-future/ 35. Hempleman, L.: NZ Produces the World’s First A.I. Medical Diagnosis Platform. Medtech Centre of Medical Excellence (2018). https://www.cmdt.org.nz/news-item/nz-produces-theworlds-first-ai-medical-diagnosis-platform 36. Thinking Ahead: Innovation through Artificial Intelligence (2018). Callaghan Innovation: https://www.callaghaninnovation.govt.nz/sites/all/files/ai-whitepaper.pdf 37. Hudson, M., Beaton, A., Milne, M., Port, W., Russell, K.J., Smith, B., Uerata, L.: Te Mata Ira: guidelines for genomic research with Māori. Te Mata Hautū Taketake-Māori & Indigenous Governance Centre, University of Waikato (2016) 38. Kennedy, M.A.: A genome project for Māori and Pasifika: charting a path to equity in genomic medicine for Aotearoa. The New Zealand Medical Journal (Online), 131(1480) (2018) 39. Beaton, A., Smith, B., Toki, V., Southey, K., Hudson, M.: Engaging Maori in biobanking and genetic research: legal, ethical, and policy challenges. Int. Indig. Policy J. 6(3), 1 (2015) 40. Rameka, L.: Whakapapa: A Māori Way of Knowing and Being in the World. In: Encyclopedia of Educational Philosophy and Theory, pp. 1–6 (2016) 41. McCarthy, C.: Museums and Māori: Heritage Professionals, Indigenous Collections, Current Practice. Routledge, London (2016) 42. Knox, J., Ross, J.: ‘Where does this work belong? ‘New digital approaches to evaluating engagement with art. Museums and the Web (2016) 43. How Dexibit is revolutionising museums and galleries using big data solutions, 11 December 2018. https://thespinoff.co.nz/podcast/business-is-boring-callaghan/07-06-2018/how-dexibetis-revolutionising-museums-and-galleries-using-big-data-solutions/ 44. May, G.: Details of Hawke’s Bay’s Rocket Lab’s next mission revealed. New Zealand Herald, Auckland, 12 March 2019 45. Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A., Jie, W.: Remote sensing big data computing: challenges and opportunities. Future Gener. Comput. Syst. 51, 47–60 (2015) 46. Quartulli, M., Olaizola, I.G.: A review of EO image information mining. ISPRS J. Photogramm. Remote Sens. 75, 11–28 (2013) 47. Witharana, C., Lynch, H.: An object-based image analysis approach for detecting penguin guano in very high spatial resolution satellite images. Remote Sens. 8(5), 375 (2016) 48. Comment: Govt is pulling NZ out of a global science project with spin-offs for IT here Derek McCormack. New Zealand Herald, Auckland, 8 April 2019

Chapter 17

Introducing a Field Service Platform Maike Müller, Dirk Stegelmeyer , and Rakesh Mishra

Abstract The introduction of platforms disrupted many consumer industries. Prominent examples include Amazon, Airbnb, and Uber, who upended their respective industries. Eventually, platforms will also enter capital goods industries and their downstream service businesses. A part of these downstream businesses in capital goods industries are field services. In this conceptual paper, we show how the current field service delivery, which is characterized by static alliances, can be replaced with dynamic platform ecosystems, which are enabled through remote monitoring technology and mobile collaborative augmented reality. We propose a field service platform ecosystem framework by applying platform research fundamentals to the industrial field service businesses.

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Introduction

The introduction of platforms disrupted many consumer industries. Noteworthy examples are Amazon, Airbnb, and Uber, who turned their industries upside down [1]. Eventually, platforms will also enter capital goods industries and their downstream service businesses, as capital goods industries are increasingly moving towards a digital foundation of networks and connectivity [2]. A part of these downstream businesses are field services [3]. Field services are activities performed at asset user’s site, including maintenance tasks and repairs. This involves either the deployment of field service technicians, or activities that enable asset users to independently service their assets.

M. Müller (&)
R. Mishra School of Computing and Engineering, University of Huddersfield, Huddersfield, UK e-mail: [email protected] M. Müller
D. Stegelmeyer Institut für Interdisziplinäre Technik, Frankfurt University of Applied Sciences, Frankfurt am Main, Germany © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_17

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The aim of this conceptual paper is to propose a field service platform ecosystem framework that is enabled by remote monitoring technology and mobile collaborative augmented reality. The framework provided in this paper is the authors’ vision of future field service delivery based on a literature study and practical experience. The scope of this paper is limited to the mere description of our vision, rather than analyzing activities and success factors of the introduction of a future field service platform ecosystem.

17.2

Conceptual Foundation

To facilitate the understanding of the framework proposed in this paper, this section introduces the underlying concepts and terminology used. In this paper, we refer to assets (i.e., capital goods or machines) in the sense of highly complex product systems, which are defined as “high cost, engineering-intensive products, systems, networks and constructs” [4]. We are limiting our thoughts to capital goods that cannot be sent back to the original equipment manufacturer (OEM) to be serviced. Asset users are customers of the OEM, who purchase capital goods and subsequently consume services. Capital goods sold to the asset users and accessible to the OEM are also called installed base in this paper.

17.2.1 Current Field Service Delivery Asset users of complex capital goods are usually risk-averse and commonly demand maintenance services from the OEM. Most OEMs are selling their products on a worldwide scale. Therefore, for OEMs, field services became very important to satisfy asset users in distant regions [5]. Field service delivery usually requires prompt reaction to faults, since downtime is expensive for asset users. Hence, to provide field services on worldwide installed bases, a local service delivery infrastructure is expected [6]. However, in many cases, OEMs do not possess the necessary resources to deliver all required field services for their worldwide installed base. Smaller OEMs especially lack resources to set up multinational subsidiaries; hence, they struggle to provide field services for their installed base in distant regions [7]. Thus, smaller OEMs are more often reliant on inter-firm collaborative service delivery with local service providers [8]. However, the challenge to cover distant regions applies also to large multinational enterprises, namely when serving regions with a small and dispersed local installed base, or when servicing marginal products with only a limited share of turnover and profit for the OEM. Hence, OEMs build strategic alliances to combine their own resources with those of third-party service providers,

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who complement the OEM’s field service offerings [9, 10]. Third-party service providers are usually independent of OEM and asset user, and they might be self-employed field service technicians or specialized service companies. Less knowledge-intensive services are commonly performed by local service providers, who must be trained constantly by the OEM. Service providers typically send their field service technicians to OEMs or OEMs conduct training at service providers’ business locations [8]. To this end, OEMs make knowledge (drawings, program code etc.) available to the service provider. For more knowledge-intensive services, the OEM’s service technicians travel to asset users’ sites, with the net intervention time being only a small part of the gross intervention time attributed to the job. These travels are time consuming, and downtime for this service becomes an expensive venture for asset users. Further, many asset users are only willing to pay for value-adding tasks, such as labor and spare parts, but not for non-valuable items, such as the technician’s travel expenses, travel time, and waiting periods for spare parts [8]. Altogether, the current field service situation is determined by rather static strategic alliances, which are characterized by contract-based agreements between OEMs and service providers. However, the current situation holds the potential for efficiency gains by optimizing the service labor intervention time.

17.2.2 Multi-sided Platform Ecosystems The term platform describes a range of phenomena in management research and other disciplines [11, 12]. In their systematic literature review of platform management research, Thomas et al. [11] identify four platform concepts, two of which are fundamental to this paper: multi-sided platforms and platform ecosystems. Generally, platforms increase the efficiency of underutilized resources [13]. Service labor cannot be stored and may thus be regarded economically as a highly perishable good. Hence, platforms might better utilize the expensive resource service labor. Multi-sided platforms are characterized by at least two distinct groups of members, producers, and users of a service who interact through a platform which serves as an market intermediary [12]. Multi-sided platforms facilitate transactions between the groups of members [13]. In contrast to traditional market intermediaries, such as Walmart or Würth, multi-sided platforms, such as Uber and Alibaba, do not take ownership of products and services [14]. Multi-sided platforms share four common characteristics [15]: First, the purpose of a platform is to facilitate valuable interactions between distinct groups of members and thereby reduce transaction costs. Second, the main characteristic of a platform is the indirect network effect, which is described as the fact that all members benefit from others using the platform [13]. The more members of each distinct group that use the platform, the more valuable it becomes [15]. The value of the network effect is mainly determined by the quality of matches—the larger each distinct group, the

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richer the data to match supply and demand [1]. Further, platform owners commonly set rules against any misconduct that would reduce the value of the offer for other members of the platform. Third, platforms often struggle with the critical mass hurdle of attracting enough members of each distinct group to assure the indirect network effect [15]; often, this is the most serious problem for platform providers [16]. Fourth, a common characteristic of platforms is that one group of members pays more than marginal cost, whereas another group of members pays less than marginal cost or even earns rewards for using the platform [15]. In addition to the notion of the platform as an intermediary matchmaker, the platform ecosystem is what Gawer [17] calls industry platforms. Industry platforms “serve as foundations upon which other firms can build complementary products, services, or technologies” [17] and “act as a hub of value exchange” [11]. The platform ecosystem particularly emphasizes the industrial community and surrounding ecosystem of the platform [11]. Prominent examples of platform ecosystems are Apple’s iPhone and its surrounding ecosystem accessible through the App Store [1]. The platform’s ecosystem creates value to users through specialization and complementary offerings, whereas the platform itself provides technology standards to the ecosystem, such as interfaces [11]. In the context of field service, a platform ecosystem holds the potential to replace the contract-based strategic alliances with a digital ecosystem characterized by dynamic partnerships between asset users, service providers, and OEMs.

17.2.3 Remote Service Maintenance of capital goods is knowledge-intensive and complex [18], and to deliver field services effectively, OEMs need to be capable of diffusing knowledge to subsidiaries, asset users, and service providers [6]. Remote services are one way to diffuse knowledge—they range from simple helpdesk support via telephone to guided repairs via video-streams or integrated systems that diagnose problems and request human support for problem solving [5, 19, 20]. OEMs typically run a telephone helpdesk to provide engineering expert knowledge to on-site technicians who physically perform maintenance and repair tasks at the installed base. These on-site technicians can either be technicians of the asset user, the OEM, or a third-party service provider. Basically, two types of technology are available to assist remote experts and on-site technicians: remote monitoring technology (RMT) and mobile collaborative augmented reality (MCAR). Remote Monitoring Technology (RMT) RMT is a combination of software and hardware components enabling remote access to the machine control systems to determine current health condition, diagnose failures, and make informed decisions. Although the adoption of RMT brings numerous challenges [21], the advantages are widely acknowledged [5, 20, 21, 23],

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and the technology is established across industries [3]. RMT saves time and money in error diagnosis and repair [20], and is recognized as an enabler for remote service delivery in capital goods industries [22], and a facilitator of globalization activities [5]. For a more extensive list of RMT’s benefits, consult Grubic’s [23] literature review. Mobile Collaborative Augmented Reality (MCAR) MCAR can complement the remote service delivery of OEMs by improving knowledge transfer from remote experts to on-site technicians. MCAR links remote experts and on-site technicians by using live audio-video streams with integrated Augmented Reality (AR) features. Many examples of MCAR applications can be found in literature [24–30], and studies indicate that the approach of sharing joint views, especially when augmented with pointing and drawing tools, is superior to audio-only support because it improves situational awareness and mutual understanding of remote experts and on-site technicians [31–33]. MCAR-systems are composed of hardware and software components. In industrial practice, the hardware components are usually smart devices, such as tablets and smartphones, or head-mounted displays [24]. The smart device is worn by the on-site technician, and the device’s main function is to capture the live audio-video stream and display information to assist the on-site service technician. In practice, the software component is usually commercially available collaboration software that enables the live audio-video stream and provides integrated AR-features [24]. Commonly, the software component provides a browser-based interface for the remote expert—usually accessed via a personal computer or a tablet. The remote expert’s interface displays the live audio-video stream captured by the hardware component and features communication functionalities such as laser pointing, drawing pens, and 2D annotations. Features such as document sharing, a chat box, and language translation can complement the live audio-video stream. Researchers also provided more complex applications enabling virtual object sharing using CAD-datasets [25, 29] or access to automated knowledge databases using AI [24]. Despite the application and composition of the system, all MCAR solutions share the same purpose: enabling knowledge transfer to on-site service technicians. Even though plentiful challenges to the industrial implementation of MCAR exist [7, 30], recent studies also demonstrate the technology’s huge potential and showcase that OEMs are starting to implement the technology [7, 34, 35].

17.3

Field Service Platform Ecosystem Framework

Presently, field service delivery is based on static strategic alliances. Since RMT and MCAR have recently evolved enough, in the future, the traditional approach of static strategic alliances might be replaced by a digitally managed ecosystem. In this section, we propose a field service platform framework (Fig. 17.1) with two value

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propositions to the members of the ecosystem. The first value proposition of the platform is to match OEMs and asset users with local service providers, or rather service technicians, just like Uber matches travelers with drivers. The second value proposition is to provide nontraditional data-driven offerings to OEMs, asset users and service providers. The field service platform framework consists of five groups of members participating in different roles: the platform owner, OEMs, asset users, service providers, and application suppliers. The platform owner facilitates valuable interactions by providing a multi-sided platform for OEMs, service providers, and asset users and stimulates external, complementary value creation by simultaneously providing the foundation for other application suppliers to provide their own data-driven offerings [17]. The platform owner controls the platform, defines the technical standards, and provides interfaces to integrate business processes such as orders, order confirmations, invoices, service reports, and technical documentation. Obviously, the platform owner has to provide a strong up-front design to facilitate interactions between all members [1]. Service providers, OEMs, and application suppliers take the role of producers who create specialized and complementary offerings. Local service providers deploy service technicians to the asset users’ sites to physically perform maintenance activities and repairs. OEMs remotely provide expert knowledge to support service providers and asset users by using RMT and MCAR. Application suppliers add value by providing data-driven offerings created with the meta-data generated by other members using the platform. Practically speaking, either an asset user with a faulty machine or an OEM fulfilling warranty, or a maintenance contract can request field service via the platform. Thus, both OEMs and asset users can assume the role of field service buyers. OEMs purchase the physical component of a field service via the platform, which is the labor of a field service technician deployed by a local service provider. Asset users purchase the solution of a certain problem via the platform, which requires cooperative field service activities between OEMs and service providers.

Fig. 17.1 Field service platform ecosystem framework

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All members of the ecosystem have an interest in sanctioning other members’ misconduct. The platform owner must guarantee that the proprietary data of the OEMs, the service providers, and the asset users will be kept confidential. This confidential information includes technical drawings, technical documentation, program code, details of the products of asset users, and much more. Therefore, we argue that no incumbent of the industry will be trusted to own such a platform. There will be competing incumbents, namely OEMs, service providers, and asset users on the platform who won’t trust another incumbent concerning the confidentiality of their intellectual property. Moreover, we argue that the platform will most likely be an industry-specific platform. The technicians need to possess the capability to maintain and repair technologically highly complex systems, e.g. printing machinery, machine tools, packaging technology, thus also the surrounding ecosystem of application suppliers must be very industry-specific [2]. In our proposed framework, the field service ecosystem consists of five groups of members: the incumbents of the field service business, namely OEMs, service providers and asset users, and two new members, namely platform owner and application suppliers. The current field service situation of static strategic alliances is replaced by a dynamic matching process of supply and demand within this framework.

17.3.1 Additional Data-Driven Offerings Within our proposed framework, the field service platform not only facilitates the matching of supply and demand for incumbents, but also serves as the foundation upon which other application suppliers build their complementary data-driven offerings [1, 17]. This is the aforementioned second value proposition of the platform: to provide nontraditional data-driven offerings to OEMs, asset users and service providers. The fusion, analysis, and interpretation of very different meta-data generated on the platform, and from external sources creates new value [36]. The new value can either be created by the platform owner or application suppliers, who are subject matter experts. The application suppliers attach themselves to the platform, expanding its ecosystem, just like application developers do in the App Store. The data used by the platform owner and application suppliers are not the confidential intellectual property of the OEM, such as technical drawings or electrical plans, but rather meta-data created by participating in the platform ecosystem. Meta-data might be browsing history, clickstreams, cellphone locations, technician and asset user reviews and ratings, work experience and qualifications, professional networks, work preferences; time stamps of service quotes, purchasing orders, order confirmations, service reports, invoices, and payments. Imagine the following examples:

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It will be possible to forecast the demand for required field service by means of algorithms using platform data and external data, such as weather or holidays. The rating of asset users, OEMs, and service providers will be scored and provided to the other members of the ecosystem to facilitate their decision to engage in partnerships. This information could include the length of time it takes to obtain a quote from a service provider or the payment behavior of an asset user. The quality of an individual service technician is assessed by data analytics combining data from service reports, customer feedback, the duration of the intervention, the installed equipment type, the quantity of additional information obtained from the network to perform the job, the frequency of expert answers provided to peer service technicians, or number of disputed invoices. With funding discounts on labor rates, the individual asset users’ price sensibilities can be ascertained. Application suppliers may combine this price sensitivity data and other data, such as the hierarchical function of the responsible person in the user’s organization, time of the day of the request, company profile, age and complexity of the equipment, and the overall utilization of the field service offer at the time of the request. As a result, the application supplier can offer a dynamic pricing strategy to optimize margins for OEMs or service providers. Altogether, the proposed field service platform is not only a matchmaker between incumbents, it is also a hub for value exchange between incumbents and nontraditional market participants such as application suppliers who provide complementary offerings. Therefore, the platform is also a marketplace for additional value in the sense of a platform ecosystem.

17.3.2 Motivation to Join a Platform Ecosystem A prerequisite for the adoption of the field service platform framework is substantial incentive for all members of the current field service ecosystem to accept the change implied. In principle, there is an incentive for change because the platform increases the efficiency of underutilized field service labor and creates additional value for the members of the ecosystem. Asset users benefit from the reduction of activities that do not add value, such as traveling and waiting hours. In addition, the asset users’ productivity is increased, as the omission of those hours reduces the mean time to repair and increases asset availability. Service providers benefit from access to a large installed base and the platform’s matching algorithm, which increases the utilization of the field service technicians. OEMs benefit because the need for highly skilled service technicians with high renumeration is decreased, and sales are facilitated by means of improved service coverage of local markets. All incumbents benefit from the convenient service delivery process provided by the platform through its strong up-front design. Furthermore, all incumbents benefit from the rating, indicating the

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trustworthiness of the service providers, or the payment behaviour of the asset users and OEMs. In addition, all incumbents benefit from the data-driven offerings of the application suppliers. Application suppliers benefit from the access to a large installed base [17] and access to meta-data. We propose that the use of the platform is free for OEMs, asset users, and service providers. Generally, the value of data can be increased by re-using or selling it [37]. The platform owner generates revenues by re-using meta-data and selling data-driven offerings to members of the ecosystem. Otherwise, the platform owner sells the meta-data to application suppliers. The application suppliers will fuse, analyze and interpret the meta-data and sell this added value to the incumbents. This means that in our proposed framework, the incumbents of the field service delivery pay nothing to use the platform, which is less than their marginal costs. However, the application suppliers will pay the full cost of the platform, which is much more than the small marginal cost they generate. Despite the many benefits previously discussed, there are also disadvantages of the proposed framework—especially from the OEM’s perspective. If the proposed framework were instituted, an OEM’s revenue from deploying employed service technicians, to service the installed base, would decrease. Further, the platform encompasses unprecedented transparency and comparability of the OEM’s offerings. This transparency enables asset users to compare the products and services of competing OEMs better than ever before, which may be not desired by OEMs. Moreover, a major challenge for OEMs might be their mindset to reject substantial changes to their business model [2]. Thus, initially only OEMs lacking local field service infrastructure will be motivated to join a platform ecosystem. However, OEMs face the risk of commoditization of their offerings if they do not holistically revisit their value propositions [2].

17.4

Conclusion

We provided a framework for future field service platform ecosystems, which is expected to be a substitute for current static strategic alliances. In addition, we analyzed some benefits and disadvantages of the proposed platform ecosystem for its members, and identified remote monitoring technology, mobile collaborative augmented reality, and platform technology as enablers for platform ecosystems. A basic assumption to our proposed framework is that meta-data generated by using the platform can be re-used to create valuable data-driven offerings, incumbents of the current field service delivery are willing to pay for. This assumption implies that the value of the data-driven offerings exceeds the total cost of the platform. We argue that service providers, asset users, and application suppliers have an incentive to join a platform ecosystem; they have nothing to lose and stand only to gain. However, OEMs will potentially lose their traditional field service revenue streams. Yet, over the long term, the platform might set an industry standard that later entices large multinational OEMs to join due to the network effect. Thus, a

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general question for OEMs is how the increased value for asset users can be monetized without risking the commoditization of their offerings in the platform context. We argue that the importance of intellectual property protection will most likely lead to a platform owner not being an established incumbent of the market. Additionally, the platform is likely to be industry specific. Furthermore, the authors are convinced that arising platforms will not be limited to the field service domain applications described in this paper; rather, they will affect the entire industrial service business, including spare part identification and supply, predictive maintenance, targeted ads, and more.

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34. 35. 36. 37.

Introducing a Field Service Platform

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Ninth International Conference on Advances in Computer-Human Interactions, 24–28 April 2016, Venice, Italy. IARIA, Wilmington (2016) Biehl, M., Prater, E., McIntyre, J.R.: Remote repair, diagnostics, and maintenance. Commun. ACM 47(11), 100–106 (2004). https://doi.org/10.1145/1029496.1029501 Küssel, R., Liestmann, V., Spiess, M., et al.: “TeleService” a customer-oriented and efficient service? J. Mater. Process. Technol. 107(1–3), 363–371 (2000). https://doi.org/10.1016/ s0924-0136(00)00727-5 Klein, M.M., Biehl, S.S., Friedli, T.: Barriers to smart services for manufacturing companies – an exploratory study in the capital goods industry. J. Bus. Indus. Mark. 33(6), 846–856 (2018). https://doi.org/10.1108/JBIM-10-2015-0204 Brax, S.A., Jonsson, K.: Developing integrated solution offerings for remote diagnostics. Int. J. Opt. Prod. Manag. 29(5), 539–560 (2009). https://doi.org/10.1108/01443570910953621 Grubic, T.: Servitization and remote monitoring technology. J. Manuf. Technol. Manag. 25(1), 100–124 (2014). https://doi.org/10.1108/JMTM-05-2012-0056 Hadar, E., Shtok, J., Cohen, B., et al.: Hybrid remote expert - an emerging pattern of industrial remote support. In: Proceedings of the Forum and Doctoral Consortium Papers Presented at the 29th International Conference on Advanced Information Systems Engineering, pp. 33–40 (2017) Alexander, T., Ripkens, A., Westhoven, M., et al.: Virtual tele-cooperation: applying AR and VR for cooperative tele-maintenance and advanced distance learning. In: Proceedings of the AHFE 2017 International Conference on Human Factors in Training, Education, and Learning Sciences, vol. 596, pp. 234–244. Springer, Cham (2017) Mourtzis, D., Zogopoulos, V., Vlachou, E.: Augmented reality application to support remote maintenance as a service in the robotics industry. Procedia CIRP 63, 46–51 (2017). https:// doi.org/10.1016/j.procir.2017.03.154 Alem, L., Tecchia, F., Huang, W.: Remote tele-assistance system for maintenance operators in mines. In: 11th Underground Coal Operators’ Conference, University of Wollongong & the Australasian Institute of Mining and Metallurgy, pp. 171–177 (2011) Bottecchia, S., Cieutat, J.-M., Jessel, J.-P.: T.A.C: augmented reality system for collaborative tele-assistance in the field of maintenance through internet. In: Proceedings of the 1st Augmented Human International Conference, Article no. 14 (2010) Wang, J., Feng, Y., Zeng, C., et al.: IEEE International Conference on Automation Science and Engineering (CASE), 18–22 August 2014, Taipei, Taiwan. IEEE, Piscataway (2014) Masoni, R., Ferrise, F., Bordegoni, M., et al.: Supporting remote maintenance in industry 4.0 through augmented reality. Procedia Manuf. 11, 1296–1302 (2017). https://doi.org/10.1016/j. promfg.2017.07.257 Fussell, S.R., Setlock, L.D., Kraut, R.E.: Effects of head-mounted and scene-oriented video systems on remote collaboration on physical tasks. In: New Horizons Conference Proceedings, Conference on Human Factors in Computing Systems, vol. 5, no. 1, pp. 513– 520 (2013) Bauer, M., Kortuem, G., Segall, Z.: Where are you pointing at? A study of remote collaboration in a wearable video conference system. In: Wearable Computers: 3rd International Symposium, Digest of Papers, 18–19 October 1999, San Francisco, California, pp. 151–158. IEEE Computer Society Press, Los Alamitos (1999) Fussell, S.R., Setlock, L.D., Yang, J., et al.: Gestures over video streams to support remote collaboration on physical tasks. Hum. Comput. Interact. 19(3), 273–309 (2004). https://doi. org/10.1207/s15327051hci1903_3 Porter, M.E., Heppelmann, J.E.: Why every organization needs an augmented reality strategy. Harvard Bus. Rev. 95(6), 46–57 (2017) Si2 Partners. Augmented Reality in Service: Ready for Prime Time?: Management Report 2018. Technology in Service (2018) Li, W.C.Y., Nirei, M., Yamana, K.: Value of data: there’s no such thing as a free lunch in the digital economy. In: 6th IMF Statistical Forum, Washington, DC (2019) Zhu, H., Madnick, S.E.: Finding new uses for information. MIT Sloan Manag. Rev. 50, 17–21 (2009)

Chapter 18

An Investigation of Weighted Neural Networks for Rolling Bearing Fault Classification Under Uncertain Speed Condition Lun Lin, Yimin Shao, Xiaoxi Ding, and Liming Wang

Abstract The fault recognition of bearing under uncertain speed condition is an important task for rotating machinery health monitoring. Since the speed shows a serious influence on the intrinsic characteristics of the acquired vibration signal, there is a significant difference for the characteristic distribution of the vibration signal under different speed conditions, and thus will lead a high misjudgment rate for fault identification of rolling bearings. In this manner, this paper proposes a method of bearing fault recognition under uncertain speed condition based on weighted neural network. Compared with the traditional network, the proposed architecture builds a speed-insensitive fault identification network by embedding a new unit with the consideration of different speed nodes, through the weight network and hence greatly improves the fault recognition accuracy of bearing faults under uncertain speed condition. Compared with the conventional methods, the identification result shows the feasibility and effectiveness of the proposed method.

18.1

Introduction

Rolling bearings is prone to various faults under complex work conditions, which has great impact on the vibration and quality evaluation of the machines, and even cause safety accidents under complex conditions. Therefore, the research on fault identification method of rolling bearing under complex working conditions has great engineering value and theoretical significance [1]. Because the characteristic distribution of the fault signal cannot be accurately evaluated when the rotational speed is uncertain, the conventional methods of bearing fault identification methods, which focus on the periodic characteristics of the shock pulse signal and the envelope signals, would turn out to be useless. Due to the overlook of the speed effect on characteristics, the recognition accuracy of traditional intelligent fault L. Lin
Y. Shao (&)
X. Ding
L. Wang State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_18

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identification algorithm has a high false positive rate. Therefore, it is necessary to develop a new method for extraction of sensitive features and accurately identifying bearing faults under uncertain speed conditions. Many researchers have down meaning work in this work. Xiaoxi Ding proposed a novel energy-fluctuated multi-scale feature mining approach based on wavelet packet energy (WPE) image and deep convolution network for spindle bearing fault diagnosis under various speeds [2]. The method he proposed heightens the diagnosis accuracy only by complicating the networks, and without a reasonable logical explanation. Yang Dalian also proposed a method for gear fault diagnosis under complex loads based on support vector machine optimized by artificial bee colony algorithm, and gets a satisfactory result [3]. But this method focuses on optimize the identification accuracy by feature selection, and without a view to research the relationship between features and fault identification results. It is effective to use vibration signal to identify the fault of the machinery. The development of the signal process method and feature extraction method improves the efficiency and accuracy of mechanical fault recognition greatly, and makes it possible to identify signal patterns efficiently [3]. The statistical characteristics of signal and spectrum, the digital filter, the Empirical Mode Decomposition and the Wavelet Decomposition are widely used in vibration signal feature extraction. Liming Wang proposed a kind adaptive digital filter for fault detection of spalling in 2019 and obtained ideal result [4]. Xiaoxi Ding extracted the power of the coefficients Wavelet Packet Transform as the feature and use it in rolling element bearing fault classification [5]. Meanwhile, the development of neural network provides a suitable method of describing the complex relationship between signal features and fault types. In this paper, a bearing fault identification method based on weighted Neural networks is proposed. Firstly, ten features in time domain and frequency domain are used to characterize bearing fault signals and establish bearing fault sample sets. Then, the bearing fault identification units corresponding to different speed nodes are constructed, which can realize the identification of bearing faults of corresponding speed conditions accurately. Finally, the fault identification results of multiple units are combined by trained weighted network and thus the bearing faults with unknown speed conditions are identified accurately. The test results obtained by applying this method and other traditional methods of experimental fault signal classification show the feasibility and effectiveness of the proposed method. The rest of paper is structured as follows: Sect. 18.2 presents the basic theory of BP neural network. Section 18.3 proposes the bearing fault identification method of uncertain speed condition based on weight neural network, and the structure and training method of the network are introduced in Sect. 18.4 introduces the bearing fault simulation test data and signal feature extraction method involved in this paper. In Sect. 18.5, the result and effectiveness of the proposed method is analyzed by using experimental data sets obtained under different speed. The conclusions are drawn in Sect. 18.6.

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18.2

209

Basic Theory of Back-Propagation Neural Network

The BP neural network is a full interconnection networks which consist of input layer, hidden layer and output layer. The typical three-layer structure is shown as Fig. 18.1. The training method of BP neural network consists of two processes: the forward propagation of information and the back propagation of error. By adjusting the weights and thresholds between different layers of the network repeatedly, the output error of network reaches the desired accuracy [6]. In the process of forward propagation, the input parameters are passed from the input layer to the output layer through the hidden layer to output the final result. The output function of the i-th node in the hidden layer is described as function (18.1). oj ¼ /ð

M X

xij xj þ hi Þ

ð18:1Þ

j¼1

The output function of the i-th node of the output layer can be written as function (18.2). q M X X ok ¼ w½ xki /ð xij þ hi Þ þ ak  i¼1

ð18:2Þ

j¼1

Where xj is the input of the input layer node j, xij is the weight coefficient between the hidden layer node i and the input layer node j, xki is the weight coefficient

Fig. 18.1 The typical three-layer structure of BP neural network

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between the hidden layer node i and the output layer node k, hi is offset variable of the hidden layer node i, ak is the offset variable of the output layer node k, uð xÞ and Uð xÞ is the output function of the hidden layer and the output layer respectively. In the process of back propagation of error, the error calculation function of the network is shown as Eq. (18.3). Ep ¼

P X L 1X ðT p  opk Þ 2 p¼1 k¼1 k

ð18:3Þ

where T is the network training target, and O is the actual output result. The weight correction amount and the threshold correction amount of output layer and hidden layer are calculated by Eq. (18.4)–(18.7). Dxkj ¼ g

P X L X

q M X X ðTkp  Opk Þ
w0 ð wki /ð xij xj þ hi Þ þ ak Þ
yi i¼1

p¼1 k¼1

Dhk ¼ g

P X L X

ðTkp  Opk Þ
w0 ð

q X

p¼1 k¼1

Dxij ¼ g

P X L X p¼1 k¼1 M X 0


/ð

wki /ð

q X i¼1

M X

xij xj þ hi Þ þ ak Þ

ð18:5Þ

j¼1

i¼1

ðTkp  Opk Þ
w0 ð

ð18:4Þ

j¼1

wki /ð

M X

xij xj þ hi Þ þ ak Þ
xki

j¼1

xij xj þ hi Þ
xj

ð18:6Þ

j¼1

Dhi ¼ g

P X L X p¼1 k¼1 M X 0


/ð

ðTkp  Opk Þ
w0 ð

q X i¼1

M X wki /ð xij xj þ hi Þ þ ak Þ
xki j¼1

xij xj þ hi Þ

ð18:7Þ

j¼1

18.3

Weighted Neural Network Towards Feature Identification Under Uncertain Speed Condition

In this paper, the fault identification units of different speed nodes are embedded by a weighted unit. Thereby, the influence law of the speed on the signal characteristics is considered and a speed-insensitive weighted fault identification network structure is constructed.

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Fig. 18.2 The architecture of weighted neural network of bearing fault identification

The process of the method of bearing fault identification of uncertain speed condition proposed in this paper is shown as Fig. 18.2. First, the bearing fault signal is preprocessed and the signal features are extracted. Next, input the feature vector into the trained fault identification units respectively, and the results of different units are obtained. Then, the signal feature vector is input into the weight network model, and the results of different units are adjusted by using the output weight vector. Finally, the final recognition result is obtained through the decision summation node. Some details about this weighted neural network architecture is introduced in following sections.

18.3.1 Structure and Optimization Process of the Identification Units The structure of the fault identification units with multiple speed nodes basically adopts the traditional BP neural network. The input of the networks is bearing fault features, and the output is the assignment probability of different fault types to which the input signal sample belongs. In the final output level, the input sample belongs to the type of fault which shows the biggest assignment probability. The structure parameters of bearing fault identification units corresponding to different speed nodes are mainly optimized by two steps. Firstly, multiple speeds are selected as the speed nodes. Then, the bearing fault signal samples

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corresponding to different speed nodes are selected as the training sets of the corresponding fault identification units.

18.3.2 Structure and Optimization Process of the Weighted Calculated Unit The model of weighted network unit is a 4-layer neural network consisting of input layer, hidden layer, weight calculated layer, and decision fusion layer. The training process of the weighted network can be listed as follows: Input the sample sets of the different speed nodes to fault identification units and the recognition results of each sample by the fault recognition units are obtained. The identification result of the different fault identification units of each sample and the features of the samples are combined as the training data sets of weighted calculated network. Using the training data sets to optimize the coefficient of network cyclically until the required training error or the maximum training times are reached.

18.4

Experiments Data and Feature Extraction

18.4.1 Data Acquisition The test data of the bearing fault simulation test bench of the Electrical Engineering Laboratory of the Case Western Reserve University was used as the verification data of the proposed method in this paper [7]. The structure of the test bench was shown as Fig. 18.3, which consists of electric motor, torque sensor and power tester. The support bearing of the motor shaft was set as fault bearing, and the type of the bearing on the driven end and the fan end was SKF6205 and SKF6205 respectively. Three vibration acceleration sensors were mounted on the base, the fan end and the drive end respectively. In this paper, the vibration signal of the fan end was used as the bearing fault signal samples. The sampling frequency was 12 kHz, and the fault bearing model was SKF6203. Four faults (normal, outer race defect, inner race defect and ball defect) were set in the experiment. The damage sizes of the fault bearings were 0.007 in. which were formed by electrical discharge machining. The test included four rotation speeds of 1730 rpm, 1750 rpm, 1772 rpm, and 1797 rpm. The specific experimental information is shown in Table 18.1

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Fig. 18.3 Bearing fault experiment system

Table 18.1 Details of bearing fault experiment Bearing type

Fault type

Fault size (inch)

Speed (rpm)

Sampling frequency (kHz)

SKF6203

Normal

0

1750, 1772,

12

Inner race defect Outer race defect Ball defect

0.007

1730, 1797 1730, 1797 1730, 1797 1730, 1797

1750, 1772,

12

1750, 1772,

12

1750, 1772,

12

0.007 0.007

18.4.2 Feature Extraction In this paper, the vibration signals are characterized by nine statistical features which are used as the input of the fault identification networks. The nine signal features include five time-domain features (RMS, P2P, Kr, If, Cf) and five frequency-domain (F3, F4, FM0, FM4, M6A) [8, 9], The calculation formulations of these features are shown as Table 18.2. In which, N means the length of vibration signal, Sk(k = 1, 2, ….. K) is the spectra of signal, K is the number of spectral lines. dn is the differential signal which only include high-order sideband components and noise components in the original vibration signal. fp1 and fp2 represent the average frequency and frequency second-order moment of the signal spectrum respectively, the calculation formulations are shown as Eq. (18.8) and Eq. (18.9).

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Table 18.2 The calculation formulations of feature extraction Label F3

Function F3 ¼ ½

K P

pffiffiffiffiffi ðSk  fp1 Þ =Kð fp2 Þ3

Label RMS

Function sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N P xrms ¼ ð x2n Þ=N

pffiffiffiffiffi ðSk  fp1 Þ4 =Kð fp2 Þ2

P2P

xP2P ¼ maxðxÞ  minðxÞ

Kr

xskew ¼ N1

3

k¼1

F4

F4 ¼ ½

K P

n¼1

k¼1

FM0

N P

FM0 ¼ PPx =

Ph

n¼0

FM4

FM4 ¼ ½N

N P

 4 =½½ ðdn  dÞ

n¼1

M6A

M6A ¼ ½N2

N P n¼1

N P

ðdn  dd Þ2 2 

If

N P

pffiffiffiffiffiffiffi =ððxn  xÞ= xvar Þ4

n¼1

I ¼ xmax =ð

N P i¼1

n¼1

 6 =½½ ðdn  dÞ

N P

 2 3  ðdn  dÞ

Cf

x2i Þ=N

Cf ¼ xmax =xrms

n¼1

K X fp1 ¼ ½ Sk =K

ð18:8Þ

k¼1

fp2 ¼ ½

K X

ðSk  fp1 Þ2 =ðK  1Þ

ð18:9Þ

k¼1

The above features were extracted from the 640 fault signal samples which obtained from the bearing fault simulation test. And then, the bearing fault signal feature sample sets were obtained which were used in training and testing of the bearing fault identification network model.

18.5

Results and Analysis

18.5.1 Results of Fault Identification Units Under Different Speeds Figure 18.4 shows the four type fault signals of 1797 rpm in experiment. We can see there is some difference between different fault signals but the difference is not so clear. On the one hand, we can find from the result which is shown as Table 18.5 that the features distribution of samples under the speed of 1750 rpm is different from other speeds greatly by advance analysis. On the other hand, the speed of 1730 rpm and 1797 rpm are the boundary of the speed conditions. As a result, this paper selects three speeds of 1730 rpm, 1750 rpm and 1797 rpm as the speed nodes. And the feature samples of the three speed nodes are chosen from the fault feature sample sets which constructed in Sect. 18.4 as the training and testing sets of the

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a normal signal

b inner race defect signal

c outer race defect signal

d ball defect signal Fig. 18.4 Four type fault signals of 1797 rpm

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Table 18.3 The training and testing results for identification units Sample sets

Number of training sample

Recognition accuracy

Number of testing sample

Recognition accuracy

Set1 (1730 rpm) Set2 (1750 rpm) Set3 (1797 rpm)

100 100 100

100% 100% 100%

60 60 60

100% 96.67% 100%

Fig. 18.5 Bar chart of training and testing results of multiple identification units

corresponding fault identification unit. The number of the training and testing samples is 75 and 45 respectively. Table 18.3 and Fig. 18.5 show the training and testing results of the fault identification units corresponding to the three speed nodes. The identification accuracy of the three identification unit for their training sets has reached 100%. And in addition to the speed node of 1750 rpm, the fault identification units of the other two speed nodes have a recognition accuracy of 100% for their testing sets. It can be seen that the traditional BP neural network has high recognition accuracy of the bearing fault in the single speed conditions.

18.5.2 Results of Weighted Network Under Various Speeds The fault identification accuracy of the weighted network model for the testing and training sets are shown as Table 18.4 and Fig. 18.6. Under uncertain speed conditions, the average recognition accuracy of the training and testing set by using the bearing fault identification method based on the weight neural network is 100% and

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Table 18.4 Training and testing results of weighted network model Sample sets

Number of training samples

Recognition accuracy

Number of testing samples

Recognition accuracy

Set1 (1730 rpm) Set2 (1750 rpm) Set3 (1797 rpm) Average

80 80 80

100% 100% 100% 100%

80 80 80

100% 100% 98.33% 99.44%

Fig. 18.6 Bar chart of training and testing results of weighted network mode

99.44% respectively, which means the proposed method has a satisfied identification performance for the bearing fault samples.

18.5.3 Comparison Between Different Methods Under Different Speeds To verify the superiority of the proposed method compared with the traditional bearing fault identification method, the bearing fault signal at the speed of 1772 rpm was selected to simulate the sample of unknown speed condition. And together with the fault signal samples of the other three speed nodes as the analysis sample sets of the two fault identification model. The fault recognition results of the proposed method and the results of the traditional method based on BP networks are shown as Table 18.5 and Fig. 18.7. It can be seen that the proposed method has high identification accuracy for all testing sets, the highest accuracy and average accuracy is 100% and 98.96% respectively. The average identification accuracy of traditional BP method is 85.86%, and the minimum level is only 63.33%. Compared with the traditional

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Table 18.5 The identification accuracy of different methods

Method1 (Traditional BP network) Method2 (Proposed weighted network) Improved accuracy

Sample set1 (1730 rpm)

Sample set2 (1750 rpm)

Sample set3 (1772 rpm)

Sample set4 (1797 rpm)

Average accuracy

90%

63.33%

90%

100%

85.83%

100%

100%

97.5%

98.33%

98.96%

+10%

+36.36%

+7.5%

−1.67%

+13.13%

Fig. 18.7 Comparison identification accuracy of different methods

method, the average accuracy of bearing fault identification of uncertain speed conditions is improved 13.13% by the proposed method. Some valuable phenomena of Table 18.5 are explained as follows: Compared with the data sets of the other speed conditions, the data set of 1772 rpm is used to simulate the bearing fault data of unknown speed conditions and without used to train the network model. Therefore, the proposed method has a lower recognition accuracy for the test data set at 1772 rpm, which is in line with the actual logic. The traditional method based on BP networks has a fault identification accuracy of 90% for the sample set of 1772 rpm speed condition, which is higher than the average level 85.83% of the method. The reason is that the samples of 1772 rpm speed condition have a certain similarity with the samples of 1797 rpm, and the

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(a) The clustering diagram of identification result of traditional BP neural network

(b) The clustering diagram of identification result of weighted neural network Fig. 18.8 The clustering diagrams of fault identification results about the two methods (label1: inner race defect/label2: ball defect/label3: outer race defect/label4: normal)

traditional method has the highest identification accuracy of 100% about the samples of 1797 rpm speed condition. Because the features of fault signal change obviously under the speed of 1750 rpm and the traditional BP network has not considered the influence of the speed to signal features, the identification accuracy of traditional BP method for the sample set of 1750 rpm speed is unsatisfactory (63.33%). But because the influence

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of speed was considered by the weighted network, the method proposed in this paper with high recognition accuracy of 100% for the samples of 1750 rpm speed. The clustering diagrams of fault identification results of two methods are shown as Fig. 18.8. There is serious fuzziness between label 3 and label 4 in Figure a, which indicates that the method based on traditional BP network cannot identify all faults of the bearing accurately. And there are obvious boundaries of the four fault categories in figure b, which means that the proposed method based weighted network can accurately identify the fault of all bearing signal samples.

18.6

Conclusion

By embedding the bearing fault identification units of multiple speed nodes and combining the results of fault identification units by weights, the weighted network architecture of bearing fault identification has constructed. Due to the construction of multiple fault identification units for the key speed nodes, the proposed method has higher recognition accuracy for the bearing fault data samples of the key speed nodes than the traditional BP network. Besides, the proposed method based on the weight network model also has good recognition accuracy for data sets that are not included in network training sets, which is because the final result is obtained by linear weighting of the results of multiple fault identification units. Therefore, the fault method based on the weight network model can identify the bearing faults of uncertain speed conditions accurately. As future directions of this work, the method of speed nodes selection can be further evaluated by using mathematical statistical knowledge, and by replacing the liner fusion function of the weighted network with an improved nonlinear fusion network method, the efficient of the weighted neural network may be improved. Acknowledgements This work was supported by the National Natural Science Foundation of China under Contract (No. 51475053), National Natural Science Foundation of China under Grant. (No. 51805051), in part by the China Postdoctoral Science Foundation (No. 2017M622960).

References 1. Liu, J., Shao, Y.: Dynamic modeling for rigid rotor bearing systems with a location defect considering additional deformations at the sharp edges. J. Sound Vib. 98(23), 84–102 (2017) 2. Ding, X., He, Q., Luo, N.: Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis. J. IEEE Trans. Instrum. Meas. 99, 1–10 (2017) 3. Yang, D., Liu, Y., Li, X.: Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. J. Mech. Mach. Theory 90, 219–229 (2015) 4. Wang, L., Ye, W., Shai, Y., Xiao, H.: A new adaptive evolutionary digital filter based on alternately evolutionary rules for fault detection of gear tooth spalling. J. Mech. Syst Signal Process. 118, 645–657 (2019)

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5. Ding, X., He, Q, Luo, N.: A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification. J. Sound Vib. 335, 367–383 (2015) 6. Liu, B., Guo, H.: Super Learning Manual of Neural Network. Posts&Telecom Press, Beijing (2017) 7. Bearing Data Center. http://csegroups.case.edu/bearingdatacenter/pages/download-data-file. Accessed March 2017 8. He, Z., Chen, J., Wang, T.: Theories an Applications of Machninery Fault Diagnostics. Higher Education Press, Beijing (2012) 9. Theodoridis, P., Kourtroumbas, K.: Pattern Recognition, 4th edn. Academic Press, Burlington (2008)

Chapter 19

An Improved VMD Approach for Sensitive Feature Extraction in the Application of Gears Fault Classification Mingkai Zhang, Yimin Shao, Xiaoxi Ding, and Liming Wang

Abstract Since the raw vibration signals of gears show complex non-stationary characteristic and are always contaminated by heavy background noise. There is an obstacle for sensitive feature extraction which will cause an incorrect identification for gear faults. Variational mode decomposition (VMD) is a widely used technique which can obtain the intrinsic information embedded in the raw signals via non-recursive decomposition. By adopting this technique, the sensitive features, which magnify the differences between health statuses, can be easily obtained. However, the outstanding performance of VMD would be weaken by the irrational setting of its key parameters. To address this issue, this paper proposes a new coarse-to-fine decomposition strategy for VMD, which focuses on the relationship between pattern recognition accuracy and parameter selection in a two-step searching way. With a sound iterative process, the optimal decomposition and sensitive features of the fault signal can be obtained. The results show that the proposed method yields a better classification accuracy based on the improved VMD.

19.1

Introduction

As a complex system, the working state of the transmission system is closely related to its various components. Among these components, gears are a vital part whose health status usually have an immense impact on the movement and power transmission of the whole system. To avoid the needless downtime caused by gears failure and identify the specific fault type, intelligent diagnosis is put forward to reveal the gear health status. However, the non-linear nature of gear fault signal makes it difficult to take effective measures to extract the suitable features which can amplify the difference between fault types. M. Zhang
Y. Shao (&)
X. Ding
L. Wang State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_19

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Currently, variational mode decomposition (VMD) has received widespread attention due to its superior characteristics in dealing with non-stationary and non-linear signals. However, an obvious non-determinism in parameter setting for fault diagnosis exists in VMD algorithm. To find a reasonable parameter setting method, Li and Chen [1] proposed an attractive method which can determine the K levels through the extreme points of the spectrum and combine the IMF with similar correlation coefficient, but this method relies heavily on human intervention and lacks adaptability. Jiang et al. [2] proposed a coarse to fine strategy which can determine both the K and a using the kurtosis index as the standard for recursive search, and the decomposition effect is easily affected by the error. It is lack of robustness to select the signal statistic index as the rationality index of the decomposition parameter. Moreover, the above methods can’t cope well with the parameter selection when facing to different fault types recognition. The feature extraction of gear fault signal is an important process which can distinguish between different fault states [3]. Compared with the gear in normal status, when a fault presents, the difference in fault form and location will be directly reflected in the energy distribution of vibration signal. Ding et al. [4] extracted a 2-D WPE image as a sensitive feature to reflect the energy flow under different fault conditions. It proves that the energy can be used as an index in fault diagnosis. Therefore, after the signal processed by VMD, the energy of different IMF which locates in different frequency bands can obviously reflect the difference between faults. In this paper, a new decomposition strategy which can locate the optimal decomposition parameters of VMD is proposed. First, this decomposition strategy is applied to the four type fault signals. Second, the energy features of each frequency band are extracted and put into the KNN classifier. Then, the optimal parameter which related to the best recognition accuracy will be obtained. The method based on VMD-ER-KNN is applied in the experiment and the results show that the proposed method yields a better classification accuracy based on the extracted sensitive features and the new parameter selection strategy.

19.2

The Theoretical Background and Related Technologies

19.2.1 A Brief Introduction of VMD VMD is a novel proposed technique in the field of signal processing which regarded as an enhancement of EMD [5]. It employs a variational constraint model to replace the structure of recursive sifting fundamentally. Hence, VMD can split a multi-component signal f ðtÞ into K intrinsic components uk(t), k 2 ð1; . . .; K Þ with obvious sparsity property concurrently. The structure of this variational constraint

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model inherits the merits of Hilbert transform and Wiener filtering and can be expressed as Eq. (19.1). ( min

fuk g;fxk g

s:t:

X

2 )    X   j jx t k @t dðtÞ þ   uk ðtÞ e   pt 2 k

ð19:1Þ

uk ¼ f

k

where fuk ðtÞg ¼ fu1 ðtÞ; . . .; uk ðtÞg, denotes the IMF sets and fxk g ¼ fx1 ; . . .; xK g are the center frequencies corresponding to the kth mode. To make the aforementioned problem unconstrained, the quadratic penalty term a and Lagrangian multiplier k are introduced. Then the original constrained problem can be transformed into Eq. (19.2).  2   X    @t ðdðtÞ þ j Þ  uk ðtÞ ejxk t    pt 2 k 2 *  +   X X   þ f ðtÞ  uk ðtÞ þ kðtÞ; f ðtÞ  uk ðtÞ   k k

Lðfuk g; fxk g; kÞ ¼ a

ð19:2Þ

2

Here, the alternate direction method of multipliers (ADMM) is employed and the main problem can be addressed by finding the saddle point of Eq. (19.2). This searching process essentially includes some independent steps called sub-optimization, and the two main sub-optimization formulas of this algorithm can be described as follows: ^unk þ 1 ðxÞ

¼

^f ðxÞ 

P i6¼k

^ui ðxÞ þ

^kðxÞ 2

1 þ 2aðx  xk Þ2 R1

ð19:3Þ

xj^uk ðxÞj2 dx

xnk þ 1 ¼ 0R1

ð19:4Þ j^uk ðxÞj2 dx

0

Through iterative updating of the Eqs. (19.3) and (19.4) in the frequency-domain, each IMF and their center frequencies can be obtained. Meanwhile, the Lagrangian multiplication operator should also be updated as Eq. (19.5). ^kn þ 1 ðxÞ ¼ ^kn ðxÞ þ sð^f ðxÞ 

X

^ unk þ 1 ðxÞÞ

ð19:5Þ

k

After the iteratively updating of the unk þ 1 , xnk þ 1 and kn þ 1 , VMD algorithm provides two different manners to judge the decomposition results. One is the

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number of iterations exceeds a preset hard threshold N, and another one is the convergence accuracy satisfied the requirements, which can be expressed as Eq. (19.6). K X ^unk þ 1 ðxÞ  ^unk ðxÞ   \e ^un  k¼1

ð19:6Þ

k

19.2.2 Signal Feature Extraction Based on Energy Ratio Compared with normal gears, when the gears fail, the energy values and distribution vary due to the different forms and locations of the faults. That means the energy of the signals will change in different frequency bands under different fault conditions. Therefore, VMD is employed to split the vibration signals of different types of gear fault into K narrow-band components which located in different frequency bands, then calculate the energy of each IMF. The obtained energy distribution can be used as a feature to amplify the difference between health statuses [6]. The feature which based energy ratio can be constructed as follows: • VMD is used to decomposed the fault signal into K components, then use the FFT to obtain the frequency information uk ðf Þ about each uk ðtÞ. • Calculate the energy of each IMF, which can be expressed as EK . EK ¼

N X

juk ðnÞj2

k ¼ 1; 2; . . .; K

ð19:7Þ

n¼1

• Normalized the energy of each modal component and obtain the characteristic vector P. P ¼ ½E1 =E; E2 =E; . . .:; EK =E E ¼

K X

Ek

ð19:8Þ

k¼1

19.2.3 Parameters Selection Methods There are two key parameters in VMD method, namely, K and a. The former one can control the number of decomposition layer and another one has a significant influence on the bandwidth of the filter embedded in VMD method. The bandwidth of each IMF will become smaller as a increases. Considering the optimal

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combination of decomposition layers and a should be suitable for the different fault types. Therefore, in order to find the optimal parameters, the decomposition parameter is first selected roughly according to a certain step size, and the different fault signals are decomposed coarsely under these parameters. Then, the energy ratio of each IMF which employed as an input feature in KNN classifier is extracted. The complete selection methods can be expressed as Fig. 19.1. It is worth noting that the parameter initialization of the VMD has a certain influence on the decomposition result. Therefore, the parameter initialization range for the coarse-to-fine decomposition strategy should be well preset according to the signal itself.

Fig. 19.1 The coarse-to-fine strategy based on recognition accuracy

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Experimental Analysis

19.3.1 Data Acquisition To evaluate the effectiveness of this method, a gearbox fault simulation platform was used to simulate the different gear faults and achieve the acquisition of fault vibration signals, as shown in Fig. 19.2. This platform includes a two-stage gearbox, a motor, a magnetic brake which used to provide loads, a signal acquisition instrument and two acceleration sensors. This data acquisition system can obtain the faulty signal though the sensors which located on the bearing cover in the right end of the middle and input shafts. Four types of gear were mounted in turn on the intermediate shaft to simulate the different states of gear, as shown in Fig. 19.3. During the data acquisition process, the sampling frequency was set to 20,480 Hz and output speed was set to 15 Hz. Then by adjusting the magnetic brake, the load torque was set to 20 N
m. The experimental steps are as follows: • Step 1: First, set the acquisition parameters as described above and start the motor. • Step 2: Wait until the gearbox runs stably and the signal acquisition starts. Here, the length of each fault signal is 20 s and each sample included 4,096 points. • Step 3: Replace the faulty gear and repeat the above steps for signal acquisition. The raw signal of four different fault types of gears and their frequency spectral can be observed as Fig. 19.4.

19.3.2 Feature Extraction Based on VMD These four kinds of fault signals collected under the working conditions mentioned above are subjected to VMD decomposition to reveal the energy characteristic. And the feature vectors about each state of the gear are calculated. The energy ratios

Fig. 19.2 Gearbox fault simulation platform for gear faults

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Fig. 19.3 Four type gear faults

Fig. 19.4 Raw signal and frequency spectral of four different gear fault types

Fig. 19.5 Energy characteristics of four fault decomposition components under different parameter combinations

between different IMFs are compared, as shown in Fig. 19.5. As displayed in Fig. 19.5, there is a significant difference in the energy distribution between the different fault states of the gear, and this difference is amplified as the decomposition parameters change. This means that combining VMD with energy features will be helpful for gear fault type identification.

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19.3.3 Coarse-to Fine Decomposition Process As we can see from the Fig. 19.4, different type of fault has varying spectral distributions, which mainly include the meshing frequency of the two-stage gears of the gearbox and other components. We can initialize the decomposition layer K at a range from 2 to 8. In addition, according to our experience, in this paper, the bandwidth parameter can be set as [500, 5000] in a step size as 500. After this initialization, the coarse decomposition stage can be applied in the sample sets of the four type raw signals, and then the energy ratio of each modal components can be obtained. The sample set of the all fault types is comprised of 400 samples, then in this paper, the first 200 samples are selected as training samples for KNN classifier training, and the other samples are used as tag data to test whether the VMD achieved optimal decomposition. In theory, when the VMD reaches the optimal decomposition, it should achieve higher recognition accuracy. The result of the coarse decomposition stage is expressed as Fig. 19.6. It can be seen from the curve in Fig. 19.6 that as the number of decomposition layer increases, the recognition accuracy fluctuates, which indicates that the resolution of the frequency band’s energy is increased. And the weak difference between different fault types which reflected on the frequency bands can be more accurately obtained. In addition, when using the same number of decomposition layers, the fluctuation of the curve shows the effect of the bandwidth parameter. Therefore, in this stage, we can obtain a relative optimal parameter combination as (7, 4500), under this parameter, the recognition accuracy can reach 96%. In the coarse decomposition stage, after the approximate range of the decomposition layer K and the bandwidth parameter is determined. Then the bandwidth parameter should be finely adjusted to obtains the optimal decomposition parameter. Here, the step size is 100. Finally, we can get the optimal parameter as Fig. 19.6 Recognition accuracy obtained during coarse decomposition stage

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(7, 4900), and the recognition accuracy can reach 97%. The decomposition result which obtained by the optimal parameters as show in Fig. 19.7. As demonstrated in Fig. 19.7, this two-step searching strategy can have a great effect on the modal decomposition, after this method, the intrinsic characteristic of the raw signal can be revealed. First, there are significant differences in different frequency bands about different fault types, Then, there is no aliasing phenomenon between modal signals. And it should be noted that this experiment’ signals were processed under the computation scenario of MATLAB 2016a on a laptop (2.50-GHz CPU, and 12-GB memory).The extraction of samples’ energy features takes some time, because the decomposition of samples by VMD is slow, however the coarse to fine procedure takes nearly 30 s, which is suitable for practical application.

Fig. 19.7 Decomposition result of the optimal parameter

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Conclusions

In practical applications, the use of appropriate intelligent diagnostic measures for gear faults is of great significance for the operation of the transmission system. In this paper, a method based on VMD-ER is proposed to solve the fault type identification problem. The original signals of different gears status are decomposed into different frequency bands by VMD, and the normalized energy in each frequency band is obtained. By combining the amplification advantage of the energy feature for the difference between different faults and the decomposition ability of the VMD for the signal modal component in a two-step strategy, the recognition accuracy of the gear fault is significant improved. It should be noted that VMD has some merits in the process of practical signal than other signal process methods, which was proved in some research [7, 8]. However, it should be noted that the problem exists in VMD algorithm can not be entirely solved only by this optimization strategy. The theoretical basis of the algorithm of VMD should be deeply studied in the future, and its decomposition effect can be achieved by optimizing its structure. Furthermore, since the method proposed in this paper can effectively reveal the sensitive band, therefore the research on the application of gear compound fault of this method will be carried out in the future research. Acknowledgements This work was supported by the National Natural Science Foundation of China under Contract (No. 51475053), National Natural Science Foundation of China under Grant (No. 51805051), in part by the China Postdoctoral Science Foundation (No. 2017M622960).

References 1. Li, Z.P., Chen, J.L., Zi, Y.Y., Pan, J.: Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of highspeed locomotive. J. Mech. Syst. Signal Process. 85, 512–529 (2017) 2. Jiang, X.X., Wang, J., Shi, J.J., et al.: A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines. J. Mech. Syst. Signal Process. 116, 668–692 (2019) 3. Chen, Z.G., Zhai, W.M., Wang, K.Y.: Vibration feature evolution of locomotive with tooth root crack propagation of gear transmission system. J. Mech. Syst. Signal Process. 115, 29–44 (2019) 4. Ding, X.X., He, Q.B.: Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis. J. IEEE Trans. Instrum. Meas. 66(8), 1926–1935 (2017) 5. Dragomiretskiy, K., Zosso, D.: Variational Mode Decomposition. J. IEEE Trans. Signal Process. 62(3), 531–544 (2014) 6. Chen, X.J., Yang, Y.M., Cui, Z.X., et al.: Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy. J. Energy 174, 1100–1109 (2019) 7. Bi, F.R., Li, X., Liu, C.C., et al.: Knock detection based on the optimized variational mode decomposition. J. Meas. 140, 1–13 (2019) 8. Zhang, X., Miao, Q., Zhang, H., et al.: A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. J. Mech. Syst. Signal Process. 108, 58–72 (2018)

Chapter 20

Coupled Vibration Analysis of a Bevel Geared Rotor-Bearing System Zhen Liu , Fucai Li , and Bo Jing

Abstract Bevel gears are widely used in various industrial structures including wind turbines, helicopter and ships. They can transmit rotational motion between two intersecting shafts which are usually perpendicular to each other. In recent years, the dynamic behavior of the gear meshing process and gear-rotor systems with parallel shafts were investigated quite intensively. However, little attention has been paid to the dynamic analysis of the bevel geared rotor-bearing systems, especially the coupled vibration of the intersecting rotors. In this paper, the dynamic analysis of a rotor-bearing system consisting of a horizontal shaft, a vertical shaft as well as a pair of bevel gears is studied. Firstly, the dynamic model of the system is derived considering the effects of unbalance, nonlinear meshing stiffness and transmission error. Based on the governing equations, the coupled axial-lateral-torsional vibration of the rotor system is then investigated. The modal analysis is carried out and the dynamic responses of the system in some cases are calculated by using numerical method. The results indicate that the excitation at one of the shafts will influence the vibration of the other shaft therefore it is necessary to analyze the modal characteristics of the whole bevel geared rotor-bearing system. The study is beneficial in fault diagnosis and condition monitoring of a certain type of the bevel geared rotor-bearing system.

Z. Liu
F. Li (&)
B. Jing State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China e-mail: [email protected] Z. Liu e-mail: [email protected] B. Jing e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_20

233

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Introduction

Gears are one of the important mechanisms for motion and power transmission. The spiral bevel gears can transmit rotation motion between two intersecting shafts and are widely used in various industrial structures including wind turbines, helicopter and ships due to the excellent performances such as smooth transmission and higher load carrying capacity. Although they have above advantages, the vibration problems of the spiral bevel geared rotor system are still serious and the demand for better NVH performance is increasing along with the rotating speed of the rotor growing higher. The dynamic behavior of gear transmission system has been of concern these years. In gear modeling process, the lumped parameter modeling method is commonly used, where the gears are represented by two rigid disks connecting by a spring and a damping element in the direction of the meshing line [1]. The stiffness of the spring can be linear [2] or time-varying since the contact number and position of the tooth will change with rotation of the gears [3]. To evaluate time-varying meshing stiffness, the potential energy method was firstly proposed by Yang and Lin [4] and then further refined by Tian [5] and Zhou et al. [6]. Other researchers use a square waveform or its Fourier series form to approximate the fluctuations of meshing stiffness. For instance, Abboudi et al. [7] expressed the extremums and fluctuation period of mesh stiffness by average stiffness and contact ratio. The influence of shaft and bearing flexibilities should also be considered in the model besides the gears [8–12]. Shafts and bearings are modeled as beam elements and springs respectively, and then assembled as motion equations of the whole geared rotor-bearing system by using finite element method (FEM). Many researches are interested in the coupled vibration of the system. Lee et al. [13] examined the natural frequencies and mode shapes of a turbo-chiller system, and derived that lateral and torsional vibration are coupled under certain conditions. In helical gear pairs the axial degree of freedom (DOF) can’t be neglect. Kubur et al. [9] proposed a model of a multi-shaft helical gear system to study the free and forced vibrations of the system. Some modes exhibit coupled lateral-axial-torsional motions. Zhang et al. [14] modeled a compressor by four pairs of helical gears and analyzed the change of meshing force affected by unbalance, external load and gear transmission error. From these researches one can conclude that there will be internal excitations caused by time-varying stiffness or transmission error in geared rotor-bearing systems and thus result in the vibration of the system. The above researches focused on the dynamic behavior of spur or helical gear-rotor systems with parallel shafts. In recent years some attention has been paid to the dynamic analysis of the bevel geared rotor-bearing system. Li et al. [15, 16] carried out the dynamic analysis of spur and spiral bevel geared rotor-bearing system successively to discuss the coupled vibrations. The gear pairs were modeled as two virtual cylindrical gears where the internal excitations were neglected. Yassine [17] studied the influence of some manufacturing defects on the dynamic behavior of a two-stage straight bevel gear system, in which the shafts were

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considered to be massless. In Ref. [18] and [19], Xu et al. built a spiral bevel gear model to investigated the coupled lateral-torsional vibration. The DOF of the model was 8 in which the swing vibrations and the effects of the shafts were neglected. These researches may not fully represent the dynamic characteristics of a bevel geared rotor-bearing system due to the limited number of DOF employed or the neglect of the shafts and bearings. Therefore the main objective of this work is to analyze the coupled dynamic characteristics of a spiral bevel geared rotor-bearing system more precisely. Firstly, the three-dimensional dynamic model of the system consists of two shafts and a pair of spiral bevel gears is built by using lumped parameter modeling method and FEM, considering the effects of unbalance, nonlinear meshing stiffness and transmission error. The modal analysis is carried out to compare the modal characteristics of the uncoupled and coupled model. And the dynamic responses in some cases are solved by using numerical method. This study can be beneficial in fault diagnosis and condition monitoring of the bevel geared rotor-bearing system.

20.2

Dynamic Model and Motion Equations

A typical bevel gear transmission system mainly consists of shafts, bevel gears and bearings. The dynamic models of each element are provided in the following subsections and some general assumptions are used in this study as follows: 1. The meshing force acts on the middle point of intersection line between tooth surface and pitch cone, and is along the common normal direction of the teeth profiles. 2. The teeth contact with each other without separation during the meshing process. 3. The small deformation hypothesis is adopted in the analysis.

20.2.1 Gear Meshing Model The model of a pair of spiral bevel gears with 12 DOF is shown in Fig. 20.1 and the definitions of the symbols are listed in Table 20.1. According to the first assumption, the meshing force Fn acting on the pinion and the gear can be expressed and decomposed as shown in Fig. 20.2, where a and b are the normal pressure angle and mean spiral angle, respectively. The pinion i has left hand teeth and the gear j is right-handed.

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Pinion i (driving gear)

Gear j (driven gear)

δi δ j

yi

yj

θ yi θ zi

oi

θ xj

A

θ zj z j

xi

B

δi δ j

xj

oj

θ xi

zi

θ yj

Projection drawing in y-direction

zi

xj

zj

xi

A

B

The action point of meshing force

(a) Three-dimensional model (left) and its projection drawing in y-direction (right)

yi

rpi oi′

θi + Ωi t

oie

Ωi

Ti

o′j

θ j − Ωj t

y xie

rp j

θ zi

e i

oi

θi

ei

yj

o ej oj

xi

θj

y

ej

θ zj

Ωj

Tj

e j

x ej

xj

(b) Cutaway view A-A (left) and B-B (right) Fig. 20.1 Dynamic model of a pair of spiral bevel gears with 12 DOF

Table 20.1 Definitions of the symbols in the model Symbol

Definition

di ; dj xi ; xj ; yi ; yj and zi ; zj hxi ; hxj ; hyi ; hyj and hzi ; hzj oi ; oi and o0i ; o0i oei ; oej

Pitch cone angle of gears i and j Lateral and axial DOF of gears i and j, respectively Swing and torsional DOF of gears i and j, respectively Gear centers when they are stationary and rotating Centers of gravity of gears i and j

xei ; xej ; yei ; yej

Displacement of oei ; oej

ei ; ej and hi ; hj Xi ; Xi Ti ; Tj rpi ; rpj

Eccentricity and initial phase angle of gears i and j Rotation speed of gears i and j Torques applied to gears i and j Average pitch radius of gears i and j

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Coupled Vibration Analysis of a Bevel Geared Rotor-Bearing System

Fig. 20.2 Force analysis diagram of pinion and gear (left: driving gear; right: driven gear)

Pinion i yi

δi

Fn

Gear j

xi

Fn

δj

α

δi

α β

237

β oi

δj

oj zj

zi

yj xj

Based on the figures, the generalized displacements vector of the gear pair’s centers of gravity oei and oej can be defined as: h iT Xije ¼ xei ; yei ; zei ; hexi ; heyi ; hezi ; xej ; yej ; zej ; hexj ; heyj ; hezj

ð20:1Þ

Thus the relative displacement of the gear mesh along the meshing force due to the vibration of oei and oej can be obtained and expressed as:   pij ¼ xei  ei cos hi þ ei cosðhi þ Xi tÞ  hezi ei sinðhi þ Xi tÞ
q1   þ yei  ei sin hi þ ei sinðhi þ Xi tÞ þ hezi ðei cosðhi þ Xi tÞ þ rpi Þ
q2   þ zei  heyi ðei cosðhi þ Xt tÞ þ rpi Þ þ hexi ei sinðhi þ Xt tÞ
q3      þ xej  ej cos hj þ ej cos hj  Xj t  hezj ej sin hj  Xj t
q7        þ yej  ej sin hj þ ej sin hj  Xj t  hezj ej cos hj  Xj t þ rpj
q8        þ zej þ heyj ej cos hj  Xj t þ rpj þ hexj ej sin hj  Xj t
q9  eij ðtÞ ð20:2Þ Table 20.2 Components of vector Q Component

Value

q1 q2 q3 q4 q5 q6 q7 q8 q9 q10

cosd1 sina þ sind1 sinb sina cosb cosa cosd1 sinb cosa  sind1 sina q3
ei sinðhi þ Xt tÞ q3
ðei cosðhi þ Xl tÞ þ rpi Þ q1
ei sinðhi þ Xl tÞ þ q2
ðei cosðhi þ Xi tÞ þ rpi Þ cosd2 sina þ sind2 sinb sina cosb cosa cosd2 sinb cosa  sind2 sina   q9
ej sin hj  Xj t     q9
ej cos hj  Xj t þ rpj       q7
ej sin hj  Xj t  q8
ej cos hj  Xj t þ rpj

q11 q12

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where ql ðl ¼ 1  12Þ is component of the vector Q and is given in Table 20.2. eij in Eq. (20.2) is the transmission error which can be expressed in the form of Fourier series [14]: 1 X eðtÞ ¼ eave þ el sinðlxm t þ ul Þ ð20:3Þ l¼1

where eave is average transmission error, xm is frequency of gear meshing. el and ul represent the magnitude and phase of transmission error fluctuation, respectively. Since the clearance and impact of gear meshing process are neglected (assumption 2), the meshing force can be written as: Fn ¼ kt
pij þ ct
p_ ij

ð20:4Þ

The meshing stiffness kt is time-varying and is written as a Fourier series form: kt ¼ kave þ

1 X

kl sinðlxm t þ ul Þ

ð20:5Þ

l¼1

where kave is the average meshing stiffness, xm is frequency of gear meshing. kl and ul are the magnitude and phase of meshing stiffness fluctuation, respectively. ct in Eq. (20.4) is the meshing damping which isffi defined as [19]: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi kt ct ¼ 2f 1=mi þ 1=mj

ð20:6Þ

where f is damping ratio. Based on the above model, the governing equation of the bevel gear pair is obtained as follows: 8 mi€xei ¼ q1 Fn > > > > mi€yei ¼ q2 Fn > > > < mi€zei ¼ q3 Fn €e ¼ q4 Fn  Iiz Xi h_ e Iix h > xi yi > > e > € _e > h h I ¼ q F þ I X iy 5 n iz i > yi xi > : €e Iiz h ¼ q6 Fn þ Ti zi

mj€xej ¼ q7 Fn mj€yej ¼ q8 Fn mj€zej ¼ q9 Fn Ijx €hexj ¼ q10 Fn þ Ijz Xj h_ eyj Ijy €heyj ¼ q11 Fn  Ijz Xj h_ exj Ijz €hezj ¼ q12 Fn þ Tj

ð20:7Þ

where ml ðl ¼ i; jÞ are the masses of gears i and j, respectively. Ilx ; Ily ; Ilz ðl ¼ i; jÞ are the moments of inertia about x, y, z axis of gears i and j, respectively.

20

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Considering the relationship between the generalized displacement of oel and ðl ¼ i; jÞ, which is given by: 8 e xi ¼ xi  ei cosðhi þ Xi tÞ þ ei cos hi > > > e > y ¼ yi  ei sinðhi þ Xi tÞ þ ei sin hi > > < ei zi ¼ zi hexi ¼ hxi > > > > > he ¼ hyi > : yie hzi ¼ hzi

  xej ¼ xj  ej cos hj  Xj t þ ej cos hj   yej ¼ yj  ej sin hj  Xj t þ ej sin hj zej ¼ zj hexj ¼ hxj heyj ¼ hyj hezj ¼ hzj ð20:8Þ

the Eq. (20.8) can be rewrote as: 8 mi€xi þ q1 Fn ¼ mi ei X2i cosðhi þ Xi tÞ > > > > > mi€yi þ q2 Fn ¼ mi ei X2i sinðhi þ Xi tÞ > > < m €z þ q F ¼ 0 i i 3 n €hxi þ q4 Fn þ Iiz Xi h_ e ¼ 0 I > ix yj > > > €hyi þ q5 Fn  Iiz Xi h_ e ¼ 0 > I > iy xi > : Iiz h€zi þ q6 Fn ¼ Ti

  mj€xj þ q7 Fn ¼ mj ej X2j cos hj  Xj t   mj€yj þ q8 Fn ¼ mj ej X2j sin hj  Xj t mj€zj þ q9 Fn ¼ 0 Ijx €hxj þ q10 Fn  Ijz Xj h_ eyj ¼ 0 Ijy €hyj þ q11 Fn þ Ijz Xj h_ exj ¼ 0 Ijz €hzj þ q12 Fn ¼ Tj ð20:9Þ

and can be further expressed in matrix form as:   €ij þ Cij þ Gij X_ ij þ Kij Xij ¼ Fij Mij X

ð20:10Þ

In Eq. (20.10), the vector Xij represent the generalized displacement of oi and oj . Mij ; Cij ; Gij ; Kij are the mass matrix, meshing damping stiffness, gyroscopic matrix and meshing stiffness matrix of the gear pair, respectively. Fij is the vector of external force. These vectors and matrices are expressed as follows:  T Xij ¼ xi ; yi ; zi ; hxi ; hyi ; hzi ; xj ; yj ; zj ; hxj ; hyj ; hzj

ð20:11Þ

  Mij ¼ diag mi ; mi ; mi ; Ixi ; Iyi ; Izi ; mj ; mj ; mj ; Ixj ; Iyj ; Izj

ð20:12Þ

Cij ¼ ct
QT
Q

ð20:13Þ

 Gij ¼ diag ½033 ;

0 Iiz Xi

0 Iiz Xi ; ½044 ; Ijz Xj 0

Kij ¼ kt
QT
Q þ ct
QT
Q_

Ijz Xj ;0 0

ð20:14Þ ð20:15Þ

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   Fij 121 ¼ mi ei X2i cosðhi þ Xi tÞ; mi ei X2i sinðhi þ Xi tÞ; 0; 0; 0; Ti iT     mj ej X2j cos hj  Xj t ; mj ej X2j sin hj  Xj t ; 0; 0; 0; Tj

ð20:16Þ

þ kt
QT
eij þ ct
QT
e_ ij

20.2.2 Shaft Model Considering the effects of shear deformation and gyroscopic moment, the shafts of the system are modeled as Timoshenko beams by using FEM. Each element of the shaft model has 2 nodes and 12 DOF, as shown in Fig. 20.3. The mass matrix Ms , stiffness matrix Ks and gyroscopic matrix Gs are presented in Ref. [20].

20.2.3 Bearing Model The bearings in this study are assumed to be rigid and modeled in the form     of springs at translation kxx ; kyy ; kzz and rotation direction khx hx ; khy hy ; khz hz . The off-diagonal terms representing cross stiffness are ignored. Thus the bearing model can be expressed as:   Kb ¼ diag kxx ; kyy ; kzz ; khx hx ; khy hy ; khz hz

ð20:17Þ

20.2.4 Motion Equations of the System The motion equation of the whole system can be obtained by assembling the above models of gear pair, shafts and bearings based on finite element theory, which is written as following matrix form: € þ ðC þ GÞX_ þ KX ¼ F MX

Fig. 20.3 Shaft element with 2 nodes and 12 DOF

ð20:18Þ

yA

θ yB

θ zB

θ zA zA

yB

θ yA zB

A θ xA x A

B θ xB

xB

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where M ¼ Mij þ Ms ; G ¼ Gij þ Gs and K ¼ Kij þ Ks þ Kb represent the system mass, gyroscopic and stiffness matrix, respectively. F is the external load vector of the system model. Besides the time-varying meshing damping matrix, the system damping matrix C also includes a Rayleigh-type damping term given by: Cs þ Cb ¼ 0:05M þ 105 ðKs þ Kb Þ

ð20:19Þ

The generalized displacement vector X is expressed as:  T X ¼ x1 ; y1 ; z1 ; hx1 ; hy1 ; hz1 ; . . .; xN ; yN ; zN ; hxN ; hyN ; hzN

ð20:20Þ

where N is the number of the nodes and the DOF of the system is 6N.

20.3

Numerical Analysis

Figure 20.4 shows a simple bevel geared rotor-bearing system consists of a horizontal shaft and a vertical shaft, each of which is supported on two bearings and carries a spiral bevel gear at the end to form a meshing gear pair. The values of the system parameters are listed in Tables 20.3, 20.4 and 20.5.

20.3.1 Coupled Modal Analysis of the System Since the rotor system is coupled by meshing gear pair, the comparative analysis of the uncoupled and coupled model can be easily achieved by considering whether the meshing stiffness and damping matrices exist or not. The modal strain energy expressed in percentage form are also calculated to determine the dominant modes. Figures 20.5 and 20.6 present the first eight normalized mode shapes where vertical axis represents the generalized displacement and horizontal axis represents the node

Fig. 20.4 A simple bevel geared rotor-bearing system

Shaft 2 20 Bearings

Node number 13 3 Shaft 1

8 10

11

Gear j z j (driven gear)

Pinion i (driving gear) zi

xi

xj

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Table 20.3 Parameters of the gears

Parameters

Pinion i

Gear j

Teeth number Pitch cone angle (°) Mass (kg) Polar moments of inertia (kg
m2) Diameter moments of inertia (kg
m2) Average pitch radius (m) Normal pressure angle (°) Mean spiral angle (°)

15 18.06 3.71 0.0062 0.0041 0.0528 20 35

46 91.94 41.70 0.7100 0.3700 0.1619

Table 20.4 Parameters of the shafts

Parameters

Shaft 1

Shaft 2

Length (m) Diameter (m) Density (kg/m3) Modulus of elasticity (GPa) Poisson’s ratio Shear coefficient

0.35 0.05 7850 210 0.3 0.9

0.46 0.10

Table 20.5 Parameters of the bearings

Parameters

All

kxx ; kyy (N/m) kzz (N/m) khx ;hx ; khy ;hy ; khz ;hz (N/m)

2  108 5  108 2  106

number, and the calculation results of natural frequencies and modal strain energy of the two models are given in Tables 20.6 and 20.7. The figures and tables reveal that in uncoupled model, the mode of the system is mainly expressed as a single form, such as pure torsional modes at 148.55 Hz and 525.07 Hz, pure lateral modes at 274.66 Hz, 275.58 Hz, 468.74 Hz, 473.50 Hz and 808.28 Hz, and pure axial mode at 591.69 Hz. The coupled vibration happens when the system is connected by meshing bevel gear pair. In the 1st mode the vibration in lateral and torsional direction occur on the both two shafts. The 2nd and 3rd modes are the same lateral modes of vertical shaft in uncoupled model and the lateral vibration causes torsional mode of horizontal shaft in coupled system. In the 4th, 5th, 7th and 8th modes of coupled model, the coupled lateral-axial-torsional vibrations happen in one of the shafts, comparing with the 4th, 5th and 8th modes of uncoupled model. The pure torsional and pure axial vibration of the 6th and 7th modes in uncoupled model occur simultaneously in the 6th mode of the coupled model. It can also be found that the axial vibration happens in all of the coupled modes.

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(c) 275.58 Hz

(d) 468.74 Hz

(e) 473.50 Hz

(f) 525.07 Hz

(g) 591.69 Hz

(h) 808.28 Hz

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Fig. 20.5 Mode shapes of the uncoupled model

The above modal analysis results indicate that the modal characteristics of the bevel geared rotor-bearing system will be changed due to the coupling effect of the meshing gear pair. It will not only cause the modal coupling of one of the shafts itself (e.g. the 4th, 5th, 7th and 8th modes), but also between the shafts of the system (e.g. the 1st, 2nd, 3rd and 6th modes). In addition, the dominant mode of the system may change from the perspective of modal strain energy (e.g. the 1st mode).

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(a) 183.31 Hz

(b) 275.11 Hz

(c) 295.97 Hz

(d) 469.29 Hz

(e) 474.16 Hz

(f) 590.74 Hz

(g) 726.89 Hz

(h) 808.63 Hz

Fig. 20.6 Mode shapes of the coupled model

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Table 20.6 Natural frequencies and modal strain energy of uncoupled model Natural frequency (Hz)

Modal strain energy (%) Lateral Swing

Axial

Torsional

148.55 274.66 275.58 468.74 473.50 525.07 591.69 808.28

1.30e−18 52.39 52.48 51.16 51.25 1.76e−18 2.79e−21 59.09

2.15e−18 1.04e−23 7.56e−24 1.48e−23 4.78e−22 1.55e−17 100.00 2.28e−22

100.00 3.04e−25 1.31e−25 9.82e−26 9.97e−26 100.00 4.00e−24 9.65e−27

Table 20.7 Natural frequencies and modal strain energy of coupled model

1.20e−18 47.61 47.52 48.84 48.75 1.72e−18 2.77e−21 40.91

Natural frequency (Hz)

Modal strain energy (%) Lateral Swing Axial

Torsional

183.31 275.11

45.11 51.45

45.91 48.15

8.94 0.40

295.97

54.39

45.28

469.29

51.17

48.83

474.16

51.25

48.74

590.74 726.89 808.63

0.54 56.62 59.28

0.44 40.60 40.67

0.04 0.51e −2 0.41e −2 7.80e −8 1.74e −7 97.80 0.12 2.54e −2

0.33 2.08e-5 5.75e−5 1.22 2.66 0.05

20.3.2 Dynamic Response Analysis of the Coupled System The dynamic response analysis under external load is also one of the major topics in geared rotor-bearing system. In this subsection, the response of the system shown in Fig. 20.4 under unbalanced and torsional excitation are emphatically discussed. Here the meshing stiffness is in the time-varying form as given in Eq. (20.5), and only the first order Fourier series of the transmission error is retained. Node 20 and node 8 are selected for response analyses because they are the nodes of bearings and easy to fix sensors. Response Under Unbalanced Excitation. As shown in Fig. 20.1, the mass unbalance is considered in the modeling process of the bevel gear pair. Here the unbalanced excitation is supposed to occur on the pinion. Figure 20.7 shows the

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axial vibration spectrum of node 20 located at the top bearing of the vertical shaft. It reveal that the rotation frequency of the horizontal shaft f1 is detected in the spectrum of the axial vibration of the vertical shaft, indicating that due to the coupling effect of the bevel gear pair, the unbalanced excitation on the one shaft will cause the change in the vibration response of the other shaft. In addition, the sidebands appear around the meshing frequency fm and its harmonics and are located at nfm  f1 , which means the meshing frequency is modulated by the unbalanced excitation frequency. Response Under Torsional Excitation. The torsional excitation may be generated by rotation of engines or propellers. In this case study, a torsional excitation of 12 times of the rotation frequency (f2 ) is applied to a node on the vertical shaft to simulate the excitation caused by a fan with 12 blades. The axial and torsional vibration spectrum of node 8 which located at the bearing nearby the pinion are shown in Fig. 20.8. This figure shows that the torsional excitation on one shaft can evoke significant vibration of the other shaft in multiple directions because of the coupling. The meshing frequency fm is modulated by the torsional excitation frequency, which is similar to the spectrum characteristics of the unbalanced response. Here the sidebands appear at nfm  12f2 . The above two case studies indict that for the spiral bevel geared rotor-bearing system, the excitation in one direction may cause coupled lateral-axial-torsional vibration of the system. Therefore various excitation factors should be considered when suppressing the vibration of this kind of rotor system. The vibration at bearings of one shaft can reflect some faults of the other shaft on which it may be not convenient to fix sensors.

Fig. 20.7 Axial vibration spectrum of node 20

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Fig. 20.8 Axial and torsional vibration spectrum of node 8

20.4

Conclusion

In this study, a three-dimensional dynamic model of a spiral bevel geared rotor-bearing system is established to analyze the coupled dynamic characteristics of this kind of rotor system. The motion equations considering the effects of unbalance, nonlinear meshing stiffness and transmission error are formulated, and the coupled modal and dynamic response analysis of a simple model are investigated. The results reveal that due to the coupling of the bevel gear pair, the modal characteristics of the system will be changed. The coupled lateral-axial-torsional vibration will occur not only in one of the shafts itself but also in the rotor system. The excitation in one direction may evoke sizeable response of the system in multiple directions, and create modulation of the meshing frequency and its harmonics by the excitation frequency. The dynamic characteristics of faults such as mass unbalance in one shaft can be observed in the vibration response of bearings on the other shaft. It can be beneficial in fault diagnosis and condition monitoring of the bevel geared rotor-bearing system. In addition, the model can be easily modified

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and extended to analyze more dynamic characteristics of this kind of system, which will be studied in the further work as well as experiments. Acknowledgements This work was supported by the National Science and Technology Major Project (2018ZX04011001).

References 1. Liang, X., Zuo, M.J., Feng, Z.: Dynamic modeling of gearbox faults: a review. Mech. Syst. Sig. Process. 98, 852–876 (2018). https://doi.org/10.1016/j.ymssp.2017.05.024 2. Zhang, Y., Wang, Q., Ma, H., Huang, J., Zhao, C.: Dynamic analysis of three-dimensional helical geared rotor system with geometric eccentricity. J. Mech. Sci. Technol. 27(11), 3231– 3242 (2013). https://doi.org/10.1007/s12206-013-0846-8 3. Liang, X., Zuo, M.J., Pandey, M.: Analytically evaluating the influence of crack on the mesh stiffness of a planetary gear set. Mech. Mach. Theory 76, 20–38 (2014). https://doi.org/10. 1016/j.mechmachtheory.2014.02.001 4. Yang, D.H., Lin, J.Y.: Hertzian damping, tooth friction and bending elasticity in gear impact dynamics. ASME. J. Mech. Trans. Autom. 109(2), 189–196 (1987). https://doi.org/10.1115/1. 3267437 5. Tian, X.: Dynamic simulation for system response of gearbox including localized gear faults. Doctoral dissertation, University of Alberta (2004) 6. Zhou, X., Shao, Y., Lei, Y., Zuo, M.: Time-varying meshing stiffness calculation and vibration analysis for a 16DOF dynamic model with linear crack growth in a pinion. J. Vibr. Acoust. 134(1), 011011 (2012). https://doi.org/10.1115/1.4004683 7. Abboudi, K., Walha, L., Driss, Y., Maatar, M., Fakhfakh, T., Haddar, M.: Dynamic behavior of a two-stage gear train used in a fixed-speed wind turbine. Mech. Mach. Theory 46(12), 1888–1900 (2011). https://doi.org/10.1016/j.mechmachtheory.2011.07.009 8. Zhou, S., Song, G., Ren, Z., Wen, B.: Nonlinear dynamic analysis of coupled gear-rotor-bearing system with the effect of internal and external excitations. Chin. J. Mech. Eng. 29(2), 281–292 (2016). https://doi.org/10.3901/CJME.2015.1019.124 9. Kubur, M., Kahraman, A., Zini, D.M., Kienzle, K.: Dynamic analysis of a multi-shaft helical gear transmission by finite elements: model and experiment. J. Vibr. Acoust. 126(3), 398–406 (2004). https://doi.org/10.1115/1.1760561 10. Hua, X., Lim, T.C., Peng, T., Wali, W.E.: Dynamic analysis of spiral bevel geared rotor systems applying finite elements and enhanced lumped parameters. Int. J. Autom. Technol. 13(1), 97 (2012). https://doi.org/10.1007/s12239-012-0009-4 11. Saxena, A., Chouksey, M., Parey, A.: Effect of mesh stiffness of healthy and cracked gear tooth on modal and frequency response characteristics of geared rotor system. Mech. Mach. Theory 107, 261–273 (2017). https://doi.org/10.1016/j.mechmachtheory.2016.10.006 12. Ma, H., Song, R., Pang, X., Wen, B.: Fault feature analysis of a cracked gear coupled rotor system. Math. Probl. Eng. (2014). https://doi.org/10.1155/2014/832192 13. Lee, A.S., Ha, J.W., Choi, D.H.: Coupled lateral and torsional vibration characteristics of a speed increasing geared rotor-bearing system. J. Sound Vibr. 263(4), 725–742 (2003). https:// doi.org/10.1016/s0022-460x(02)01103-3 14. Zhang, H., Wei, G., Wen, B., Han, Q., Hao, H.: Meshing characteristics of geared rotor system in integrally geared compressor with unbalance excitation. J. Vibr. Control 25(1), 26– 40 (2019). https://doi.org/10.1177/1077546318767559 15. Li, M., Hu, H.Y., Jiang, P.L., Yu, L.: Coupled axial–lateral–torsional dynamics of a rotor– bearing system geared by spur bevel gears. J. Sound Vibr. 254(3), 427–446 (2002). https:// doi.org/10.1006/jsvi.2001.4016

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16. Li, M., Hu, H.Y.: Dynamic analysis of a spiral bevel-geared rotor-bearing system. J. Sound Vibr. 259(3), 605–624 (2003). https://doi.org/10.1006/jsvi.2002.5111 17. Yassine, D., Ahmed, H., Lassaad, W., Mohamed, H.: Effects of gear mesh fluctuation and defaults on the dynamic behavior of two-stage straight bevel system. Mech. Mach. Theory 82, 71–86 (2014). https://doi.org/10.1016/j.mechmachtheory.2014.07.013 18. Xu, J., Wan, L., Luo, W.: Influence of bearing stiffness on the nonlinear dynamics of a shaft-final drive system. Shock Vibr. (2016). https://doi.org/10.1155/2016/3524609 19. Xu, J., Zeng, F., Su, X.: Coupled bending-torsional nonlinear vibration and bifurcation characteristics of spiral bevel gear system. Shock Vibr. (2017). https://doi.org/10.1155/2017/ 6835301 20. Stringer, D.B.: Geared rotor dynamic methodologies for advancing prognostic modeling capabilities in rotary-wing transmission systems. Doctoral dissertation, University of Virginia (2008)

Chapter 21

Parameters Analysis and Optimization Design of a Slotless Halbach Linear Generator for Wave Energy Harvesting Na Liu, Yimin Tan, Weiqiang Mo, and Zuguang Zhang

Abstract In the research of wave energy harvesting system, establishing a system’s analytical model is the prerequisite of the design optimization. In this paper, an optimization is conducted for the slotless Halbach linear generator, whose analytical model is developed employing the magnetic vector potential theory. In the process of the optimization, the simulated annealing algorithm, which is one of global optimization methods, is applied with the analytical model to gain a set of optimal design parameters, including the generator’s dimensions of permanent magnets and the winding coils. With the derived parameters, the design of a slotless Halbach linear generator for wave energy convertor is settled, and the analytical analysis indicates that the peak output power of the optimized generator reaches 527.24 W. This work shows that the described optimization process of the Halbach linear generator is the key in the design of a wave energy convertor.

21.1

Introduction

Ocean wave energy is a kind of inexhaustible and renewable green energy. The exploitation and utilization of wave energy is of significance in alleviating energy crisis. Because of the earth gravity, wind blowing over the ocean’s surface brings about ocean waves and producing tremendous potential energy which can be captured as form of electrical energy [1]. This capture energy work is realized through wave energy converter. In the process of harvesting ocean wave energy, the maximization of wave energy conversion efficiency is the most concerned issue. Several wave energy converters have been designed to extract ocean wave energy [2–4], such as the oscillating water column, oscillating body systems, and overtopping devices [5]. There are several kinds of power take-off(PTO)mechanisms N. Liu
Y. Tan (&)
W. Mo Beijing Institute of Technology, Zhuhai, China e-mail: [email protected] Y. Tan
Z. Zhang Sea Electric Energy, Inc., Mississauga, Canada © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_21

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lies in wave energy converters, which are linear generators [6], power hydraulics [7], turbines [8], linear to rotary motion transmission mechanism [9]. By studying and researching from other’s work, we have proposed to use Halbach permanent magnet(PM) linear generator as the PTO, which PM array could enhance the magnetic distribution in the targeting direction. As a result, it is expected that the power conversion efficiency to be increased. Based on the Halbach linear generator performance model that have been established in the previous work results [10, 11], there are several parameters do have a significance influence on the performance of the generator which are: the length of four segments NdFeB Halbach PM arrays, the radius of central shaft, the thickness of magnet, the width of air gap, winding wire gauges, and the velocity of the ocean wave. When designing the wave energy harvesting converter, the critical parameters of the converter need to be optimized. The optimization of the converter’s parameters is a problem of combinatorial optimization, i.e. non-deterministic polynomial (NP) optimization problem [12]. The calculation time will be exponentially increasing against the rising of the system’s parameter numbers. Up to today, several global optimization algorithms have been proposed to resolve the aforementioned NP problems. Genetic algorithm method have been used to resolve the problem of optimization design of Halbach linear generator to achieve good performance and optimize the generator’s critical variables [13–15]. Particle swarm optimization has been applied in the design of Halbach PM synchronous generators for the megawatt level wind turbines [16]. Markovic employed a mono-objective optimization method which returned only one output parameter for improving the power density [17]. Besides, simulated annealing algorithm (SAA) has been proposed to deal with the problem of combinational optimization design. SAA draws an analogy between the problem of combination optimization and the problem of thermodynamic equilibrium in statistical mechanics which simulated the process of annealing to gain the result of approximately global optimization solution [18–20]. Compared to the algorithm above, SAA is able to obtain a sub-optimized solution while maintaining a satisfactory computation efficiency. This feature of SAA could guarantee a good balance between optimization accuracy and efficiency. In this paper, we have employed SAA in the optimization of a Halbach linear generator for wave energy harvesting. The remainder of this paper will focus on the parameter analysis and optimization design of the slotless Halbach linear generator for wave energy harvesting. It will be divided as follows: the first part will focus on the analytical model of Halbach linear generator. The second part will discuss results of simulated annealing algorithm and analysis of the results.

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21.2

253

The Analytical Model of Halbach Linear Generator

21.2.1 Topological of Halbach Linear Generator The linear Halbach generator is the PTO system of the wave energy converter [21]. It can transform the wave energy directly to electric energy without the intermediate transmission mechanism for energy conversion. Thus, the system not only has a simple structure but also can reduce the energy loss, which in turn will greatly improve the efficiency and the stability of the converter. Generally, a typical direct-drive generator system has the following mechanical structures: a module of wave energy collection(the float), energy transformation mechanism (generators) and other modules. In order to eliminate the cogging force, the slotless iron core topology has been used to improve stability of wave energy converter. The scheme can be found in Fig. 21.1.

21.2.2 Modelling On the basis of Faraday’s law of electromagnetic induction, the induced electromotive force produced by the system should be: R Bds du ¼ N e ¼ N dt dt

ð21:1Þ

Here, / is the magnetic flux generated by the magnet, N is the number of turns of a group of coil windings, B is the magnetic field intensity, and s is the cross-sectional area of the magnetic field. When we are planning to analyzing the performance of the linear generator, the magnetic field distribution model of the generator need to be simplified, three assumptions are put forward: first, the length of the armature is regarded as infinite. second, the fringing effects existed in a finite armature will not be introduced as for the coils are moving inside the armature [10, 21]; third, the armature’s permeability is infinite due to its ferromagnetic property.

Fig. 21.1 Halbach linear generator. a 3-D model. b Axial profile. c Front view

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The structure of the Halbach linear generator, as is given in Fig. 21.1, which can be divided into three regions in the process of establishing the magnetic field distribution: Region 1-airgap region, Region 2-Halbach PM arrays region, Region 3-central shaft region. The magnetic field of magnet domain and free space domain become  B¼

l0 H lH þ l0 M

ð21:2Þ

Where l ¼ l0 lr , l0 is the vacuum’s permeability, lr denotes the permeability of the magnet, which is close to 1. The vector H denotes the magnetic field, and the vector M represents the magnetization of Halbach PM arrays. Theoretically, the magnetic field has a curl but no divergence field and the magnetic induction line is always closed. So the divergence of the magnetic induction intensity is considered ! to be zero, that is r  B ¼ 0. In terms of the vector analysis theory, the magnetic vector potential r is introduced into Maxwell Equations, the previous equation can ! be written as B ¼ r  r, then Eq. (21.2) can be changed as follows: 

r2 r1;3 ¼0 ! r2 r2 ¼ l0 rM

ð21:3Þ

Meanwhile, the magnetized magnetic field can be expressed by Fourier transform. ! Therefore, M can be given by ! ! ! M ¼ Mq þ MZ

ð21:4Þ

! ! Here, Mq is the radial magnetic induction intensity of Halbach PM arrays and Mz is the axial magnetic induction intensity of Halbach PM arrays. 8 1 kp kp P k1 > 0 sinð 4 Þ sinð 2 Þ > ð1Þ 2 4B cosðnk zÞ < Mq ¼ l0 kp k¼1

1 kp P kþ1 > 0 sinð 4 Þ > ð1Þ 2 4B : Mz ¼ l0 kp sinðnk zÞ

ð21:5Þ

k¼1

Here nk ¼ 2kp Lt , nk is the spatial angular frequency of the nth harmonic component. The magnetic vector potential has only one component in cylindrical coordinates. Hence, the domain equations can be determined as follows: 8 2 @ r1 > > < @z2 2 þ @ r2 @z2 þ > > : @ 2 r3 þ @z2

@ 2 r1 @q2 @ 2 r2 @q2 @ 2 r3 @q2

þ þ þ

1 @r1 q @q 1 @r2 q @q 1 @r3 q @q

1  @r @q2 ¼0

2  @r @q2 ¼  l0



@r3 @q2

¼0

@Mq @z

ð21:6Þ

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At the boundary between Region 1 and Region 2 as well as Region 2 and Region 3, the continuity of axial magnetization of the magnetic field should meet the following conditions: (B

1z

l0 B3z l0

¼ Bl2z  H1z

ð21:7Þ

0

¼ Bl2z  H3z 0

The continuity of radial magnetization of the magnetic field should be satisfied as (

B1q ¼ B2q

ð21:8Þ

B3q ¼ B2q

It is worth noting that the magnetic flux density outside region 1 and region 3 is 0. By solving the aforementioned domain equations and boundary conditions, the radial magnetic field in the three regions can be obtained as follows: 8 < B1q ¼ nk ðC1k I1 ðnk qÞÞ cosðnk zÞ B2q ¼ ½ðnk C3k þ FAK ðnk qÞÞI1 ðnk qÞ þ ðnk C4k  FBk ðnk qÞÞK1 ðnk qÞ cosðnk qÞ : B3q ¼ nk ðC5k I1 ðnk qÞÞ þ C6k K1 ðnk qÞ cosðnk zÞ ð21:9Þ The axial magnetic field in corresponding regions can be expressed as follows: 8 < B1z ¼ nk ðC1k I0 ðnk qÞÞ sinðnk zÞ B ¼ ½ðnk C3k þ FAK ðnk qÞÞI0 ðnk qÞ  ðnk C4k  FBk ðnk qÞÞK0 ðnk qÞ sinðnk qÞ : 2z B3z ¼ nk ðC5k I0 ðnk qÞÞ  C6k K0 ðnk qÞ sinðnk zÞ ð21:10Þ Here I and K represent the modified Bessel functions of first and second kind, respectively. According to equations above, the magnetic linkage and reaction force of a single coil of the generator can be written as follows: u= 

N hðri  rp  gÞ Z

F¼ z

zþh

Z

Z

zþh

z ri rp þ g

Z

ri

rp þ g

2pqr1 ðq; zÞdq dz

2pqB2q ðq; zÞdq dz

ð21:11Þ

ð21:12Þ

Here g is width of air gap, h is height of each group of coils. The induced electromotive force (EMF) of the coils can be derived from above equations, assuming its effective value is E, then the output voltage and output power of the generator are as follows:

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(

V ¼ Ro Eþ Ri Ro P ¼ ðRo Eþ Ri Þ2 Ro

ð21:13Þ

Where Ri is the internal impedance of the coil, and Ro is the external load of the generator. When the condition met with Ro ¼ Ri , the maximum power can be gained.

21.3

Application of Simulated Annealing Algorithm

21.3.1 Key Design Parameters In order to gain the maximal efficiency under the same conditions, the key design parameters of the generator need to be optimized, which are the width of the coil la , the length of 4 segments of Halbach PM arrays lt , the thickness of magnet lm , the radius of the central shaft rs , and winding wire gauge. In addition, when oscillation speed of the ocean wave is higher, the reaction force of the single coil will be stronger,

Fig. 21.2 The flowchart of the SAA

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thus the corresponding output power of the Halbach linear generator will be more larger. We will discuss how to determine the critical parameters through using simulated annealing algorithm in the following parts.

21.3.2 The Application of Simulated Annealing Algorithm Kirkpatrick etc. have realized that there were similarities between combinatorial optimization problems and statistical mechanics in 1983 [18]. Then they introduced the annealing idea into the combinatorial optimization field and proposed simulated annealing algorithm(SAA) to solve the large-scale combinatorial optimization problem, especially the NP complete combinatorial optimization problem. SAA adopts metropolis acceptance criteria and uses a cooling schedule composed of four parameters which are the initial value of the control parameter ti , the end value of the control parameter te , the length of Markov chain Lk and the attenuation function of the control parameter a to control the process of SAA [22, 23]. Starting from an initial solution, the SAA will execute an iterative process in which, a new solution is generated and then to be accepted/abandon with a probability, the sub-optimal solution can be obtained with an appropriate environmental setting. By repeating the process, the global optimal solution of combinatorial optimization problem will be gained. Figure 21.2 is the flowchart of the SAA applied in modeling of Halbach linear generator. Quality of the final solution is inversely proportional to the CPU time consumed, so it is hard to balance the two. The experimental conclusion of Johnson et al. [24, 25], the value of Ti is defined as 103 . The stopping criterion constructed by Nahar etc. [26, 27] indicates that only when the control parameter is trivial comparing to its initial value, can a high-quality final solution be obtained. Thus, the final value of the control parameter is defined to be 0.01. The Markov chain length is fixed to 120. As for the attenuation function of the control parameter, a commonly used function is ti þ 1 ¼ a  ti [25, 26], where a is a constant number closed to 1. Suggested by Kirkpatrick et al. [18, 24], we make the value equal to 0.95. The 0bjective function of the SAA is the function that returns the max power of wave energy converter. The function is constructed based on the model of Halbach linear generator.

21.3.3 Simulation Results and Analysis of the Results The Halbach PM linear generator has several key design parameters which will have significant effects on the converter’s performance. The radius of the central shaft has influence on generator’s magnetic linkage and reaction force, suggested by Eq. 21.11 and 21.12. The specific relationships can be seen in Fig. 21.3(a). The

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Fig. 21.3 a Relationship between the output power of a Halbach linear generator and the radius of the central shaft. b Relationship between the output power of a Halbach linear generator and the thickness of a permanent magnet

equivalent power-take-off (PTO) damping coefficient is proportional to the generator’s output peak power. The peak power of the generator increases along with the thickness of PM. As for the impact of the central shaft’s radius, we can observe that while the radius becomes larger, the peak power increases significantly, as shown in Fig. 21.3(b). This above conclusion can be observed from Fig. 21.3. The performance of the generator will improve as the dimensions increase, which will enhance flux density distribution. However, the cumbersome structure caused by the large size is undesirable for the generator. Besides, large size will also increase the difficulty of assembly as well as the manufacturing cost. Considering the reasons above, we have fixed the radius of the center shaft to 20 mm and the thickness of the magnet to 20 mm. From Fig. 21.3 we can conclude that the power output of the generator is monotonous increasing against the PM dimensions. The reason is that when PM size increases, the total magnetomotive force is enlarged which enhances the magnetic flux density distribution in the air gap. As for the solution of output peak power of Halbach linear generator, we can observe that the analytical solution result matches well with the SAA, Fig. 21.4 confirms that. The optimization process has been implemented in the MATLAB environment. The program has calculated on the order of 19,580 times. Generate a new solution will costs 0.486 s, the optimal solution has been accepted 8494 times, corresponding the deterioration solution has been accepted 5444 times. But, with the gradual proceeding of the program, the probability of accepting the deteriorating solution is getting lower. The maximum power of the generator model can reach 527.24 W. The optimal width of the air gap is 54 mm. It worth note that the value includes the height of each group coils. The result of SAA matches the result of the traversal method with an error within 3.6%. The design parameters of a slotless Halbach linear generator for wave energy convertor is settled as in Table 21.1.

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Fig. 21.4 Output peak power of Halbach linear generator. a Analytical solution result with the traversal method. b SAA result

Figure 21.5 shows the result of magnetic flux density distribution in normal direction and tangential direction at the outer surface of PM. From the distribution of the flux density in the radial direction, we can clearly observe that the maximum magnetic density strength in the air gap reaches 1.4T. Meanwhile, the magnetic flux density distribution in the axial direction is greatly suppressed. Table 21.1 Optimization parameters of a slotless Halbach linear generator

Parameters

Value

Width of air gap Length of 4 segments NdFeB Halbach PM array Radius of central shaft Thickness of magnet wire gauges

54 mm 390 mm 20 mm 20 mm AWG 22

Fig. 21.5 Magnetic flux density distribution at the outer surface of PM a normal component. b tangential component

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Conclusion

In this paper, we have further improved the time domain model based our previous work in order to gain key parameters for designing a wave energy converter. In the paper’s first section, a topological of Halbach linear generator has been shown firstly. Then a time domain has been constructed. Subsequently, an analysis has been conducted on the proposed model of Halbach linear generator aimed to extract the electrical energy from ocean wave. In the paper’s second section, we used SAA to get an optimization global solution. There are several parameters do have influence on the generator’s magnetic flux density distribution. Firstly, we have picked four of them to analyze, which are the length of air gap, length of 4 segments NdFeB Halbach PM arrays, radius of central shaft, thickness of magnet. In order to obtain the maximum power of the generator, we need to find the optimal combination of those parameters. Then the work of optimization has been done using SAA. The values produced by SAA matches well with the traversal method with an error within 3.6%.

References 1. Al Shami, E., Zhang, R., Wang, X.: Point absorber wave energy harvesters: a review of recent developments. Energies 12(1), 47 (2019) 2. Hong, Y., Eriksson, M., Castellucci, V., Boström, C., Waters, R.: Linear generator-based wave energy converter model with experimental verification and three loading strategies. IET Renew. Power Gener. 10(3), 349–359 (2016) 3. Lehmann, M., Karimpour, F., Goudey, C.A., Jacobson, P.T., Alam, M.R.: Ocean wave energy in the United States: current status and future perspectives. Renew. Sustain. Energy Rev. 74, 1300–1313 (2017) 4. Babarit, A., Duclos, G., Clément, A.H.: Comparison of latching control strategies for a heaving wave energy device in random sea. Appl. Ocean Res. 26(5), 227–238 (2004) 5. Drew, B., Plummer, A.R., Sahinkaya, M.N.: A review of wave energy converter technology. Proc. Inst. Mech. Eng. Part A J. Power Energy 223, 887–902 (2016) 6. Engström, J., Kurupath, V., Isberg, J., Leijon, M.: A resonant two body system for a point absorbing wave energy converter with direct-driven linear generator. Journal of applied physics 110(12), 124904 (2011) 7. Ruehl, K., Brekken, T.K., Bosma, B., Paasch, R.: Large-scale ocean wave energy plant modeling. In: 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply, pp. 379–386.. IEEE, September 2010 8. Kim, B.H., Wata, J., Zullah, M.A., Ahmed, M.R., Lee, Y.H.: Numerical and experimental studies on the PTO system of a novel floating wave energy converter. Renew. Energy 79, 111–121 (2015) 9. Sang, Y., Karayaka, H.B., Yan, Y., Zhang, J. Z., Muljadi, E., Yu, Y.H.: Energy extraction from a slider-crank wave energy converter under irregular wave conditions. In: OCEANS 2015-MTS/IEEE Washington, pp. 1–7. IEEE, October 2015 10. Tan, Y., Lin, K., Zu, J.W.: Analytical modelling of Halbach linear generator incorporating pole shifting and piece-wise spring for ocean wave energy harvesting. AIP Adv. 8(5), 056615 (2018)

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11. Tan, Y., Lin, K., Zhang, Z.: Comprehensive modelling of a slotess halbach linear generator based wave energy converter. In: IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, pp. 1717–1722. IEEE, October 2018 12. Paz, A., Moran, S.: Non deterministic polynomial optimization problems and their approximations. Theoret. Comput. Sci. 15(3), 251–277 (1981) 13. Caner, M., Gerada, C., Asher, G., Özer, T.: Design optimization of Halbach array permanent magnet motor to achieve sensorless performance using genetic algorithm. COMPEL-Int. J. Comput. Math. Electr. Electron. Eng. 35(5), 1741–1759 (2016) 14. Sadeghi, S., Parsa, L.: Multiobjective design optimization of five-phase Halbach array permanent-magnet machine. IEEE Trans. Magn. 47(6), 1658–1666 (2011) 15. EIGebaly, A.E., El-Nemr, M.K.: Optimal design of slotless PM halbach array linear generator for wave energy converters at maximum power transfer condition. In: 2018 Twentieth International Middle East Power Systems Conference (MEPCON), pp. 271–276. IEEE, December 2018 16. Alshibani, S.: Application of particle swarm optimization in the design of halbach permanent magnet synchronous generators for megawatt level wind turbines. In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), pp. 865–868. IEEE, October 2018 17. Markovic, M., Perriard, Y.: Optimization design of a segmented Halbach permanent-magnet motor using an analytical model. IEEE Trans. Magn. 45(7), 2955–2960 (2009) 18. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983) 19. Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. In: Simulated Annealing: Theory and Applications, pp. 7–15. Springer, Dordrecht (1987) 20. Schuur, P.C.: Classification of acceptance criteria for the simulated annealing algorithm. Math. Oper. Res. 22(2), 266–275 (1997) 21. Vermaak, R., Kamper, M.J.: Experimental evaluation and predictive control of an air-cored linear generator for direct-drive wave energy converters. IEEE Trans. Ind. Appl. 48(6), 1817– 1826 (2012) 22. Aarts, E., Korst, J.: Simulated annealing and Boltzmann machines (1988) 23. Davis, L.: Genetic algorithms and simulated annealing (1987) 24. Kirkpatrick, S.: Optimization by simulated annealing: quantitative studies. J. Stat. Phys. 34(5– 6), 975–986 (1984) 25. Johnson, D.S., Aragon, C.R., McGeoch, L.A., Schevon, C.: Optimization by simulated annealing: an experimental evaluation; part I, graph partitioning. Oper. Res. 37(6), 865–892 (1989) 26. Nahar, S., Sahni, S., Shragowitz. E.: Experiments with simulated annealing. In: Proceedings of 22nd Design Automation Conference, Las Vegas, pp. 748–752, June 1985 27. Otten, R.H.J.M., Van Ginneken, L.P.P.P.: Stop criteria in simulated annealing. In: Proceedings 1988 IEEE International Conference on Computer Design: VLSI, pp. 549– 552. IEEE, October 1988

Chapter 22

Vibration Characteristics of Self-excited Vibration About Sliding Bearings Ryosuke Fukui, Hiromitsu Ohta, Yuta Yamada, Naoya Nagahashi, Tomoo Shigi, and Satoshi Tamura

Abstract This paper shows vibration characteristics of a sliding bearings. Especially, we study self-excited vibration (Oil whirl) of the sliding bearings. We tried three analysis methods, which are trajectory of shaft vibration and tracking analysis, and particle number of wear debris in lubricant generated by direct contact between a shaft and outer race of the sliding bearing. In a sliding bearing, an oil whirl, which is a characteristic self-oscillation, occurs at a rotation speed less than twice the critical speed of the experiment system. In addition, since the vibration of the foundation affects the vibration of the slide bearing when installed on a base with low rigidity, it is necessary to install it on the one with high rigidity. It seems that the bearing inner ring and the outer ring did not contact in the runout due to the natural frequency of the experiment system. The oil whirl contacted and the increase of wear debris in lubricant generated by the direct contact was confirmed.

22.1

Introduction

Nowadays, several machines support our life and industry. Regarding the fishery industry, it is used in food processing factories and fishing boats. Sliding bearings are generally used in a lot of machines. In general, operating with lubricant, therefore the pump to circulate lubricant is necessary, consequently the structure of sliding bearing is complicated. It can be used semipermanently as long as there is no fault. However, the fault of the sliding bearing is self-excited vibration called oil whirl. Amplitude of the shaft increases by self-excited vibration without external force. Oil whirl [1] can cause damage to inner ring and outer ring of the sliding R. Fukui
Y. Yamada
N. Nagahashi Graduate School of Fisheries Science, National Fisheries University, 2-7-1 Nagata-Honmachi, Shimonoseki, Yamaguchi 759-6595, Japan H. Ohta (&)
T. Shigi
S. Tamura Department of Ocean Mechanical Engineering, National Fisheries University, 2-7-1 Nagata-Honmachi, Shimonoseki, Yamaguchi 759-6595, Japan e-mail: ohta@fish-u.ac.jp © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_22

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bearing, finally, breakdown of the sliding bearings occurs. In this research, we aim to understand the characteristics of these self-excited oscillations (Oil whirl) of sliding bearing. We tried three analysis methods, which are trajectory of shaft vibration, tracking analysis and particle number of wear debris in lubricant.

22.2

Self-excited Vibration Called Oil Whirl

Self-excited vibration is one in which non-oscillating energy is converted into excitation force through a system. Oil whirl is self-oscillation that occurs in the rotating shaft supported by the sliding bearing. It occurs at speeds below twice the primary critical speed.

22.3

Analysis Method

22.3.1 Experimental Devices I explain experimental devices in this study. Figure 22.1 shows the sliding bearing test machine. The rotating speed of the shaft is up to 5000 [rpm]. The natural frequency of the rotating shaft of the sliding bearing is about 2543 [rpm] (42.4 [Hz]). Table 22.1 shows the characteristics of the experimental lubricant (Additive-free machine oil VG22) supplied to the sliding bearing.

Fig. 22.1 The sliding bearing test machine

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Table 22.1 The characteristics of the experimental lubricant

265

Kinematic viscosity

Flash point

Density

Hue

mm2/s (40 °C) 21.1

°C (COC) 180

g/cm2 (15 °C) 0.881

ASTM L1.5

22.3.2 Vibration Analysis We increased the rotating speed (run-up) from 500 [rpm] to 5000 [rpm], and made three measurements under the same conditions. And we decreased the rotating speed (coast-down) from 5000 [rpm] to 500 [rpm], and made three measurements under the same conditions. Vibration sensors are installed two directions of horizontal vibration X and vertical vibration Y. So it is possible to draw the trajectory of the rotation shaft from two sensors. In this research, we installed vibration sensors in horizontal direction and direction of 45 degrees with the vertical axis. Figure 22.2 shows reference figure. Therefore, the data obtained was corrected in the vertical direction by the following Eq. (22.1) and (22.2). yD ¼ ycos45 þ xD cos45 y¼

ð22:1Þ

yD  xD cos45 cos45

ð22:2Þ

Y

X

Fig. 22.2 Reference figure of installed vibration sensors

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22.3.3 Particle Number of Wear Debris in Lubricant We measured mount of wear debris for a while after running at 1500 [rpm]. Because it was necessary to stabilize the amount of wear debris. Lubricating oil analysis [2, 3] uses light shielding by particles in the oil by opening a window in the piping where sample solution flows. Discrimination of particle size is also possible. Because the shielding time is different depending on the size of wear debris.

22.4

Results and Discussion

Figure 22.3 shows the frequency response function (tracking analysis) of the sliding bearing. When we operated run-up, Frequency response function rose at 3700 [rpm] and kept rising at 5000 [rpm]. When we operated coast-down, it fell at a stretch at 3400 [rpm]. This phenomenon is the oil whirl. Because it occurs at twice the critical speed or less. Figure 22.4 is the trajectories of the rotating shaft [4]. The shaft hardly swings at 535 [rpm] or 1498 [rpm]. However, the trajectories were increased at 4182 [rpm], and further increased at 4899 [rpm], for the cause of generation of oil whirl. Figure 22.5 shows the lubricating oil analysis results for the NAS grade, the number of particles [5], and the number of bubbles. NAS grade is a grade assigned to particles in oil by diameter and content, and the higher the value, the more advanced the contamination. The number of particle increased from maximum rotating speed 5000 [rpm]. It reached its maximum value while maintaining the

Fig. 22.3 The tracking analysis of the sliding bearing machine

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Fig. 22.4 The trajectories of the rotating shaft

Fig. 22.5 Particle number of wear debris in the lubricant

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rotating speed of shaft at 5000 [rpm]. After that it decreased. When the rotating speed of shaft decreased, the number of particles increased and then decreased. Since there are time lags in the analysis results and the number of revolutions, it can be said that the number of particles increased when the number of revolutions was actually lower than 5000 [rpm]. After that, in a state where the number of revolutions of the shaft is maintained, the oil film pressure inside the bearing changes due to the variation of the number of revolutions, the runout becomes large, and contact with the outer ring is maintained. I think that the number of contacts with is small.

22.5

Conclusions

In the sliding bearing, oil whirl, which is a characteristic self-oscillation, occurs at a rotation speed less than twice the dangerous speed. In addition, since the vibration of the foundation affects the vibration of the slide bearing when installed on a base with low rigidity, it is necessary to install it on the one with high rigidity. It seems that the bearing inner ring and the outer ring did not contact in the runout due to the natural vibration. The oil whirl contacted and the increase of particle number of wear debris in lubricant was confirmed. Sliding bearings are excellent in load bearing capacity and remaining life and easy to maintain, but when oil whirl occur, wear due to contact with the bearing outer ring causes damage to the bearing and deterioration of lubricating oil. In addition, high-speed rotating machines using sliding bearings are not only at the natural frequency at which resonance occurs, but also at the rotational speed at which oil whirl occurs, since the oil whirl makes a much larger swing than natural resonance. Acknowledgements This work was supported by Japan Society for Promotion of Science (JSPS), Grant-in-Aid for Science Research (C) (17K06979), and Fundamental Research Developing Association for Shipbuilding and Offshore (REDAS) of the shipbuilders’ Association of Japan. Therefore, the authors wish to express our deep gratitude to all JSPS and REDAS partners.

References 1. Capone, E.: Oil whirl in journal bearings under no load conditions. Wear 26(2), 207–217 (1973) 2. Wang, L., Gao, R.: Condition Monitoring and Control for Intelligent Manufacturing, pp. 132– 134. Springer Press, Heidelberg (2005) 3. Iron and steel institute of Japan: Handbook of Condition Diagnosis Technology, Maruzen publications, pp. 221–224 (1986). (in Japanese) 4. Wang, L., Gao, R.: Condition Monitoring and Control for Intelligent Manufacturing, pp. 111– 114. Springer Press, Heiselberg (2005) 5. Fitch, E.C.: Proactive Maintenance For Mechanical Systems. 2 edn. FES, Inc., p. 28 (1992)

Chapter 23

Compound Fault Diagnosis of Rolling Bearing Based on Transformation Scale Improved BPD and MCKD Jing Meng , Liye Zhao , and Ruqiang Yan

Abstract Rolling bearing is one of the major parts of a rotary machine, and its status also influences the operation of the rotary machine. This paper presents a new method to detect and separate compound faults of rolling bearing by integrating an improved basis pursuit denoising (BPD) algorithm with maximum correlated kurtosis deconvolution (MCKD). Split variable augmented Lagrangian shrinkage algorithm (SALSA) is adopted in this paper to perform BPD. Furthermore, to set appropriate values of Lagrange multipliers in BPD, transformation scale, which is determined by the energy in each sub-band of the initial denoised signal, is introduced. The vibration signal is then denoised to reveal repetitive impulses through new Lagrange multipliers. MCKD is used for further separation of compound faults in rolling bearing. Vibration signal analysis simulating compound faults of inner race fault and outer race fault verifies effectiveness of the presented method.

23.1

Introduction

Rotary machine is common in the mechanical system. Failure of rotary machine can cause economic loss, environmental contamination and even casualties. Because of the harsh environment and complex mechanical structure, compound faults usually happen in practical engineering. In consideration of rolling bearing’s importance in rotary machine, the compound fault diagnosis of rolling bearing also plays an important role in mechanical system. J. Meng
L. Zhao
R. Yan (&) Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, 210096 Nanjing, China e-mail: [email protected] J. Meng e-mail: [email protected] L. Zhao e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_23

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Localized defect in rolling bearing generates repetitive transients in the measured vibration signal. Many methods have been adopted to extract the repetitive transient component. For example, after the vibration signal was filtered by multi-wavelet packet, ensemble empirical mode decomposition (EEMD) was adopted for diagnosing multi-fault in a blade rotor [1]. Fault sensitive matrix was built to choose the best intrinsic mode function (IMF) and envelope order analysis transformed the extracted IMF into angular domain to diagnose compound faults of bearing [2]. Wavelet transform was used to characterize bearing fault related features and these features were used to train the Hidden Markov Model (HMM) model to classify the fault types [3]. Dual-tree complex wavelet transform (DTCWT) combined with NeighCoeff shrinkage denoising method was proposed to recognize different bearing faults, which showed good denoising performance [4]. Optimal variational mode decomposition (OVMD) selected best intrinsic mode components based on the weight factor. The selected component was processed by 1.5-dimension envelope spectrum to reveal fault features of multiple fault in bearing [5]. Some artificial intelligence methods were also adopted. Support vector machines trained selected sensitive non-dimensional symptom parameters to fuse vibration information. And possibility function was used to identify fault type of rolling bearing [6]. Time- and frequency-domain statistical features obtained from filtered signals by EMD were used as the input of adaptive neuro fuzzy inference system to classify different compound fault types of bearing [7]. Recently, sparse representation has also been used in fault diagnosis of rolling bearing. In terms of dictionary atoms, an adaptive impulse dictionary was proposed to simulate the real impulse in engineering [8]. An improved impulsive wavelet was used to denoise the strong harmonic component in the raw vibration signal [9]. In terms of decomposition methods, matching pursuit and basis pursuit were used [10, 11]. Matching pursuit was widely used in bearing fault diagnosis [8, 12, 13]. Basis pursuit was also applied in bearing fault diagnosis [14–16]. To denoise the original vibration signal, basis pursuit was adopted in this paper, where split variable augmented Lagrangian shrinkage algorithm (SALSA) was used to solve the basis pursuit denoising (BPD) problem [17]. Since Lagrange multipliers of SALSA play a critical role in soft threshold function, a new rule is proposed to choose the values of Lagrange multipliers based on sub-band energy. To further extract and separate the compound fault signal, maximum correlated kurtosis deconvolution (MCKD) is adopted. The rest is arranged as follows. Details of the proposed method is described in Sect. 23.2. Then experimental study is conducted to validate the performance of the proposed method for compound fault diagnosis of rolling bearings in Sect. 23.3. Conclusions are drawn in the last section.

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Methodology

In this section, basis pursuit denoising problem solved by SALSA is described. The new rule of defining Lagrange multipliers in SALSA is presented.

23.2.1 BPD Solved by SALSA The vibration signal obtained is always accompanied by noise. The collected signal can be expressed as follows. y ¼ xþn

ð23:1Þ

Where y means raw vibration signal collected from accelation sensor, x denotes denoised vibration component, and n represents noise. They have the same size, y; x; n 2 RN . x can be represented by redundant basis defined as follows. x ¼ U w

ð23:2Þ

Where U means an over-complete transform, U means the inverse transform of U, and w means sparse coefficients. In order to get w, l1-norm is adopted to transform Eq. (23.1) and Eq. (23.2) into BPD problem in Eq. (23.3). 1 arg min ky  U wk22 þ kkwk1 2 w

ð23:3Þ

where k is Lagrange multiplier. However, sometimes it’s more useful to adopt non-uniform regularization of w. 1 arg min ky  U wk22 þ kk  wk1 2 w

ð23:4Þ

where k and w have the same size, and  means element-wise multiplication. Equation (23.3) is the special case of Eq. (23.4) when elements in k all have the same value.SALSA is adopted to solve Eq. (23.4), and variable splitting strategy is applied into Eq. (23.4) as 1 arg min ky  U wk22 þ kk  uk1 2 w;u subject to w  u ¼ 0 Equation (23.5) can be rewritten in the form of Eq. (23.6).

ð23:5Þ

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min EðzÞ

ð23:6Þ

z

subject to Hz  b ¼ 0 where EðzÞ ¼ 12 ky  U z1 k22 þ kk  z2 k1 , z1 ¼ w, H ¼ ½ I I , b ¼ 0. The solution to Eq. (23.6) is written as follows. zk þ 1 ¼ arg min EðzÞ þ z

z2 ¼ u,

z ¼ ½ z1

l kHz  dk k22 2

dk þ 1 ¼ dk  ðHzk þ 1  bÞ

z2  T ,

ð23:7Þ ð23:8Þ

where l denotes a penalty parameter and k means the iteration number. Equation (23.7) and Eq. (23.8) can be rewritten in an explicit form. 1 l wk þ 1 ¼ arg min ky  U wk22 þ kuk  w  dk k22 2 2 w uk þ 1 ¼ arg minkk  uk1 þ w

l ku  wk þ 1  dk k22 2

dk þ 1 ¼ dk  ðuk þ 1  wk þ 1 Þ

ð23:9Þ ð23:10Þ ð23:11Þ

Specific solution to Eqs. (23.9)–(23.11) can be obtained as follows. wk þ 1 ¼ ðUU þ lIÞ1 ðUy þ lðwk  dk ÞÞ

ð23:12Þ

uk þ 1 ¼ softðwk þ 1 þ dk ; k=lÞ

ð23:13Þ

dk þ 1 ¼ dk  uk þ 1 þ wk þ 1

ð23:14Þ

where soft threshold function is defined as Eq. (23.15). softða; bÞ ¼ maxð1  b=jaj; 0Þ
a

ð23:15Þ

where a is input and b means the threshold in Eq. (23.15).

23.2.2 MCKD Theory Kurtosis value of periodic impulse signal can’t reveal the periodic impulses accurately [18]. Therefore, a new measure called correlated kurtosis (CK) was proposed to measure periodic impulses.

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Inverse filtering in a general equation is expressed as: o¼

L X

fk xnk þ 1

ð23:16Þ

k¼1

where the input signal is x and output signal is o. The filter coefficient is denoted as f. Filter length is L and N indicate the signal length. The objective function of MCKD is expressed as: max CKM ðTS Þ ¼ max f

f

Q ð M onmTS Þ2 PN m¼02 M þ 1 ð n¼1 on Þ

PN

n¼1

ð23:17Þ

where M denotes the shift number, fs denotes the sampling frequency, and Ts is the deconvolution period,TS ¼ fs
T. T indicates the fault period of rolling bearing to be identified. The final f is obtained by the derivatives of Eq. (23.17). Because of the reliable parameter CK, MCKD can extract periodic impulses of rolling bearing from noisy background. [19, 20].

23.2.3 Proposed Method From Eq. (23.13), k determines the value of threshold. The way of determining values of k is very important for the denoising performance. In this paper, a rule based on subband energy and transformation scale is proposed. k is firstly set as the norm of the wavelet shown in Eq. (23.18). kðjÞ ¼ kwðjÞk2

ð23:18Þ

where wðjÞ is the wavelet basis corresponding to sparse coefficient wðjÞ. If kðjÞ is set as a very small value, the corresponding threshold will be a small value too. Much noise will be left in the denoised signal. And the denoising performance will be weakened. So transformation scale S is proposed to transform kðjÞ to a more appropriate value to improve the denoising performance of BPD. To determine the values in S, vibration signal is firstly denoised by using k. And energy E in each sub-band decomposed by Tunable Q-factor wavelet transform (TQWT) is calculated to improve the values in k. S and E has the same size. EðjÞ ¼ kwðjÞk22

ð23:19Þ

Sub-band numbers m1 and m2 mean where the two maximums lie in E. The corresponding elements in S will be set as two equal values as c. The other elements in are set as same values as d.

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Vibration signal y

Set

TQWT-SALSA denoises the vibration signal

λ the initial

Calculate energy in each subband E

Calculate transformation scale S Power frequency spectrum of the separated signals

TQWT-SALSA denoises the vibration signal and get x

x is processed by MCKD to separate the compound fault

Update

λ by S to λ new

Fig. 23.1 The flowchart of the proposed method

S ¼ ½d

d




d

c d m1




d m2




d

c

d


d T

ð23:20Þ

To ensure denoising performance, c should be much smaller than d. c is equal to 10 while d is set as 100 in this paper. With the transformation scales in S, k will be updated in Eq. (23.21). knew ¼ S  k

ð23:21Þ

knew is adopted to perform the BPD again. The noise in the vibration signal will be reduced. The periodic impulses in the vibration signal will be much clearer. Compound bearing faults are usually mixed together in the time domain. To further extract and separate the compound fault, MCKD is adopted. The entire process of the suggested method is shown in Fig. 23.1.

23.3

Experiment

In this section, a vibration signal simulating compound faults in bearing is studied to validate the proposed method. The experiment platform called QPZZ test platform bench is shown in Fig. 23.2. QPZZ test bench. The type of bearing is SKF6205 with parameters in Table 23.1. The main components of the platform are shown in Fig. 23.2. QPZZ test bench. Compound faults are composed of inner and outer raceway faults. The length of the signal was set as 8192 sampling at 10240 Hz. The original vibration signal collected from QPZZ and corresponding information in frequency domain are displayed in Fig. 23.3. The repetitive impulses of compound faults can’t be seen in the original signal.

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Fig. 23.2 QPZZ test bench

Table 23.1 Fault characteristic frequency

6205 bearing fault characteristic frequency (Rotation speed 1500r/min) Fault location Frequency/Hz

Outer race/ fo 89.62

Inner race/fi 135.38

Ball/ fb 117.84

The result of the first BPD is shown in Fig. 23.4. The repetitive impulses of compound faults still can’t be seen from Fig. 23.4(b). According to the proposed method, E is calculated according to the sparse wavelet coefficients in each sub-band shown in Fig. 23.5. Energy percent is calculated by the ration of sub-band energy to total energy. And the two maximums in E determine the corresponding values in transformation scale S. With transformation scale S, knew is obtained. The SALSA denoises the original vibration signal with knew . The result of the denoised process is shown in Fig. 23.6 (b). The repetitive impulses in denoised signal are much clearer than the original vibration signal in Fig. 23.6(a). To further extract the repetitive impulses, the denoised signal is processed by Teager energy operator [21]. To separate the compound faults, MCKD is adopted. In Fig. 23.7, information of the extracted signal of inner race fault in time domain and frequency domain is revealed. The repetitive impulses of inner race fault can be clearly seen in Fig. 23.7(a). fi and its harmonics are displayed in Fig. 23.7(b).

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Amplitude

20 10 0 -10 -20

0

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Fig. 23.4 a The original vibration signal b The first SALSA denosied signal

The extracted signal of outer race fault is shown in Fig. 23.8. The repetitive impulses of outer race fault can be detected in Fig. 23.8(a). fo and its multiples are displayed in Fig. 23.8(b).

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Fig. 23.5 Energy percent in each sub-band

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Fig. 23.6 a The original vibration signal b The second SALSA denoised signal

As can be seen from the above experimental results, compound faults in rolling bearing can be diagnosed and separated, which validates the effectiveness of the proposed method.

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Fig. 23.7 a Extracted signal of inner race fault b Power spectrum of inner race fault

Amplitude

1.5 1 0.5 0 -0.5

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Conclusion

A method combining improved BPD with MCKD is proposed to diagnose the compound faults of rolling bearing in this paper. A new parameter called transformation scale is defined to update the Lagrange multipliers involved in SALSA. The transformation scale is set according to the two maximums of sub-band energy. With the new Lagrange multipliers, the denoising performance of BPD is improved and repetitive impulses are much clear in the time domain. To further separate the compound faults of rolling bearing, MCKD is used to deal with the denoised signal enhanced by Teager operator. The experimental results reveal that the presented method improves the denoising performance of SALSA effectively. Information about the compound faults of bearing in different domains (time and frequency) can be clearly revealed. Acknowledgements The project is supported by National Natural Science Foundation of China (51575102), Six talent peaks project in Jiangsu Province (JXQC-003), Fundamental Research Funds for the Central Universities of China (2242017K40112), Fundamental Research Funds for the Central Universities of China and Postgraduate Research&Practice Innovation Program of Jiangsu Province (KYCX18_0072).

References 1. Jiang, H., Li, C., Li, H.: An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis. Mech. Syst. Signal Proc. 36(2), 225–239 (2013). https://doi.org/10. 1016/j.ymssp.2012.12.010 2. Zhao, M., Lin, J., Xu, X., Li, X.: Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis. Sensors 14(11), 20320–20346 (2014). https://doi.org/10.3390/s141120320 3. Purushotham, V., Narayanan, S., Prasad, S.A.N.: Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT E Int. 38(8), 654–664 (2005). https://doi.org/10.1016/j.ndteint.2005.04.003 4. Wang, Y., He, Z., Zi, Y.: Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform. Mech. Syst. Signal Proc. 24(1), 119–137 (2010). https://doi.org/10.1016/j.ymssp.2009.06.015 5. Yan, X., Jia, M., Xiang, L.: Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum. Meas. Sci. Technol. 27(7), 075002 (2016). https://doi.org/10.1088/0957-0233/27/7/075002 6. Xue, H., Li, Z., Li, Y., Jiang, H., Chen, P.: A fuzzy diagnosis of multi-fault state based on information fusion from multiple sensors. J. VibroEng. 18(4), 2135–2148 (2016). https://doi. org/10.21595/jve.2016.16712 7. Lei, Y., He, Z., Zi, Y.: Application of a novel hybrid intelligent method to compound fault diagnosis of locomotive roller bearings. J. Vib. Acoust.-Trans. ASME 130(3), 1–6 (2008). https://doi.org/10.1115/1.2890396 8. Cui, L., Wang, J., Lee, S.: Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis. J. Sound Vibr. 333(10), 2840–2862 (2014). https://doi.org/10.1016/j.jsv.2013. 12.029

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9. Qin, Y.: A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 65(3), 2716–2726 (2017). https:// doi.org/10.1109/TIE.2017.2736510 10. Mcclure, M.R., Carin, L.: Matching pursuits with a wave-based dictionary. IEEE Trans. Signal Process. 45(12), 2912–2927 (1997). https://doi.org/10.1109/78.650250 11. Chen, S.S., Saunders, D.M.A.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001). https://doi.org/10.2307/3649687 12. Liu, B., Ling, S.: Gribonval R. Bearing failure detection using matching pursuit. NDT E Int. 35(4), 255–262 (2002). https://doi.org/10.1016/s0963-8695(01)00063-9 13. Zhang, X., Liu, Z., Miao, Q., Wang, L.: Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time–frequency atom dictionary. Mech. Syst. Signal Proc. 107, 29–42 (2018). https://doi.org/10.1016/j.ymssp.2018. 01.027 14. Cong, W., Meng, G., Zhu, C.: Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit. J. Intell. Manuf. 28(6), 1–15 (2015). https://doi.org/10.1007/s10845-015-1056-2 15. Yang, H., Mathew, J., Lin, M.: Fault diagnosis of rolling element bearings using basis pursuit. Mech. Syst. Signal Proc. 19(2), 341–356 (2005). https://doi.org/10.1016/j.ymssp.2004.03.008 16. Qin, Y., Mao, Y., Tang, B.: Vibration signal component separation by iteratively using basis pursuit and its application in mechanical fault detection. J. Sound Vibr. 332(20), 5217–5235 (2013). https://doi.org/10.1016/j.jsv.2013.04.021 17. Afonso, M.V., Bioucas-Dias, José M., Figueiredo, Mário A.T.: Fast image recovery using variable splitting and constrained optimization. IEEE Trans. Image Process. 19(9), 2345–2356 (2009). https://doi.org/10.1109/TIP.2010.2047910 18. Mcdonald, G.L., Zhao, Q., Zuo, M.J.: Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mech. Syst. Signal Proc. 33, 237–255 (2012). https://doi.org/10.1016/j.ymssp.2012.06.010 19. Hong, L., Dhupia, J.S.: A time domain approach to diagnose gearbox fault based on measured vibration signals. J. Sound Vibr. 333(7), 2164–2180 (2014). https://doi.org/10.1016/j.jsv. 2013.11.033 20. Zhang, D., Yu, D., Zhang, W.: Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes. Meas. Sci. Technol. 26(11) (2015). https://doi.org/10.1088/0957-0233/26/11/115003 21. Henríquez, R.P., Alonso, J.B., Ferrer, M.A., Travieso, C.M.: Application of the Teager-Kaiser energy operator in bearing fault diagnosis. ISA Trans. 52(2), 278–284 (2013). https://doi.org/ 10.1016/j.isatra.2012.12.006

Chapter 24

Realization of Condition Monitoring of Gear Box of Wind Turbine Based on Cointegration Analysis Biao Zhang, Chao Zhang, Haoran Duan, Yunting Ma, Jianjun Li, and Lingli Cui Abstract In this paper a method, based on the cointegration analysis of wind turbine SCADA data, is proposed to solve the problems such as misreporting and false alarm caused by affected by environmental effect in wind turbine SCADA system. Using cointegration theory to the wind turbine SCADA data will produce a stationary cointegration residual involving state information of wind turbine. Through analysis this residual can realize the wind turbine’s condition monitoring. The proposed method is using the experimental SCADA data as the research object what is acquired from a 1.5 MW DFIG under varying environmental and operational conditions in Gu Yang County, Bao Tou City, Inner Mongolia. A cointegration model was established using some of the nonstationary data and the model was validated with a set of known gearbox fault data. The result demonstrates that the proposed method can effectively restrain the response caused by environment and operation in SCADA data, accurately identify the running state of wind turbine, and simply and effectively realize the state monitoring of wind turbine.

B. Zhang
C. Zhang (&)
H. Duan
Y. Ma School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China e-mail: [email protected] J. Li School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China L. Cui Mechanical Engineering and Applied Electronics, Beijing University of Technology, Beijing 100124, China © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_24

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Introduction

It is well known that wind turbines are developing to high power and efficiency and any accidents of wind turbines will lead to a lot of maintenance costs to enterprises, and even threaten the people lives and property. Therefore, it is very important to identify and deal with faults in the early stage [1]. At present, the realization of wind turbine condition monitoring is to monitor the mechanical parameters, and the commonly used monitoring objects are oil, temperature, vibration and so on [2, 3]. In recent years, SCADA system has been introduced into the condition monitoring of wind turbine. The data collected by this system are many kinds (such as speed, temperature, electric energy and angle, etc.), and the amount of data collected by this system is large. Therefore, the condition monitoring of wind turbine can be realized by analyzing the key parameters. However, it is very difficult to analyze and explain the huge SCADA data of the wind turbine. The reliability of the data analysis result is insufficient because the wind turbine receives the influence of the environment and its operating factors. Therefore, it is very important to remove environmental and operational factors influence from monitoring objects of SCADA system [4]. In order to achieve this goal, a new SCADA data analysis method is found to remove the trend of the data and realize the condition monitoring of wind turbines. Cointegration theory [5, 6], proposed by Engle and Granger in the 1980s, is a widely used method for processing nonstationary signals in the field of economics [7]. In recent years, cointegration analysis has been frequently applied to engineering [8, 9]. The fundamental reason for the introduction of cointegration analysis is the nonstationarity of the measured signal. If a group of signals have cointegration relationship, the nonlinear trend of the signal and the influence of environment and other factors will be removed by the cointegration calculation and producing a cointegration residual, which represents the long-term dynamic equilibrium relationship among the variables. If the residual is stable, the detected object state is normal, on the other hand, the residual deviation from the stationary state means that the detected object is abnormal. The application of wind turbine SCADA data to condition monitoring has achieved some results [8–10]. However, there are some important problems in the analysis of wind turbine SCADA data. • Wind turbine SCADA data are generally collected, averaged and stored at 10-min intervals. The sampling frequency is very low and the fault characteristic frequency can not be collected. Therefore, the traditional data analysis methods (for example, Fourier transform method), Wavelet transform, etc.) is not suitable for the analysis of SCADA data [11]. • SCADA system is a long-running monitoring system, so it is difficult to analyze and interpret such a large amount of data because the data collected and recorded are not only of many kinds, but also of large quantity. • Most of the data collected by SCADA system are non-stationary time series. It is difficult to extract useful state information from it and the analysis period is

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long. It is necessary for workers to have a higher professional level with great labor intensity [4]. • Because each part of the wind turbine system is affected by the environment and operation factors, the data collected by the SCADA system will also be affected, and the change of the working condition and the environment will cause the change of the SCADA data, therefore, Changes in SCADA data do not necessarily mean wind turbine anomalies [5]. To sum up: using SCADA data for wind turbine condition monitoring needs to analyze a large number of low-frequency SCADA data, so we should avoid artificial analysis and interpretation of signals as far as possible, so many machine learning methods (such as in-depth learning Neural networks, etc.) are used to process SCADA data [12], However, there are many shortcomings in machine learning, such as the complexity of the algorithm, the long training cycle of samples and so on. In addition, the state of operation of wind turbines is largely affected by environmental factors, and the current state monitoring methods using SCADA data are used to monitor a key parameter individually, without taking into account external factors (e.g., wind speed, environmental temperature, etc.), causes the monitoring results to be unreliable. In order to solve the above problems, a SCADA data analysis method based on cointegration calculation is proposed. This method avoids the complicated sample training process, uses the method of vector autoregressive, and analyzes multiple objects at the same time. Through cointegration analysis to remove the common trends in the data, restrain the influence of external factors, and facilitate the analysis and interpretation of a large number of low-frequency data.

24.2

Cointegration Analysis

24.2.1 Stationarity and Autoregressive Model of Time Series The random sequence obtained by sampling the continuous stochastic process at equal intervals is called the time series. The commonly used methods of stationarity test of time series are argument Dickey- fuller (ADF) test and Phillips & perron (PP) test etc. [13]. Auto regression (AR) model is the most commonly used model to deal with stationary time series. The autoregressive model describes the linear relationship between any time t of the sequence {xt} and the values of the previous p [7, 14], defined as follows. xt ¼ /1 xt1 þ /2 xt2 þ


þ /p xtp þ et

ð24:1Þ

Where et is an independent white niose process with zero mean. /1 ; /2 ;


; /p is coefficient of model. P is an order of model. The model (24.1) is called the first-order autoregressive model, which is denoted as ARð1Þ.

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The above theory can also be used in vector time series {Xt}. The stationarity of vector time series requires that each time component be a stationary sequence itself. The statistical relationship between each component sequence is also required to be stationary. The vector time series satisfying the above relations is called vector stationary time series. For the vector stationary time series, the Vector Auto regression, (VAR) model [7, 14] can be established, and the model of each variable is expressed as: Xt ¼

p X

Ai Xti þ Vt

ð24:2Þ

i¼1

Where fXt g is n stationary time series with zero mean. A1 ;


; An are coefficient matrix. Vt ¼ v1 ; v2 ;


; vn are white noise. In engineering practice, most of the data are nonstationary time series, if the nonstationary time series directly regression will lead to pseudo-regression problem, the conclusion is not consistent with the actual, in order to solve this problem will often be used in the process of integration.

24.2.2 Integration In order to avoid false regression to nonstationary time series, it is necessary to convert nonstationary time series into stationary one. Generally, the method to deal with this problem is to convert the time series into difference series. If the time series becomes stationary time series after d times difference operation, then it is called d-order simple integrality, which is denoted as I ð1Þ [7]. Special, when d = 1: Dyt ¼ yt  yt1 ¼ et

ð24:3Þ

If et is a stationary time series, it is called the first order integral sequence, denoted as I ð1Þ.

24.2.3 Cointegration Based on the above-mentioned concept of stationarity, the concept of cointegration is proposed. The cointegration theory describes the long-term equilibrium relationship between two or more non-stationary variables. Although these variables are nonstationary time series, their linear combination (equilibrium relation) is a stationary time series [7]. E-G test [8] is a cointegration test between two variables. If fyt g ¼ fy1t ; yt gT is a series of n-dimensional time vectors, If

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(1) The component sequences of fyt g are all I ð1Þ time series, that is, all of them are first-order integral time series. (2) if there exists a vector b 6¼ 0 so that lt ¼ bT yt  I ð0Þ is called cointegration between fyt g components, b is called cointegration vector. Normalize b to: nt ¼ y1y  b1 y2t þ c

ð24:4Þ

If et is a stationary sequence, then there is a cointegration relationship between fyt g on the contrary there is no cointegration relationship. In practice, it is often necessary to analyze multiple variables, so it is necessary to use Johansen test method, based on VAR model [11], is an effective test method for multivariable and multi-cointegration relations. For the multivariable Yt ¼ ðy1 ; y2 ;


; ynt Þ, if there is exist ðn  r Þ—dimension matrix B, formula 24.4 extends to: 8 T 9 8 b Y > > > > > > > 1T t > > < b2 Yt = < T þ B Yt ¼ . > > > > .. > > > ; > : T > : br Yt

c1 c2 .. . cr

9 > > > =

8 9 n > > > > > 1t > < n2t = ¼  Ið0Þ . > > > > > .. > > > > ; : ; nrt

ð24:5Þ

Let nt ¼ BT Yt be the residual sequence, the ADF test was carried out to judge its stationarity, and the model test was realized.

24.3

Application of Cointegration Analysis Method in Condition Monitoring of Wind Turbine

24.3.1 Wind Turbine Experimental Data In this paper, the SCADA data of 1.5 MW doubly-fed wind turbine is used as the research object, and the co-integration model is established by using the SCADA data collected at the initial stage of operation (at which the wind turbine parts are not abnormal). Four sets of variables are selected as the study object, and the response variable gets its location as shown in Fig. 24.1

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Wind speed (m/s)

(a) 14 12 10 8 6 4 0

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Fig. 24.1 Variable position

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The selected data is shown in Fig. 24.2, (a)–(d): As can be seen from Fig. 24.2, wind speed, as the input of wind turbine, has an important impact on other parameters of wind turbine. The four groups of data shown in Fig. 24.2 have a similar trend with wind speed, and the relationship between each parameter and wind speed is shown in Fig. 24.3. From Fig. 24.3, it can be seen that there is a nonlinear relationship among main shaft speed, generator power, generator speed and wind speed of wind turbine, and the corresponding parameters are increasing with the increase of wind speed. This indicates that there may be a common trend relative to wind speed in this set of parameters, which can be removed by cointegration calculation.

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Table 24.1 ADF test results of parameters Project

Var 1

Diff (Var1)

Var 2

Diff (Var2)

Var 3

Diff (Var3)

Var 4

Diff (Var4)

Statistic

−2.21

−8.21

−0.86

−8.35

−2.35

−5.37

−0.85

−7.66

Critical value

−2.56

−2.56

−2.56

−2.56

−2.56

−2.56

−2.56

−2.56

Result

Nonstationary

Stationary

Nonstationary

Stationary

Nonstationary

Stationary

Nonstationary

Stationary

24.3.2 Establishment of Model The cointegration model of four groups of variables is established by using the above-mentioned method. Step 1: the first step is to test the unit root of the first-order difference sequence of the selected data machine using the ADF test method. The test results are as shown in Table 24.1. In the table, variable 1 represents wind speed, variable 2 represents speed of impeller, variable 3 represents speed of generator, variable 4 represents generator power, when statistical value is less than critical value, time series is stable, and vice versa. Table 24.1 shows that all variables are nonstationary time series, and their first-order difference series are stationary time series, which indicates that the selected parameters are first-order simple integral series, that is I ð1Þ.

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Table 24.2 Result of Johansen test

r

JohansenH0 test

Test statisticQr

1% critical value

0 1 2 3

1 1 1 0

1592.41 565.14 126.31 4.19

54.68 35.46 19.93 6.63

Table 24.3 Results of ADF test for cointegration residuals

Project

ADF test statistic

1% critical value

n1 n2 n3

−38.25 −22.54 −11.25

−2.56 −2.56 −2.56

The second step: using the method described in Sect. 24.1.3 to analyze four groups of variables. Johansen H0 co-integration relation test assumes that there is no co-integration relationship among the variables, but rejecting the hypothesis indicates that there is co-integration relationship between variables. The results are as shown in Table 24.2: Table 24.2 shows the number of cointegration relations. The result of JohansenH0 test is 1 to accept the original hypothesis and 0 to reject the original hypothesis. From the table, we can see that there are three co-integration relations among the variables at a confidence level of 1%. At the same time, the coefficient matrix and intercept are estimated. 2

1 6 - 85:6670 B¼6 4 - 0:0031 0:8063

1 0:1048 - 0:0050 - 0:0009

3 2 3 1 - 0:083770 - 27:1854 7 7 7C¼6 4 - 7:160651 5 0:1020 5 9:432702 0:0164

Establish a cointegration model: n ¼ b 1 y 1 þ b2 y 2 þ b3 y 3 þ b4 y 4 þ c

ð24:6Þ

Three sets of cointegration residuals were obtained and the residuals were tested by ADF. The results are as shown in Table 24.3:

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The statistic in Table 24.3 is less than the critical value, indicating that the residual sequence is stable. From the above table, it is found that the three groups of residual series are stationary time series. Because the Johanson test method is based on the statistical method of the maximum eigenvalue, the first residual sequence has the largest eigenvalue, which is the most suitable for state monitoring. Then the cointegration model is determined as follows: nt ¼ y1  85:6670  y2  0:0031  y3 þ 0:8063  y4  0:083770

ð24:7Þ

The first residual sequence is shown in Fig. 24.4: the maximum and minimum limit values corresponding to the residual theory of the wind turbine in normal operation are represented by two horizontal dot lines in the diagram. In this range, the operation state of wind turbine is normal, On the contrary, the state is abnormal.

24.3.3 Identification of Abnormal States The model (24.7) is validated with a set of SCADA data with known gearbox failures, and the selected data is shown in Fig. 24.5: Fault description: the figure shows that the wind speed is above 6 m/s, and the wind turbine spindle speed, generator speed and power are 0. Before this (in ellipse), we can see that the wind speed is more than 6 m/s, and the wind speed is more than 6 m/s. The rotation speed of the main shaft of the generator remains at 17 RMP, while the speed of the generator and the power of the generator show a downward trend. At this point, it can be seen that this phenomenon may be caused by the failure of the gearbox bearing or journal of the wind turbine. At this time, the parameters of the wind turbine do not exceed the threshold set by the system, so the SCADA system still does not find the fault. Until xx261, the parameters exceed the threshold set by the system, and the SCADA system sends out an alarm and enters the state of downtime and overhauling.

residual

4 2 0 -2 -4 0

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10 8 6 100 sample

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(c)

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0

1500 1000

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500

x=261

0 0

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(b)

main shaft speed (RPM)

Wind speed (m/s)

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200

15 10 5

x=261 0

100 sample

200

(d) 2000 1500

Fault evolution

1000 500

x=261 0

100 sample

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Fig. 24.5 Original SCADA failure data

residual

0

-10

x=256

-20 0

50

100

150

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sample Fig. 24.6 Cointegration residual of fault data

The cointegration residual is calculated by model (24.7) as shown in Fig. 24.6: Figure 24.6 shows that for the 256th sample, the cointegration residual deviates from the stationary state and exceeds the set limit value, indicating that the operating state of the wind turbine is abnormal, about 40 min before the time when the failure was discovered by the SCADA system.

24.4

Conclusion

In this paper, a wind turbine condition monitoring method based on co-integration analysis is presented. The wind turbine data with rated load of 1.5 MW are used to model and verify, and a set of known gearbox fault data are used to test. The results show that the normal and normal state of wind turbine can be distinguished by this

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method only by analyzing the residual error. This method can analyze and interpret a large amount of low-frequency SCADA data automatically and save manpower and calculation time. Compared with the traditional big data analysis method, the greatest advantage of this method is that the calculation is simple. And the state can be identified at the beginning of the fault.

References 1. Zhu, T., Zhang, C.: The fault diagnosis approach for bearing of combining stochastic resonance de-noising with ELMD. Mach. Design Res. 34(3), 103–107 (2018). (In china) 2. Hameed, Z., Hong, Y.S., Cho, Y.M., et al.: Condition monitoring and fault detection of wind turbines and related algorithms: a review. Renew. Sustain. Energy Rev. 13(1), 1–39 (2009) 3. Márquez, F.P.G., Tobias, A.M., Pérez, J.M.P., et al.: Condition monitoring of wind turbines: techniques and methods. Renew. Energy 46(none), 169–178 (2012) 4. Meng, P., Li, Q.: Hidden trouble analysis and treatment method of wind turbine based on SCADA fault alarm data. Wind Energy (01), 94–98 (2019). (in china) 5. Engle, R.F., Granger, C.W.J.: Cointegration and error-correction: representation, estimation and testing. Econometrica 55, 251–276 (1987) 6. Johansen, S.: Statistical analysis of cointegration vectors. J. Econ. Dyn. Control 12(2), 231– 254 (1988) 7. Zhang, S., Pan, Z., Guo, M.: Co-integration Theory and Wave Mode. Tsinghua University Press, Beijing (2014). (in china) 8. Yao, G.: The application of co-integration theory in automobile engine system fault diagnosis. Nanjing University of Aeronautics and Astronautics, Nanjing (2010) 9. Qian, C., Kruger, U., Pan, Y.: Application of cointegration testing method to condition monitoring and fault diagnosis of non-stationary systems. Acta Petrol. Sinica 23(9), 69–76 (2007) 10. Dao, P.B., Klepka, A., Pieczonka, Ł., et al.: Impact damage detection in smart composites using nonlinear acoustics—cointegration analysis for removal of undesired load effect. Smart Mater. Struct. 26(3), 035012 (2017) 11. Yi, C., Lv, Y., Xiao, H., et al.: Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment. Meas. Sci. Technol. 29(4), 045104 (2018) 12. Du, M., Yi, J., Guo, J., Cheng, L., Ma, S., He, Q.: Research on the application of neural networks on wind turbine SCADA data analysis. Power Syst. Technol. 42(07), 2200–2205 (2018) 13. Xia, N.: The research on the comparison of unit root of DF, ADF and PP. Quant. Tech. Econ. 9, 129–135 (2005) 14. Li, Z., Ye, A.: Advanced Econometrics. Tsinghua University Press, Beijing (2000)

Chapter 25

Research on Surge Control of Centrifugal Compressor Based on Reinforcement Learning Kun Jiang, Yang Xiang, Tianyou Chen, and Chaojun Jiang

Abstract Surge control is always an important issue in compressor health management. Although the traditional PID anti-surge methods has made some achievements, the adaptation across variable working conditions is still a challenge. Meantime, reinforcement learning (RL) has been proven strong adaptability and high accuracy in recent years, however, few researches attempt RL in surge control applications. Therefore, this paper proposes a RL anti-surge method for compressor. In specific, a novel architecture based on Actor-Critic algorithm is presented, and the relative structures are re-designed aiming at the issue. Moreover, the simulation of supercharging system is conducted to validate the proposed method, in which the compressor operation models are static and dynamic, respectively. The results show that the agent of Actor-Critic can sense the state of the compressor in real time and then output the appropriate anti-surge valve opening which not only avoids the surge, but also ensures the working efficiency at a high level. Comparing with the traditional PID anti-surge method, the superiority of the proposed method such as the strong adaptability across variable working conditions is verified.

25.1

Introduction

Compressor is the core component of supercharging system, has always been the research object of experts and scholars, and surge is undoubtedly one of the main faults of it. When the compressor inlet flow decreases, it deviates from the designed working condition, and the compressor outlet pressure does not match the pipeline system, resulting in severe vibration of the compressor at low frequency and periodicity. Surge deteriorates compressor performance, reduces pressure head and working efficiency significantly, and also causes damage to compressor blades [1]. Surge control technology can be divided into two categories: one is adopted in the K. Jiang
Y. Xiang (&)
T. Chen
C. Jiang School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_25

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design of compressor, which enlarges the range of stable working conditions through parameter design; the other is adopted for the combined operation of compressor and pipeline network, such as the use of anti-surge device to keep compressor running in safety zone. The second kind of anti-surge methods can be also divided into passive anti-surge control technology and active anti-surge control technology. Since a certain surge margin needs to be considered when it comes to the passive anti-surge control technology for the compressor to avoid the surge zone, which not only reduces the working range of the compressor, but also sacrifices certain performances, such as unable to meet the requirements of the operation in variable working conditions. So active anti-surge control technology emerge as the times require. Around 1986, Greitzer and Moore put forward the concept of active control for the instability of turbomachinery compression system [2]. From then on, the study of active surge control began. With the progress of science and technology, experts and scholars have proposed many active anti-surge control methods. According to the anti-surge control strategy, they can be divided into traditional PID active control method and current popular intelligent anti-surge active control technology. As a relatively mature control technology, the PID control technology plays an important role in the active anti-surge control of compressors. The mainstream design still adopts the traditional PID algorithm [3, 4]. Due to the influence of non-linearity, uncertainty and time-varying of the system, traditional PID control cannot fully meet the control requirements, and sometimes even fails to reach the minimum control requirements. Therefore, some new control method are needed to make up for the deficiency of PID control. In recent years, intelligent anti-surge control has become a research hotspot, and has achieved abundant results. For the intelligent control of surge, the current popular theoretical research mainly focuses on the aspects of fuzzy control [5, 6], neural network control [7, 8] and model predictive control [9, 10]. These intelligent control technology are mature in theoretical research and have been applied in a small scale. However, since the stability and reliability of machine operation are especially emphasized in engineering application, constant and complicated tests during applications are needed. With the development of computer and artificial intelligence, there are an increasing number of new ideas for anti-surge control of compressor. In 2016, DeepMind team developed AlphaGo program based on deep RL algorithm, which defeated Korean Go master Li Shishi, it has aroused a research boom of RL. RL has been widely used in unmanned driving, robotics and closed-loop adaptive control owing to its strong perception and decision-making ability. Besides, one of the hotspots in the field of control is the application of RL to realize the self-adaption of agent behavior to environment and the optimization of controller. Therefore, this paper applies RL method to compressor anti-surge control, by which the agent can take corresponding decision-making actions according to the perceived state information of centrifugal compressor, timely prevent the occurrence of surge.

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RL and Actor-Critic Algorithm

RL aims at maximizing the sum of long-term rewards. By exploring the environment independently, perceiving the environment, applying actions to influence the environment and obtaining rewards from the environment, the optimal strategy can be obtained through continuous learning in the interactive process [11]. Its principle is shown in Fig. 25.1 The anti-surge problem of compressor in this paper can be equivalent to a reinforcement learning task, the state information of compressor is expressed as the observation of reinforcement learning, and the opening adjustment of anti-surge valve is expressed as the action of reinforcement learning. Because of the continuity of compressor model, compressor surge problem can be studied based on continuous and endless reinforcement learning framework. Based on the above analysis, this paper chooses a model-independent and online reinforcement learning framework to design anti-surge control algorithm for compressor, called Actor-Critic framework, whose simplified model block diagram is shown in Fig. 25.2. Where f denotes the hyperparameter of the Actor network, lgpf(St, At) is the logarithm of the output value of the Actor network on the premise that the current observation is St, the action is At, the hyperparameter is f, and the learning strategy is p. TD is the time difference algorithm; TD_est and TD_target are respectively the estimated value and target value of value function under the time difference algorithm; TD_error is the training error of the Critic network. Actor and Critic used in this paper are both multi-layer BP neural network structures. Actor is an action output module, and its function is trained by constructing a strategy gradient, so as to output the probability of various actions. The loss function of the Actor network is: LðfÞ ¼ lgpf ðSt ; At Þ
Vt

ð25:1Þ

Where Vt is value function, which is generated by Critic network. The gradient calculation of LðfÞ is called the policy gradient of the Actor network, which can be expressed as: rLðfÞ ¼ brf lgpf ðSt ; At Þ
Vt

ð25:2Þ Interactive Process

Fig. 25.1 The principle of RL

Environment Observation and Reward

Action

Agent Strategy

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Fig. 25.2 Actor-Critic framework for surge control of compressor

training anti-surge valve

(TD_error).lgπ (St,At) lgπ (St,At)

Actor

observation

TD_error

Compressor

TD_est

Critic training

Reward

TD_error

TD

TD_target

In the formula, r denotes the gradient, and b is the learning rate of the strategy gradient, b 2 ð0; 1Þ. The gradient descent method is used to train the strategy gradient, and finally the output of the Actor network is the probability distribution of different actions. Critic is a value evaluation module. Its function is to evaluate the value of each action by time difference algorithm (TD) [13] according to the observed value and reward value, and transmit the value function to Actor to provide reference for its action selection. The output value of Critic is the estimated value of the valve function (TD_est) in time difference algorithm, when feeding the reward r into Critic, then the target value (TD_target) of the value function is calculated as follows: TD targetðSt ; At Þ

TD estðSt ; At Þ þ a½r þ cmaxðTD estðSt þ 1 ; AÞÞ  TD estðSt þ 1 ; At Þ

ð25:3Þ In the formula, TD estðSt ; At Þ represents the value function under current observations and actions. maxðTD estðSt þ 1 ; AÞÞ is the maximum of value function under the next observation state, where ðSt þ 1 ; AÞ denotes the action that can maximize the value function in the St þ 1 observation state. a is the learning rate and c is the discount factor, a; c 2 ð0; 1Þ. Suppose: TD error ¼ TD targetðSt ; At Þ  TD estðSt ; At Þ

ð25:4Þ

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The gradient descent method is used to train Critic by using TD_error as the loss function of Critic. At the same time, TD_error is regarded as the value function Vt in the actor, and the loss function L (Actor) of the actor network is as follows. LðActor Þ ¼ TDerror
½lgpf ðSt ; At Þ

ð25:5Þ

When the loss function of the Actor network in formula (25.5) is trained by gradient descent method, the output of the Actor is the probability value of each action. By constantly trying and exploring, the agent can strengthen the choice of action which is beneficial to anti-surge, avoid the choice of action which causes the trend of surge, and finally learn a good anti-surge strategy.

25.3

Modeling of Centrifugal Compressor

In this paper, the compressor is modelled in Aspen HYSYS. HYSYS has a compressor module, which can accurately simulate the actual operation of centrifugal compressor according to the ASME standard [12], and provides real-time flow, temperature, power, efficiency and other values that vary with time during the operation of compressor. HYSYS also provides an ordinary anti-surge controller for the simulation of centrifugal compressor. After the user specifies the surge line and corresponding control parameters, HYSYS can simulate how the anti-surge valve operates when the compressor has surge trend and the change of relevant parameters. The centrifugal compressor model is established based on the data provided by a single-stage centrifugal compressor. Relevant parameters of the compressor are shown in Table 25.1. The performance test temperature of the compressor is 14 °C and the outlet pressure is 9950 kPa. Specific test data are shown in Fig. 25.3. Based on the above test data of centrifugal compressor, the static model of centrifugal compressor is established in HYSYS to verify material balance. The static model uses PR equation, which is commonly used in the field of petroleum, natural gas and chemical industry, to calculate strictly physical properties. After verifying the physical balance in the static model, we switch the model from static mode to dynamic mode, as shown in Fig. 25.4.

Table 25.1 Parameters of the compression system Rotor radius

Inlet diameter

Out diameter

Duct length

Plenum volume

Initial flow

Initial inlet pressure

0.2 m

0.055 m

0.055 m

1.8 m

1.5 m3

270.1 kg/s

7301 kPa

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(a) Pressure Head-Flow Curve

(b) Efficiency-Flow Curve

Fig. 25.3 Performance curve of centrifugal compressor

Fig. 25.4 The dynamic model of supercharging system

The natural gas and the return flow of the loop converge on the Scrubber, and then enter into the compressor. Natural gas is compressed by the compressor and then cooled by a cooler, following that does it pass through the separator to output or enter the loop, and an anti-surge control valve is set in the loop to control the flow of loop. The boundary condition of the compressor is set as pressure specification in the outlet, which is 9.95 kPa, and the feed pressure is 7.102 kPa. Besides, in order to ensure that the anti-surge valve can provide enough anti-surge flow when the compressor has surge tendency or when surge occurs, the anti-surge valve is chosen with large anti-surge valve coefficient Cv and is set to open quickly, which means the surge trend and the starting and stopping of the anti-surge valve action occur in a very short time, so the integral step size of HYSYS is set very small (1 ms) and the real time is used.

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Because the anti-surge controller of HYSYS is relatively simple and cannot be modified directly, the anti-surge control strategy based on Actor-Critic algorithm designed in this paper cannot be directly simulated on HYSYS, therefore, based on ActiveX control technology, a hybrid compressor anti-surge simulation platform is built between MATLAB and HYSYS. ActiveX control technology is used to realize real-time interconnection between MATLAB and HYSYS, we compile Actor-Critic algorithm in MATLAB to adjust the anti-surge valve of compressor in HYSYS in real-time, that means Actor-Critic algorithm in MATLAB is equivalent to anti-surge controller or agent of RL, and compressor system in HYSYS is equivalent to environment of RL.

25.4

Anti-surge Control Strategy Based on Actor-Critic

25.4.1 Algorithmic Design When the connection between MATLAB and HYSYS is established, the operation state parameters of the compressor need to be read in real time. The anti-surge control strategy designed in this paper needs to read the inlet flow (Q), inlet pressure (P), polytropic pressure head (H), speed (n) and temperature (T) of the compressor, and then output the anti-surge valve opening to the compressor. The above five state parameters are used as the state input of Actor-Critic algorithm and the opening of anti-surge valve as the action output of Actor, anti-surge valve opening is 0–100% continuous value. The shape of the Actor network is shown in Fig. 25.5.

Hidden layer

Fig. 25.5 The shape of Actor Input layer

Out layer

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Both Actor and Critic networks adopt BP neural network. Actor network has five neurons in input layer, 12 neurons in hidden layer and two neurons in output layer, five input feature are Q, P, H, n and T. In order to speed up network training, the input features are pre-processed and narrowed to −10–10. Hidden layer uses relu activation function. The two values of output layer are the mean (l) and variance (r) of Gauss distribution respectively, the neuron outputting the l uses sigmoid activation function, then u is multiplied by 100 to correspond to the opening range of the anti-surge valve, the neuron that outputs the r uses softplus activation function. After obtaining l and r, random sampling of Gaussian distribution with mean value 100 times of l and variance r, then apply the random sampling value to the anti-surge valve in HYSYS, where compressor system respond to the action and moves to the next state. The output of r and the construction of Gaussian distribution are designed to make the agent have the function of exploring and utilizing at the same time. Critic network structure differs from Actor in that there is only one neuron in the output layer and the output layer uses linear activation function. The output value of Critic network represents the value of a state, Critic needs to calculate the value of the current state and the value of the next state at each step. A good anti-surge control strategy should not only ensure the safety of centrifugal compressor operation, but also improve the working efficiency and economy of compressor operation. As a result, when the compressor is in danger of surge, the anti-surge valve can prevent surge accurately, and ensuring that the opening of anti-surge valve will not cause excessive gas reflux. That means that the goal of Actor-Critic learning is very important and directly determines the quality of anti-surge strategy, while the design of reward function plays a guiding role in the goal of Actor-Critic. By observing the performance curve of centrifugal compressor, it is found that when the operating point of the compressor at a certain speed is near the optimal operating line in Fig. 25.6(a), the compressor not only has a high pressure head, but also has the highest efficiency in Fig. 25.6(b). That means that the compressor can not only prevent surge, but also maintain high economy when

(a) The optimal operating line in H-Q figure

(b) The optimal operating in E-Q figure

Fig. 25.6 The optimal operating line of centrifugal compressor

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running near the optimal operating line. Therefore, in the case of surge, the operating point of the compressor should be adjusted to the optimal operating line as far as possible. We fit the optimal line with a straight line in H-Q figure, the fitting linear equation is shown in Eq. 25.6, and the goodness of fit is measured by coefficient of determinate R2 , which is 0.9968, indicating that the goodness of fit is very good. H ¼ 0:8012Q  4311:6

ð25:6Þ

So the closer the operating point is to the fitting line, the better. Then, the reward function can be designed as Eq. 25.7, take the opposite number of distances and narrow it by a hundred times. r ¼ 0:01

j0:8012  Q  H  4417:3j qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 0:88672 þ ð1Þ2

ð25:7Þ

25.4.2 Anti-surge Control Strategy Simulation After the establishment of compressor system and the design of Actor-Critic algorithm, we carry out the dynamic simulation of anti-surge strategy. The initial learning rate of Actor network and Critic network is 0.001 and 0.01 respectively, and they decays as Eq. 25.8. a ¼ 0:95episode a0

ð25:8Þ

Where episode express the number of training rounds, and a0 is initial learning rate of two networks. Besides, the value discount coefficient is 0.9 and the reward discount coefficient is 0.99, moment algorithm is used to speed up training, in which the super-parameter is 0.9. Actor-Critic algorithm is trained for 600 episodes, 120 steps per episode. In particular, in order to keep the compressor system in its initial setting surge operating point before each episode of training, so as to carry out the next episode of training, we use code to close the anti-surge valve before each episode of training and suspend the program in MATLAB for 0.6 s after closing the anti-surge valve. Firstly, three surge working conditions are simulated, which are the sudden drop of inlet flow to 120 kg/s, 100 kg/s and 80 kg/s respectively. In the dynamic model, Actor-Critic training is carried out to see if the running point can eventually reach the optimal operating line. The operating points before and after training under three working conditions are shown in Fig. 25.7.

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(a) 120kg/s

(b) 100kg/s

(c) 80kg/s

Fig. 25.7 Before and after training under three surge working conditions

Fig. 25.8 Efficiency of last episode

It can be seen from the above three figures, operating points under three surge working conditions eventually reach the optimal operating line, with a small amount of pressure head loss and high operating efficiency. In addition, we can also see that the lower the inlet flow under surge condition, the more reflux is needed to control surge, and the more pressure head loss is caused. Besides, the efficiency curve of the last episode under three surge conditions is shown in Fig. 25.8, it can be seen that the compressor can quickly reach the safe area from the surge area and make small changes near the maximum efficiency, and the lower the inlet flow is under surge condition, the smaller the optimal efficiency that can be achieved.

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25.4.3 Contrast In order to compare the RL anti-surge control system with traditional PI anti-surge control system under constant parameters, the above three surge working conditions are simulated by using the PI anti-surge controller of HYSYS. After optimizing the parameters, we chose 0.75 as Kp, 2 s as Ti and 151.6 kg/s as the flow setting point of PI controller according to three surge conditions. The operating points before and after PI control under three working conditions are shown in Fig. 25.9. Meanwhile, contrast the surge control time of PI anti-surge system with that of RL anti-surge system after completion of training, the results are shown in Table 25.2. As can be seen from the Fig. 25.9, except for the surge condition in which the inlet flow drops sharply to 100 kg/s, the operating point can finally reach the optimal operating line, however, the final stable operation point is far from the optimal operation line under the other two surge working conditions. The data in Table 25.2 shows that the surge control time of the RL anti-surge system is much shorter than that of the PI anti-surge system under constant parameters, because the opening of the anti-surge valve of the former is adjusted to the most suitable position at one time, while the latter needs constant adjustment. Through the analysis of Figs. 25.7, 25.9 and Table 25.2, we can see that the performance of RL anti-surge control is better than that of traditional PI anti-surge control with constant parameters under three working conditions, which shows that RL anti-surge control system has better adaptability and timeliness, and can prevent compressor surge more effectively.

(a) 120kg/s

(b) 100kg/s

(c) 80kg/s

Fig. 25.9 Before and after PI control under three surge working conditions

Table 25.2 Contrast of control time

Actor-Critic PI

120 kg/s

100 kg/s

80 kg/s

1.73 s 16.65 s

1.71 s 15.84 s

1.72 s 18.52 s

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Conclusion

In this paper, the dynamic anti-surge system of centrifugal compressor is established by the way of MATLAB and HYSYS interconnection, Actor-Critic algorithm in RL domain is used to control surge of centrifugal compressor, and the difference between RL anti-surge control system and traditional PID anti-surge control system under constant parameters is compared. The simulation results show that the RL anti-surge system can prevent surge without from losing too much pressure head and maintain high operation efficiency, and the RL anti-surge control system has a superiority over traditional PID anti-surge control system in terms of adaptability under constant parameters What must be pointed out is that although the RL control system has the advantages of strong adaptive ability and online learning function, it also has imperfections for its complex algorithm and difficulties to build the framework at the same time. But on the whole, it is practical to apply RL method to compressor surge control, and RL adaptive control is also an important research field of mechanical equipment unmanned in the future.

References 1. Gravdahl, J.T., Egeland, O.: Compressor Surge and Rotating Stall: Modeling and Control. Springer, Heidelberg (2012) 2. Moore, F.K., Greitzer, E.M.: A theory of post-stall transients in axial compression system. Gas Turb. 13(2), 32–39 (1986) 3. Chen, H.Z.: Research of compress anti-surge based on the integral separation PID algorithm. Adv. Mater. Res. 189–193, 567–570 (2011) 4. Wen, L., Gao, L., Dai, Y.: Research on system modeling and control of turbine-driven centrifugal compressor. In: 2011 6TH IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 2090–2095 (2011) 5. Hafaifa, A., Laaouad, F., Guemana, M.: A new engineering method for fuzzy reliability analysis of surge control in centrifugal compressor. Am. J. Eng. Appl. Sci. 2(5), 676–682 (2009) 6. Chen, W.J., Niu, Y.L., Xia, J., Huang, Z.W.: Fuzzy controller in the application for anti-surge of the axial-flow compressor. Appl. Mech. Mater. 573–576 (2011) 7. Jokar, A., Zomorodian, R., Ghofrani, M.B., Khodaparast, P.: Active control of surge in centrifugal compressors using a brain emotional learning-based intelligent controller. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 230(16), 2828–2839 (2015) 8. Javadi Moghaddam, J., Farahani, M.H., Amanifard, N.: A neural network-based sliding-mode control for rotating stall and surge in axial compressors. Appl. Soft Comput. J. 11(1), 1036– 1043 (2011) 9. Jones, K., Cortinovis, A., Mercangoez, M., Ferreau, H.J.: Distributed model predictive control of centrifugal compressor systems. IFAC PapersOnLine 50(1), 10796–10801 (2017)

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10. Torrisi, G., Grammatico, S., Cortinovis, A., Mercangoz, M.: Model predictive approaches for active surge control in centrifugal compressors. IEEE Trans. Control Syst. Technol. 25(17), 1947–1960 (2017) 11. Sutton, R., Barto, A.: RL: An Introduction. MIT Press, Cambridge (1998) 12. Hafaifa, A., Rachid, B., Mouloud, G.: Modelling of surge phenomena in a centrifugal compressor: experimental analysis for control. Syst. Sci. Control Eng. 2, 632–641 (2014) 13. Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. IEEE Trans. Neural Netw. 9(5), 1054 (1998)

Chapter 26

Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Railway Switch Failures Daniela Narezo Guzman, Edin Hadzic, Benjamin Baasch, Judith Heusel, Thorsten Neumann, Gerrit Schrijver, Douwe Buursma, and Jörn C. Groos

Abstract Railway switches are a crucial asset since they enable trains to change tracks without stopping. Switch failures can compromise a larger part of the railway infrastructure, which can have a negative impact on reputation and revenues. Switches are a costly asset due to frequent inspections, maintenance and renewal of components. Therefore knowing current and future asset condition can be helpful in optimizing switch maintenance to prevent complete failure. The goal of the research presented here is to exploit switch condition monitoring and weather data to identify switch failures on an early stage. Approaches for detection of anomalous switch behavior and prediction of failures are developed. To validate the anomaly detection results obtained by applying the Isolation Forest algorithm, two different annotated data sets are considered. It is found that the anomaly detection approach performs well when applied to a switch, which is characterized by narrow feature distributions within temperature bins. Moreover first results from an Autoregressive Integrated Moving Average model for failure evolution prediction are presented.

26.1

Introduction

Railway turnout systems consist of one or more switches and a crossing. The switch blades are the moving parts, which once in one of their end-positions, enable trains to change direction without stopping. The movement of the blades into an end-position is powered by one or more point machines. Standard switches are D. N. Guzman (&)
B. Baasch
J. Heusel
T. Neumann
J. C. Groos Institute of Transportation Systems, German Aerospace Center, Berlin/Braunschweig, Germany e-mail: [email protected] E. Hadzic
G. Schrijver
D. Buursma Strukton Rail, Utrecht, The Netherlands © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_26

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complex electro-mechanical devices with more than twenty potential failure modes [1] caused by contamination, loose or damaged parts, wear, blades blockage, among others. Turnout systems are one of the railway infrastructure assets that mostly affect the system availability causing train delays. According to [2] almost one third of the total maintenance cost of railways is spent for switches and crossings. Therefore there is a need to detect upcoming failures early enough in order to plan ahead maintenance activities and prevent switches from failing. Condition monitoring is the continuous update of an asset health state made available through sensors. In railway turnout systems force, voltage and current sensors provide a useful representation of the energy required by the point machine to relocate the blades from one end position to the other [3]. Such systems enable efficient ways to repair and maintain these assets, improving reliability, availability and safety of the railway system, while reducing costs. Considering this, a number of different approaches have been developed for detecting, diagnosing and predicting failures [4–7].

26.1.1 Switch Normal and Abnormal Behavior A switch behaves normally when it repositions the blades from the start to the end lock position within a certain time and with the point machine consuming a certain power. Under normal behavior the corresponding measured current curve (CC) has a particular shape. The particular features of CC systematically vary with temperature (see Fig. 26.1) and other weather conditions (e.g. rain and air humidity), thus one can speak of a range of normal states. This range is switch- and repositioning direction-specific. A new functioning switch is expected to behave normally once the blades and the switch-glides have been adjusted. These adjustments can change with time, usage, repairs and maintenance, causing the behavior of a functioning switch to shift into a different range of normal states [8]. Therefore the definition of normal switch behavior is not necessarily static over several years of operation. Switch incipient failures become more severe with time and usage; in such cases switches may keep functioning even when their condition is progressing towards a complete failure. Abrupt failures are typically of random nature and can instantly compromise switch functioning (e.g. a stone blocking the blades). Certain incipient or abrupt failures modify the shape of monitored CC and, as consequence, the particular features derived from them, reflecting the abnormal behavior of the switch. SR’s in-house developed remote system monitors about ten thousand assets around the world. Approximately one thousand point machines in the Netherlands are maintained by SR. The data exploited in this paper was gathered in the Netherlands and provided by SR: monitoring data from current sensors installed at point machines, air temperature measurements, asset register data and expert assessment of CC (see Sect. 26.2). In Sect. 26.3 the Isolation Forest (IF) method for anomaly detection, as well as the Autoregressive Integrated Moving Average

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Fig. 26.1 Current curves measured at one switch over a wide temperature range. Features derived from curves, such as duration, systematically vary with temperature (scale in degrees Celsius)

(ARIMA) model for timeseries prediction are briefly explained. Results for both methods are found in Sect. 26.4. Section 26.5 contains conclusive remarks.

26.2

Input Data

26.2.1 Monitoring Data This is the same data as in [9]: CC sampled at 50 Hz with corresponding air temperature measured at the relay house when the blades were repositioned. The CC are from seven double-slip switches in one repositioning direction only (direction 1) acquired between January 2012 and February 2017. Training and Test Sets. The Principal Components (PC)-model (see Sect. 26.3.1) needs to be trained with normal switch behavior in order for it to differentiate abnormal behavior. The collection of CC of one switch is divided into training and test sets. To select the training set a time-window between two consecutive reported failures was identified for every switch (as in [9]). Statistical outliers from the CC contained in that window are removed. The training set consists of the remaining CC, which are assumed to represent normal switch behavior. The test set is formed by all CC (and the corresponding temperature information) outside the training set. Feature Normalization. Features extracted from CC are derived both from the entire curve (median, maximum, skewness, kurtosis, length (i.e. duration) and area under the curve) and from the ‘plateau’ section defined as the curve section between data point 20 and 60 (mean and standard deviation). Every feature is temperature normalized in order to remove its temperature dependence; a process which consists of two steps. Step 1: in every temperature bin the mean and standard deviation of

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the feature values that belong to the training set are calculated. Step 2: the bin mean value from step 1 is subtracted from all features values in it (belonging to both training and test sets) and then divided by the corresponding standard deviation from step 1. Data scarcity for extremely low and extremely high temperature bins can be problematic. Here this issue is overcome by linearly interpolating mean and standard deviation bin values for scarcely populated bins (containing less than ten data points). The features normalization has improved with respect to [9] given that (1) the temperature bins are smaller (0.5 instead of 1 K), (2) it considers linearly interpolated values for feature normalization at extreme temperatures, and (3) the normalized features undergo a second normalization that ensures the mean value and the standard deviation of the entire training set equals zero and one, respectively, which is a requirement for the PC-model (Sect. 26.3.1). Below the normalized features are just referred to as features.

26.2.2 Annotated Data Assessed Alerts. SR monitoring system generates alerts when switch- and seasonal-specific thresholds derived from a reference curve are exceeded by incoming CC. The alerts described in [9] are considered here. These were assessed and categorized as false, true or undefined by a SR maintenance expert a posteriori taking the following into account: air temperature of the curve that triggered the alert with respect to the one of the reference curve, exceedance of set thresholds, CC of alert and switch history. Note: in [9] 3531 alerts with known reference temperature were considered, including those assessed as undefined. In Sect. 26.3.1 undefined are neglected, accounting for 462 alerts in total. Labelled Current Curves. A SR maintenance expert assessed CC from two switches (switch IDs: 2606 and 3076) as normal or abnormal based on their shape and duration, as well as domain knowledge. Since this is a very time-consuming job only a limited number (1765) of CC was considered. The decision on which CC to assess took into account particular periods in the switch historical record with many failures and alerts.

1

Table 26.2 in [9] contains errors: the correct total number of assessed alerts was 353 (not 346) out of which 266 were true, 79 false and 8 undefined. Moreover for switch 3090 the number of true alerts was 31 (not 24).

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26.2.3 Maintenance and Reported Failures Register Planned maintenance actions on—and reported failures of—switches are registered in a data base. This information is crucial for finding a training set and it supports the interpretation of the modelling results. A more detailed description is found in [9].

26.3

Approach and Methods

26.3.1 Anomaly Detection The methodology for anomaly detection first applies the PC Analysis (PCA), which determines principal and residual components using the features of the training set to generate the PC-model. Both training and test sets are then represented in terms of the components generated. The information contained in the principal and residual part of a CC is summarized by two parameters: Hotelling’s (T2) and Square Prediction Error (SPE). A more sophisticated method than thresholding for finding anomalies (as done in [9]) is posed by the IF algorithm. IF is applied to all data points in the two-dimensional space defined by the T2 and SPE parameters. PCA. It uses the features that maximize variance while minimizing redundancy among them (see [9] for more details). Here the PC-model is chosen to retain at least 90% of the total variance; as a consequence the number of PC is smaller than the number of features. Once trained, the PC-model is applied to both training and test set features, obtaining T2 and SPE for every CC. Isolation Forest. This method is built on the basis of decision trees. In these trees, partitions are created by first randomly selecting one of the parameters that define the multi-parameter space, and second, randomly finding a value in the range of the parameter that splits the data points into two sets (one set corresponds to parameter values that are e.g. smaller or equal to the splitting value). Outliers are assumed to be less frequent than regular data points; furthermore outliers stand out since they lie further away from the regular data points in the parameter space. Therefore by using random partitioning, outliers can be identified closer to the root of the tree when compared to regular observations. The path for going from the root to the terminal node is proportional to the number of edges that a data point must pass in the tree. IF considers a collection of such trees (i.e. a forest) and computes the average number of edges or partitions required for isolating a particular point. An outlier is a data point, which on average requires fewer partitions until it is isolated from the rest. IF computes an anomaly score defined as sðx; nÞ ¼ 2EðhðxÞÞ=cðnÞ

ð26:1Þ

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E ðhð xÞÞ is an estimation of the expected value (i.e. the average over the collection of trees) of the number of edges in a tree for point x and cðnÞ is a normalization constant for a data set of size n. The IF Python implementation (available in scikit-learn) returns 0:5  sðx; nÞ as anomaly score [10]. The normalization value cðnÞ tends to infinity as n does, thus in case that the limit of E ðhð xÞÞ is zero, sðx; nÞ tends to one.

26.3.2 ARIMA Model for Timeseries Prediction In an autoregressive (AR) model for a univariate time series the value of the single attribute at time t is assumed to depend linearly on the values of the time series of the p immediate preceding time steps, disturbed by a random error term: Xt ¼

Xp i¼1

ai
Xti þ c þ et

ð26:2Þ

The coefficients a1 ; . . .; ap ; c are the underlying parameters and need to be estimated from the training data, i.e., data points in a prior time interval of the time series. The error terms et of all time steps are assumed to be mutually independent, centered and stationary [11]. Since the number of time steps used for training is typically larger than p, the set of linear equations Xs ¼

Xp i¼1

ai
Xsi þ c

ð26:3Þ

created by all the time steps s considered for training is usually over-determined, having no exact solution. The present model assumption corresponds to a linear regression problem using the precedent p values of the time series as regressors for the value at the present time step. The coefficients a1 ; . . .; ap ; c are estimated as least-squares estimates (i.e., chosen such that they minimize the sum of squared errors of the training data points over all possible combinations of coefficients) and used to compute the expected value of Xt . AR models are more robust when combined with a moving-average (MA) model, which presumes that subsequent values in the time series are represented as a function of the past history of deviations Xt ¼

Xq i¼1

bi
eti þ l þ et

ð26:4Þ

where l is the mean of the timeseries and the coefficients b1 ; . . .; bq need to be learnt from the data. The MA model relates the current value to the mean of the series and the previous history of deviations, i.e. it expresses the former as a function of the unexpected behavior in the past. Because the values eti are not part of the observed data, but deviations from the expected values (which are themselves computed from historical deviations) the system of equations (consisting of

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Eq. 26.4 for all time steps used for training) is inherently nonlinear. To solve the system of equations iterative non-linear fitting procedures are used. The AR and the MA models combined, with p autoregressive terms and q moving-average terms, create the ARMA model. Furthermore the ARIMA model de-trends the time series by differencing them before developing an ARMA model [12]. The degree of differencing is given by parameter d.

26.4

Results

26.4.1 Detected Anomalies IF anomaly score for all CC from switches 2606 and 3076 are plotted as a function of time in Fig. 26.2. An extreme outlier has an anomaly score very close to −0.5, whereas a data point, which is extremely difficult to isolate from all the others has an anomaly score very close to 0.5. To validate the results of the approach, both the assessed alerts and the labelled CC are considered and compared: how often do the expert’s assessment and the IF-detected anomalies agree? For switch 3076 most data points evaluated as abnormal and normal by the specialist have a negative and positive anomaly score, respectively. Therefore there is a good match between model and expert assessment. In contrast, for switch 2606 we observe that specially in year 2013 many CC that were assessed as abnormal by the specialist have a positive anomaly score. Moreover, at the beginning of 2017, several curves assessed as normal got a negative anomaly score. The latter represent a mismatch between model and expert assessment. Match and mismatch between model and assessment based on 1765 annotated CC for switches 3076 and 2606 is separately quantified in Table 26.1. In this matrix the positive condition (represented by P) is anomalous CC and the negative condition (represented by N) is normal CC according to the expert assessment. The predicted class refers to the output of the IF algorithm. For switch 2606 (3076) it is found that 46 (413) out of P = 108 (440) assessed anomalous CC were detected as abnormal by the IF algorithm (i.e. true positives or TP); the remaining 62 (27) were classified as normal by the IF algorithm (i.e. false negatives or FN). 172 (8) out of N = 1086 (131) CC annotated as normal were miss-classified by the IF algorithm (i.e. false positives or FP), and the remaining 914 (123) CC were classified as normal by the algorithm (i.e. true negatives or TN). Based on these results it is derived that the sensitivity (TP/P or true positive rate) of the model for switch 2606 (3076) is 0.43 (0.94); the precision (TP/P* or positive prediction value) is 0.22 (0.98); and 1 – specificity (FP/N or false positive rate) is 0.16 (0.06). The wide performance difference among both switches is explained by considering the training set. Features extracted from CC in the training set of switch 2606 have a wider distribution than for switch 3076, as exemplified by two features in Fig. 26.3. Switch 2606 CC plateau mean value, as well as CC length or duration,

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Fig. 26.2 Anomaly score of CC as a function of time (years) for switches 2606 (top) and 3076 (bottom). Unassessed CC in training and in test sets (open grey and black circles, respectively). Assessed CC by specialist: normal (solid blue triangles) and abnormal (solid red circles)

Table 26.1 Confusion matrix for two separate switches. Actual class is the expert’s assessment of CC and predicted class is according to the IF-algorithm Switch 2606/Switch 3076

Actual class (expert assessment)

Predicted class (IF) Abnormal Normal (+) (−) True anomalous curve (+) True normal curve (−) Total

TP = 46/ 413 FP = 172/ 8 P* = 218/ 421

FN = 62/ 27 TN = 914/ 123 N* = 976/ 150

Total P = 108/ 440 N = 1086/ 131 1194/571

are distributed over wider ranges for temperature bins between 3° and 20 °C, approximately. This indicates that switch 2606 reaction to temperature changes is harder to establish, making the detection of smaller anomalies more difficult. These findings could be indicative of switch 2606 having technical problems during the

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Fig. 26.3 Training set mean ± standard deviation (error bars) of features ‘mean value of plateau CC section’ (left figure) and ‘length of CC’ (right figure) for switch 2606 (red circles) and switch 3076 (black diamonds) in 0.5 K temperature bins

training set window; in spite of the fact that no failures were reported during this time. The model output for this switch does not perform well when compared to the specialist assessment since the model (trained with relatively high scattered features) is too insensitive. On the other hand, the results for switch 3076 are validated quite positively since this model is trained with a better defined normal behavior (trained with features that are not as scattered). Temperature influences the blades movement systematically, causing many false alerts due to the limited temperature validity range of reference curves used for defining thresholds. Table 26.2 shows the confusion matrix for 467 alerts triggered between 2012 and 2017 for the seven switches under consideration. In this matrix the positive condition is alert assessed by an expert to be true, and the negative condition is alert assessed to be false. It is found that 379 out of 383 true alerts were identified as abnormal by the IF-algorithm i.e. they are TP; the remaining 4 were identified as normal by the algorithm (FN). Additionally 16 out of 79 false alerts had a negative anomaly score (FP) and the remaining 63 had a positive score (TN). Based on these results it is found that the overall sensitivity of the seven models for differentiating between true and false alerts is 0.99; the precision is 0.96 and 1-specificity is 0.20. The results obtained in [9] for 345 false/true alerts (where a somewhat arbitrary T2 threshold value was used to differentiate between normal and abnormal) lead to a sensitivity of 0.98, precision of 0.95 and 1-specificity equal to 0.19. Thus both methods (IF and thresholding) predict these classes similarly well. Table 26.2 Confusion matrix of alerts from seven switches. Actual class is the expert’s assessment of CC that triggered an alert and the predicted class is according to the IF-algorithm

Actual class (expert assessment)

True alert (+) False alert (−) Total

Predicted class (IF) Abnormal Normal (+) (−)

Total

TP = 379 FP = 16

FN = 4 TN = 63

P = 383 N = 79

P* = 395

N* = 67

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Fig. 26.4 ARIMA model prediction (orange solid circles) of log(T2) for incipient failure (switch 3076). Black open circles: log(T2) timeseries. Grey open squares: anomaly score

26.4.2 Predicted Timeseries Several subsequent anomalies for a switch are indicative of an incipient failure. For such cases an ARIMA model can be used to model the evolution of the failure and make a prediction. To exemplify the potential of this method, a log(T2) timeseries selection is considered. Figure 26.4 shows ten consecutive anomalies (CC with negative anomaly score) starting at measurement index 4852. An ARIMA model with parameters p ¼ 3; d ¼ 1; q ¼ 1 is trained with nineteen log(T2) measurements (with indexes 4843 to 4862). The ARIMA model predicts the next eleven log(T2) values, which in this case is equivalent to nearly one day. The errors of the prediction are up to 10% larger or smaller than the log(T2) derived from measured CC.

26.5

Discussion

The switch anomaly detection approach presented here applies IF to the output parameters of the PC-model derived for CC features of seven switches separately. The IF method allows monitoring switch health status via a single parameter (anomaly score). Since normal behavior is not static (as explained above), the anomaly detection approach is required to be adaptive in order to incorporate modifications in the switch normal behavior. For this, the approach needs to incorporate the influence of all external factors that modify the switch behavior. Future work will focus on including the effect of load and usage, as well as rain and humidity, into the switch behavior modelling. Furthermore, investigating the performance of this method when applied to smaller time windows, as well as to the PC multi-variate space, remains to be done. The validation of this approach is based on two sets of annotated data (assessed alerts and labelled current curves). These contain evaluated (normal or abnormal) CC by experienced maintenance personnel. The evaluation relies (not only) on

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visual assessment of CC, which is not a highly precise method given that it is influenced by subjectivity and might not perceive small anomalies. Nevertheless it is an effective method for separating obvious anomalies from all other measurements and is therefore very relevant for model validation. Based on the validation results of the anomaly detection approach it is concluded that the approach performs well when applied to a switch that is characterized by narrow feature distributions within temperature bins. In such case the model is more sensitive to deviations from the learnt normal behavior. Wider feature distributions cause the model to be less sensitive, which is reflected in poorer performance. Moreover the validation results based on assessed alerts suggest that the approach presented here has high sensitivity, precision and specificity, similar as for the thresholding method used in [9]. The similarity between the results of both approaches is due to the fact that true alerts are severe outliers and can therefore easily be detected, even by the simplest approach. First results for an ARIMA model derived from one of the PC-model output parameters and used for prediction of failure progression are presented. This is ongoing research, which so far shows to have potential for switch failure prediction. Note that an ARIMA model can be developed for any timeseries, thus future work will explore modelling the PC, based on which T2 and SPE can be derived, and in turn an anomaly score be obtained. Further research shall focus on the conditions that trigger the automatic development of an ARIMA model, on the optimal selection of the model’s parameters, and on assessing the quality of the prediction. Acknowledgements Research conducted within the Shift2Rail project In2Smart (EU Horizon 2020 research and innovation program, grant agreement 730569).

References 1. García Márquez, F.P., Lewis, R.W., Tobias, A.M., Roberts, C.: Life cycle costs for railway condition monitoring. Transp. Res. Part E: Logist. Transp. Rev. 44, 1175–1187 (2008) 2. INNOTRACK: Deliverable 1.4.8—Overall Cost Reduction (2009) 3. INNOTRACK: Deliverable 3.3.2—Available sensors for railway environments for condition monitoring (2009) 4. Atamuradov, V., Medjaher, K., Camci, F., Zerhouni, N., Dersin, P., Lamoureux, B.: Feature selection and fault-severity classification-based machine health assessment methodology for point machine sliding-chair degradation. Qual. Reliab. Eng. Int. 99(3), 1–19 (2019) 5. Böhm, T.: Remaining useful life prediction for railway switch engines using classification techniques. Int. J. PHM 8, 1–15 (2017) 6. Camci, F., Eker, O.F., Baskan, S., Konur, S.: Comparison of sensors and methodologies for effective prognostics on railway turnout systems. Proc. IMechE Part F: J. Rail Rapid Transit. 230(1), 24–42 (2016) 7. García Márquez, F.P., Schmid, F., Conde Callado, J.: A reliability centered approach to remote condition monitoring. A railway points case study. Reliabil. Eng. Syst. Saf. 80(1), 33– 40 (2003)

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8. Narezo Guzman, D., Hadzic, E., Schuil, R., Baars, E., Groos, J.C.: Turning data driven condition now—and forecasting for railway switches into maintenance actions. In: Transport Research Arena,. Vienna (2018) 9. Narezo Guzman, D., Hadzic, E., Schuil, R., Baars, E., Groos, J.C.: Data-driven condition now —and forecasting of railway switches for improvement in the quality of railway transportation. In: Kulkarni, C.S., Tinga, T. (eds.) Proceedings of European Conference on PHM Society, Utrecht, vol. 4, pp. 1–10 (2018) 10. Sharova, E.: Unsupervised anomaly detection with isolation forest. In: PyData Conference, London (2018) 11. Akaike, H.: Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 21(1), 243– 247 (1969) 12. Aggarwal, C.: Outlier Analysis. Springer, Switzerland (2017)

Chapter 27

Service Engineering: Faster Spare Parts Procurement Supported by Digital Technologies Theresa Breckle, Sebastian Allegretti, Sven Seidenstricker, and Bastian Joos Abstract In the manufacturing industry, spare parts are held in stock to reduce the consequences of equipment downtime, playing an important role in achieving the desired equipment availability at a minimum economic cost. However, expensive and high-quality spare parts are often not kept in stock. In the event of a failure, it is therefore a question of entrepreneurial relevance whether it is economically better to replace a complex and expensive spare part or to invest in a new asset. For an economic comparison, however, information such as a replacement price, and procurement time as well as the replacement duration of the spare part is required. In the case of spare parts for older machines and assets, information such as the purchase price, the replacement time or even a possible manufacturer is sometimes missing. By using digital tools, this missing information can be obtained through instant pricing or known as instant quoting based on the CAD data model of the required part. In this contribution, we analyse the spare parts procurement process and present the results of the market study carried out by us on instant pricing. This study includes provided manufacturing technologies (e.g. additive manufacturing or plastic injection moulding) as well as providers with their services and limitations. We determine the potentials and limits for use of this digital tool in spare parts procurement. Using an example, we show how this digital technology changes the spare parts procurement process and we present where the opportunities for this digital technology lie. And show on the basis the application example, how the information procurement for the decision making between the procurement of spare part or new machine or asset can be supported thereby, in order to be able to make faster a founded decision.

T. Breckle (&)
S. Allegretti Ulm University of Applied Sciences, Ulm, Germany e-mail: [email protected] S. Seidenstricker Baden-Wuerttemberg Cooperative State University Mosbach, Mosbach, Germany B. Joos Allegretti & Partner Consulting, Ulm, Germany © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_27

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Service Engineering is discussed in the academic environment for a few decades. Beside the service sector the research field service engineering and management was a minor topic especially in the mechanical engineering. Aspects as machine maintenance or after sales services at the end of product life cycle were considered. In recent years pushing through new digital technology possibilities service engineering is getting more into the focus. The potential of systematic development of services is recognized from highly innovative companies the mechanical engineering industries. There establish service engineering as an integral approach like they are used to design products. The development of services is sustainably anchored in their organizations. Service Engineering can help to identify completely new services and emerge new business models. These business model innovations are based on new digital technologies and often have the potential for changing traditional industries. In particular, if the relative advantage of the value proposition can be significantly increased disruptive change will be possible. However, service innovations as a result of modifying processes and applying digital technologies can lead to fundamental changes in the value creation processes and thus increase efficiency.

27.2

Spare Part Procurement Process

Over the past decades, spare parts management has attracted great interest in research and practice. This topic area covers a wide range of research such as maintenance strategies, spare part logistic, supply chain management and also questions in the area of economic efficiency, such as life cycle costs or Total Cost of Ownership of an asset. Spare part management and its processes are a crucial factor of many capital-intensive companies. The successful mastery of this business process has a direct impact on the availability of high-quality and expensive capital assets that are essential to operational processes [1]. In fact, the non-availability of a spare part item can lead to long unproductive downtimes if required, which affect the operating company’s results [2]. Finally, it has a direct effect on the economic success of the company [3]. Due to the function of spare parts to support maintenance activities, the policies, processes and procedures for spare parts procurement and stocking differ from those for other types of inventory such as raw materials, work in process and finished goods [4–7]. One of the most important critical points in the management of spare parts is the uncertain question of when a part is needed. This results from the unpredictability of the occurrence of failures. Beyond that the procurement of spare parts is often limited to one or few suppliers, which leads to restrictions of the procurement lead time and the costs; or in the reverse case of procurement from several suppliers, quality variations of the delivered materials can

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occur [4]. The availability of spare parts is a crucial problem as well, as the procurement of spare parts can take a long time in unexpected cases and rare components have long delivery times [8]. This contribution refers to the life cycle of an asset located at the transition between Middle Of Life (MOL) and End Of Life (EOL). In the context of a Total Cost of Ownership analysis (cp. [9]), different spare parts strategies should be used for higher-priced spare parts in these phases of the life cycle than for standard spare parts such as simple sealing rings or hoses. The essential strategies in the procurement area can first be subdivided into demand-oriented and consume-oriented strategies according to the type of procurement triggering [5]. This contribution focuses on the procurement of spare parts used in older machines. We assume that the spare parts are not on stock at the supplier or at the manufacturer himself, but must first be manufactured. Therefore the demand-oriented procurement strategy is used in the case of an actual damage. According to Pawellek [5], there is no conclusive and generally accepted definition of the term maintenance strategy. As defined in VDI-RL 2895 [10], strategic planning is a component of maintenance planning from which the procedure is derived. The damage-related strategy is used in this article. In the damage-based strategy, repair measures are only carried out after a breakdown or malfunction of a machine or asset has occurred. No preventive measures are taken. Also the procurement of spare parts becomes active after a failure or malfunction has occurred. In the case of components with a high failure rate or long delivery times, however, spare parts are kept in stock in order to reduce the downtime of the system. In the event of damage, this leads to a complete shutdown of the system. This presupposes that the possible consequences of a failure have already been taken into account in advance. In the event of damage, it is then necessary to evaluate what this spare part costs, where it can be procured and how long the period will be until production can be restarted. On the other hand, there is the question of a replacement invest. Particularly in such a case, the procurement of the spare part may be complicated because no digital data (CAD model of the spare part) are available for the spare parts of the old systems. Therefore, for an initial evaluation of the procurement costs and procurement duration, the manufacturers must be contacted. A first evaluation of the procurement costs and procurement duration therefore requires contacting the manufacturer and the duration of information retrieval process may take a long time and the information may depend on a single supplier. However, this is very time-critical and therefore a different procedure is required. These questions are already being addressed in the field of additive manufacturing (AM), as the central challenge in spare parts management is to ensure high spare parts availability at low costs. AM could be helpful in this context, as the production of spare parts by AM enables a fast and cost-effective procurement of

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these parts [8, 11]. The study by Chekurov [8] examines the distribution and use of DSP and AM in the industrial environment and shows that the demand and interest is very high. But the application is still at an early stage. The question therefore arises as to which digital tools are available to accelerate and support the quotation cost evaluation process for a spare part and to use these digital tools for decision making for the procurement of spare parts.

27.3

Market Analysis

27.3.1 Approach of the Market Analysis Our market analysis was based on the following question: “Which digital solutions concerning instant pricing (a process that allows a price to be offered directly as a result of a digital interaction with a provider) and forecasting of lead times are currently available on the market and which restrictions do customers have to face.” Due to the poor predictability of spare parts demands, fast delivery times and availability are of high importance. Customers need to know, which suppliers can provide the demanded parts and which terms and conditions apply. The aim of the market study was to work out and analyse the current range and services of the providers of platforms and manufacturers concerning instant pricing and forecasting of lead times. This should take into account what is currently available on technological and internet-based solutions on the market. This study is based on an extensive internet research and expert survey. At the beginning of the study, expert discussions were held with representatives from industry and research. From the experiences and information from the experts, specific search terms and potential providers were identified. The search engines, scientific platforms and journals were used as sources of information. By narrowing down the search terms, the selection of researched solutions was included in iterative loops and clustered into the areas of platform and manufacturer. The search results were not regionally limited to receive a global picture. The determined solutions and services of the providers were evaluated in the following categories: • • • • • • •

Digital bidding process Technology casting Technology 3D printing Technology CNC processing Technology sheet metal processing Other technologies Country

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27.3.2 Results of the Market Analysis A total of 50 providers from 10 countries were examined. The analysed on-demand manufacturing platforms offer the customer the opportunity to submit a CAD model online. The platform evaluates which providers can meet the needs of the customer. The customer receives a list of potential suppliers. Often the assignment to the most suitable provider proceeds unnoticed in the background. In this way, the customer benefits from a clear presentation of the supplier market. As a result of the use of algorithms, a digital feasibility study is carried out and a comparison made with possible manufacturing capacities. Thus, the possible lead time of the required spare parts is determined. In most cases the customer receives direct feedback on the expected price as well as possible delivery dates. In addition, the platforms often offer additional services. Noteworthy are special tools for feasibility analysis and design support. Such platforms are currently on the rise. So far, the platforms are limited to easily calculable manufacturing technologies. These include 3D printing, injection moulding, CNC machining and various types of metalworking. The lengthy search for potential suppliers and costly supply requirements as well as performance comparisons are significantly reduced (Table 27.1). Similar to the platforms described above, some manufacturers have discovered the interface less, digital form of offering. Often innovative approaches are available, but not fully implemented. For example, after the files have been uploaded, an immediate release of a binding offer is dispensed with. Also, delivery dates are often given only as blanket or for orientation. The assurance of price and delivery date takes place only after examination by an expert. However, some manufacturers try to give the customer an approximate price. For example, some foundries have an online price calculator, which on the basis of part weight and rough assumptions, gives a price for the first orientation. However, this is not a binding offer price (Table 27.2). The market study shows that suppliers in the area of 3D printing are pioneers in the field of interface-free customer communication. But even suppliers of other production technologies are already adapting the successful approaches. Digital technologies and artificial intelligence enlighten the non-transparent provider market. Nevertheless, the use of artificial Intel intelligence or the use of algorithms is not yet used in complex technologies, such as iron casting. Although some manufacturers are already pursuing initial approaches to digital collaboration with customers (Table 27.3).

324 Table 27.1 Overview platforms

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Table 27.3 Digital technologies

27.4

Application Example

This chapter describes how to use a digital instant quoting tool for a spare part by showing the process of the offer of a spare part exemplarily. The three-dimensional product geometry is shown in Fig. 27.1. Here, the cover of a gearbox housing was selected here as an example of application. Such covers are usually complex and expensive components.

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Fig. 27.1 Application example

Fig. 27.2 Quote for the gearbox housing

The crucial factor for instant quoting is that the product geometry is available as a three-dimensional model. This application example uses a web-based tool. As already described above, these tools can process different data formats such as .step, .3dxml, .catpart, .prt, or .sat. In this case, the geometry model of the gearbox cover was uploaded as a stp-file to the provider’s site. The file size is limited. This file is dragged and dropped onto the corresponding field on the provider’s website. In the second step, a selection has to be made about the manufacturing process. The user can choose from several 3D printing processes. CNC machining, injection moulding, sheet metal and urethane casting are also available as manufacturing processes. In the last step, the material has to be selected from a drop-down list. This provider also offers an additional page where the materials in the drop-down list are explained and described. Depending on the process selection, the selection of permissible materials is restricted. All entries are checked by the software and after entering contact data the quotation process starts. This application example was originally designed for a casting process and the geometry was adapted for this process. In order to test the limits of this digital tool, it was therefore first examined whether an offer for a CNC machining process would be made. The evaluation time was longer than five minutes. The result was that a manual quotation had to be created and in this case the supplier had to be contacted directly. This result was to be expected due to the product geometry. Selecting a 3D printing process for metals (DMLS) and the selection of an associated material (Stainless Steel 316L) resulted in a quote, see Fig. 27.2, and an estimated delivery date and a lead time in less than two minutes.

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Discussion

The application example shows that crucial improvements in the spare part procurement process result for the supplier and the user due to the new technological possibilities. The lead time receiving an offer and transaction costs can be reduced. Despite the advantages, there are some limitations. Firstly, the spare part procurement process isn’t applicable to all existing manufacturing processes. Secondly, the lack of CAD data for the needed spare part is a serious problem in practice. The CAD models have to be created first. This causes costs and can be time consuming. Legal questions and security-relevant topics should also be considered. The topic ownership of the data might be one concern due to different legal frameworks. So it has to be clarified, if the provider of the platform is allowed to reuse the CAD data and which cases the data have to be deleted in accordance with certain data security-related requirements. Various legal issues and challenges may arise according to intellectual property right as well. In this context, it is generally necessary to consider the way in which transparency about price policy and production capacity should be created. The issue of liability for safety-related components is also a question that seems to be open. This shows that certain industries cannot be served with this type of quotation, in particular when guidelines place high demands on the development process (such as in aerospace or health and medical industry). Finally, it is important to discuss which degree of detail and maturity the offer price can be calculated. This means, in which way is the profitability analysis correct and if manual rework can be avoided. If the process is combined with quality gates, then the process reduces significantly the lead time and effort in request and bid process.

27.6

Conclusion and Outlook

This contribution introduced in a topic of Service Engineering focussing on spare part procurement process which is changing enabled by new digital technologies. The efficiency can be increased on the side of supplier as well as on the customer side. The market analysis identified 50 providers from different countries. The offered services and solutions, the manufacturers and the applied digital technologies were presented in three overviews. An application example demonstrates the process and options which is provided within the spare part procurement. However, it was recognized the process and available solutions offer opportunities for improvement. Using an interface-free connection between customer and supplier including immediate feedback based on digital technologies might be one them. Therefore, the lead time of the parts procurement process can be significantly reduced. Furthermore, digital data models can be provided simultaneously with the

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sale of the physical product. This could be a unique selling proposition for suppliers or other sources of revenue. Creating digital data models as a value proposition in the business model could also be an option to trigger service innovation.

References 1. Driessen, M., Arts, J., van Houtum, G.-J., Rustenburg, J.W., Huisman, B.: Maintenance spare parts planning and control: a framework for control and agenda for future research. Prod. Plann. Control 18, 1–20 (2014). https://doi.org/10.1080/09537287.2014.907586 2. Godoy, D.R., Pascual, R., Knights, P.: Critical spare parts ordering decisions using conditional reliability and stochastic lead time. Reliab. Eng. Syst. Safety 119, 199–206 (2013). https://doi.org/10.1016/j.ress.2013.05.026 3. Sarker, R., Haque, A.: Optimization of maintenance and spare provisioning policy using simulation. Appl. Math. Model. 24, 751–760 (2000). https://doi.org/10.1016/S0307-904X(00) 00011-1 4. Kennedy, W.J., Patterson, J.W., Fredendall, L.D.: An overview of recent literature on spare parts inventories. Int. J. Prod. Econ. 76, 201–215 (2002). https://doi.org/10.1016/s0925-5273 (01)00174-8 5. Pawellek, G.: Integrierte Instandhaltung und Ersatzteillogistik: Vorgehensweisen, Methoden, Tools, 2nd edn. Springer, Berlin (2016) 6. Roda, I., Macchi, M., Fumagalli, L., Viveros, P.: A review of multi-criteria classification of spare parts. J. Manuf. Technol. Manage. 25, 528–549 (2014). https://doi.org/10.1108/JMTM04-2013-0038 7. Molenaers, A., Baets, H., Pintelon, L., Waeyenbergh, G.: Criticality classification of spare parts: a case study. Int. J. Prod. Econ. 140, 570–578 (2012). https://doi.org/10.1016/j.ijpe. 2011.08.013 8. Chekurov, S., Metsä-Kortelainen, S., Salmi, M., Roda, I., Jussila, A.: The perceived value of additively manufactured digital spare parts in industry: an empirical investigation. Int. J. Prod. Econ. 205, 87–97 (2018). https://doi.org/10.1016/j.ijpe.2018.09.008 9. Durán, O., Macchi, M., Roda, I.: On the relationship of spare parts inventory policies with total cost of ownership of industrial assets. IFAC PapersOnLine 49, 19–24 (2016). https://doi. org/10.1016/j.ifacol.2016.11.004 10. Organisation der Instandhaltung: Instandhalten als Unternehmensaufgabe = Organisation of maintenance: Maintenance as a task of management, 2012th edn. Beuth, Berlin (2012) 11. Liu, P., Huang, S.H., Mokasdar, A., Zhou, H., Hou, L.: The impact of additive manufacturing in the aircraft spare parts supply chain: supply chain operation reference (scor) model based analysis. Prod. Plann. Control 25, 1169–1181 (2014). https://doi.org/10.1080/09537287.2013. 808835

Chapter 28

Temperature Monitoring Data Transmission Through Metallic Barrier Based on Ultrasonic Technology Dingxin Yang , Dong Tian, and Haifeng Hu

Abstract A novel temperature monitoring prototype through metal barrier based on ultrasonic waves is introduced in this paper. By using this promising technique, temperature sensor data can be transmitted through a metal barrier without physical penetrations. The structure of the temperature monitoring prototype, the reliable data trans-mission modulation schemes and the coding and decoding for temperature signal transmission through-metal-wall are presented. Finally temperature monitoring experiments are carried out and the results show that temperature sensor data can be transmitted through the metal barrier at a speed of 10 Kbps. The method presented is proved to have a potential engineering application in enclosed metal structure monitoring without penetration.

28.1

Introduction

There is a great requirement for sensing the condition of a sealed metal structure in modern aeronautics and aerospace industry To obtain the internal state information of a closed metal structure, a through-wall data transmission system which permits complete bidirectional transmission of data is needed [1–3]. Unfortunately, traditional data transmission technology has strong disadvantages of limiting or potentially compromising. Firstly of all, traditional electromagnetic wave cannot propagate through a metal wall effectively because of the effect of electromagnetic shielding [4]. Secondly, drilling holes in the metal wall which allow wires to pass through is undesirable because this will damage the structural integrity and environmental isolations, for instance a sealed vessel which containing toxic gases. An alternative technique for wireless transmitting data through metal barriers which can be used for condition monitoring is ultrasonic system. Because of its perfect performance in transmission capacity and power transmission efficiency, ultrasonic D. Yang (&)
D. Tian
H. Hu Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha 410073, Hunan, People’s Republic of China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_28

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data and energy transmission systems through metal barriers have been the focus of many researchers [5–9]. Temperature is the most common parameter need to be monitored in engineering. In some occasions we want to monitor the exact temperature inside a seal metallic structure, while the temperatures inside and outside may not be equal due to the heat insulation layer. So we investigated the temperature sensor data transmission through a metallic barrier based on ultrasonic transducers. The scheme presented in the paper can also be used to transmit data of other various sensors. Specifically in this paper, a temperature monitoring prototype through-metal-wall based on piezoelectric ultrasonic transducers with practical engineering application is implemented. The system structure of the temperature monitoring system, the bi-directional data transmission modulation schemes, the coding and decoding for temperature sensor data are presented in the paper. Temperature monitoring experiment results have shown that the system is capable of transmitting the temperature sensor data through a 3 cm thick metal wall at a speed of no less than 10 Kbps using acoustic impedance modulation method, which is of practical engineering value for sealed metal structure monitoring.

28.2

System Structure

The diagram of the ultrasonic through-wall data transmission system is illustrated in Fig. 28.1. The system consists of ultrasonic acoustic-electric channel, outside circuits and inside circuits. The acoustic-electric channel is established by attaching a set of two piezoelectric transducers (PZT) to a metal wall as a structure of sandwich through epoxy coupling layer. The piezoelectric transducers with a diameter of 16.5 mm are made of APC850 material from American Piezoelectric Corp and the thickness of these transducers are selected to give them a nominal thickness-mode resonance at 2 MHz.

Outside PZT

Inside PZT

Outside circuits Regulator

DSP

Inside circuits

Envelope Amplifier detection Amplitude modulation

Power amplifier

Ultrasonic carrier generation LED display

Amplifier

Regulator

Envelope detection

Impedance modulation Coupling Coupling layer layer Metal barrier

Temperature sensor DSP LED display

Fig. 28.1 Diagram of the temperature sensor data transmission system through a metal wall

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The core of the outside circuits is TMS320F28335 DSP from Texas Instruments. DSP controls ultrasonic carrier generation module AD9850 to generate ultrasonic frequency signals. Power amplifier is a high frequency amplifier AR150A100B which can output up to 150 W continuous waves from 10 kHz to 100 MHz. The ultrasonic frequency signals are amplified and applied to terminals of the outside piezoelectric transducer. The ultrasonic waves generated by the outside piezoelectric transducer will propagate through coupling layer and metal wall. A part of the ultrasonic waves will be received by the inside piezoelectric transducer and converted into electric signal. The outside to inside data transmission uses amplitude-shift keying(ASK) modulation through a amplitude modulation module, which is also controlled by DSP. At the same time a fraction of the acoustic vibration power will be reflected back towards the outside and received by the outside piezoelectric signal. The amount of the reflected signal is determined by the acoustic impedance of constituent parts of the acoustic-electric channel including the electric impedance of the inside circuits. Thus changing the electrical load on the terminals of inside piezoelectric transducer (for instance, setting opening or shorting the terminals) will alter the electric impedance which, in turn, modulates the amplitude of the reflected signal. The changes in the reflected signal are received by the outside transducer. The variations in the reflected signal result in amplitude variations in the applied voltage. These amplitude variations are related to the data being sent from the inside and are easily detected to recover the binary data, which is called impedance modulation [10]. One type of the impedance modulation circuit is shown in Fig. 28.2. The control of impedance modulation is fulfilled by the TMS320F28335 DSP of the inside circuits. On one hand, the received ultrasonic waves are processed through envelope detection, amplifier module and voltage regulator and commands sent from outside are read by inside DSP. On the other hand, inside DSP acquires data from the temperature sensor DS18B20 and encodes the sensor data to binary bit stream. DSP switches on/off the metal-oxide semiconductor field-effect transistor (MOSFET) according to the binary bit stream. On and Off of MOSFET are corresponding to two different impedance states. The voltage amplitude across the inside piezoelectric transducer will vary according to different impedance states. Thus this variation will lead to small changes of the signals across the terminals of the outside piezoelectric transducer. The outside signals are also processed through envelope detection, amplification, and voltage regulator. Outside DSP will decode the data to temperature sensor data and display them on the LED display.

Fig. 28.2 Impedance modulation circuit

Inside PZT terminal port

DSP control port

   

MOSFET

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Amplifier & Regulator

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But it should be noted that “outside to inside” data transmission and “inside to outside” data transmission cannot occur at the same time. That is to say, the system can only realize half-duplex communication through a metal barrier.

28.3

The Encoding and Decoding Schemes for Temperature Signal Transmission Through the Metal Wall

The inside DSP reads the temperature sensor data and performs average filtering to reduce measurement error, finally the accurate temperature value is determined through look-up table. If the temperature value is 22.5 °C degree, then inside DSP should send an integer 225 to outside. For data transmission from inside to outside, proper data transmission protocol should be designed. Here a protocol based on serial port communication protocol is designed for temperature data transmission. The communication protocol includes 1 start bit, or 9 data bit and there is no stop bit or check bit. The digital sending waveforms including encoding temperature data are shown in Fig. 28.3. When temperature value is not less than 25.6 °C degree, there will be 8 valid data bits. Otherwise there will be 9 data bits to transmit. In order to differentiate start bit and data bit, two kinds of digital waves are defined. In idle state, the output remains low level. The start bit is defined as a high-low-high level transfer and every bit lasts 60 µs. While a data bit is defined as a level lasting 100 µs. High level for bit 1, and low level for bit 0. The integer representing the temperature value will be sent from low significant bit to high significant bit in sequence. There will be an interval between continuous temperature value sending. When receiving data at the outside, an inverse decoding process will be performed. The received digital waveforms and decoding of every bit of sending from inside to outside is shown in Fig. 28.4. The decoding of temperature data starts from the determination of a start bit. When a rising edge has been detected, the level is read after delaying 30 µs. Then delay 60 µs and read the level again, and read the level once more after another 60 µs. If the level sequence is high-low-high, then the start bit is determined. 60μs

100μs

… Low level

Low level Start bit

8 or 9 valid data bit

Fig. 28.3 Illustration of digital waves and encoding for temperature data sending

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Fig. 28.4 Illustration of waves and decoding for received temperature data

60μs

335 100μs

… Low level

Low level Start bit

8 or 9 valid data bit

Fig. 28.5 Temperature sensor data transmission system through a metal wall

The following data will be temperature data. The temperature data will be read one by one bit after every 100 µs delay until 8 or 9 bits have been read. In Fig. 28.4, the detecting moments for the receiving bits are marked with dots in the waveforms. The read temperature data will be stored in an array and sent to LED display via SPI interface.

28.4

Temperature Monitoring Experiment

Based on the foregoing design diagram of the system, the implementation of the physical system is shown in Fig. 28.5. The thickness of the metal wall is 3 cm. The procedure for temperature data transmission through metal wall is as follows. First, after power on, the outside DSP will control the ultrasonic carrier generation AD9850 to generate 2 MHz sinusoidal signal. Then push the “send” key on the outside DSP platform. This command will be sent to the inside DSP by amplitude modulation. When the inside DSP has received the command, it will read the temperature sensor data, encode and send the data bit by bit via impedance modulation as described above. The temperature data will be sent continuously at about 300 ms intervals. When the outside DSP receives the temperature data it will decode and display the temperature value on the outside LED. At the same time, the temperature will be displayed on the inside LED controlled by inside DSP. We can check two sets of display values whether they are the same or not.

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Fig. 28.6 Waveforms measured at the inside PZT terminals

voltage , V

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Fig. 28.7 Waveforms measured at the outside PZT terminals

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In experiment, we can hear the squeak sound because the data transmission rate is at 10 Kbps, which is in the frequency range human can hear. Figure 28.6 shows the waveforms measured directly at the inside PZT terminals when temperature data is transmitting. The valley zones in the waveforms are due to the effect of impedance modulation. The waveform at the outside PZT terminals is shown in Fig. 28.7. It can been seen that there are small variations in the waveforms which contain the information sending from the inside circuits. Figure 28.8 shows the recovered waveforms after envelop detection, amplification and voltage regulation. The upper part of Fig. 28.8 is the received waveforms representing continuous sending temperature data. Because the temperature data are sent continuously at the identical time intervals, the received waveforms are periodic. The lower part of Fig. 28.8 is the an enlarged single waveform of sending temperature data. It can be seen that the bits transmitted from low significant bit to high significant bit is “110010001”. We inverts the sequence and the integer is 275, which represents the temperature value of 27.5 °C degree. Figure 28.9 shows the displayed temperature on the inside LED and outside LED at the same time. It can be seen that the values are the same.

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The experiment results have shown the temperature sensor data can be transmitted through the metal wall correctly and the data transmission rate can reach 10 Kbps. In experiments when we heat the temperature sensor, the temperature values displayed on the outside and inside LEDs will remain synchronized with changes of temperature of the sensor.

Fig. 28.8 Recovered temperature data being transmitted from inside to outside

110010001

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Fig. 28.9 Temperature display on inside and outside LED

(a) Inside LED display

(b) Outside LED display

28.5

Conclusion

This paper has presented the approach used for sensor data transmission through-metal-wall based on ultrasonic without penetration of the metal barrier. The structure of a temperature monitoring system, the data transmission modulation schemes and the coding and decoding for temperature signal transmission through a metal wall are discussed in detail. The physical system is implemented and experiments are carried out. Temperature data transmission results show that data can be transmitted at a speed of 10 Kbps through a 3 cm thick metal wall using ultrasonic waves. Although in the paper, only temperature data transmission through a metal wall is tested using the system, it can transmit other sensors data with a few modifications. So the approach presented has a potential engineering application in enclosed metal structure monitoring without drilling hole for wire feed through. Acknowledgements The authors would greatly appreciate the support provided by National Natural Science Foundation of China No. 51375485 and Natural Science Foundation of Hunan Province No. 2017JJ2300 for this work.

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References 1. Sherrit, S., Badescu, M., Bao, X., Bar-Cohen, Y., Chang, Z.: Efficient electromechanical network model for wireless acoustic-electric feed-throughs. In: Proceedings pf the SPIE 5758, Smart Structures and Materials 2005: Smart Sensor Technology and Measurement Systems, vol. 5758, pp. 362–372 (2005) 2. Kluge, M., Becker, T., Schalk, J., Otterpohl, T.: Remote acoustic powering and data transmission for sensors inside of conductive envelopes. In: IEEE Sensors, pp. 41–44 (2008) 3. Roes, M.G.L., Duarte, J.L., Hendrix, M.A.M., Lomonova, E.A.: Acoustic energy transfer: a review. IEEE Trans. Ind. Electron. 60(1), 242–248 (2013) 4. Graham, D.J., Neasham, J.A., Sharif, B.S.: Investigation of methods for data communication and power delivery through metals. IEEE Trans. Ind. Electron. 58(10), 4972–4980 (2011) 5. Sherrit, S., et al.: Studies of acoustic-electric feed-throughs for power transmission through structures. In: Proceedings of the SPIE 6171, Smart Structures and Materials 2006: Industrial and Commercial Applications of Smart Structures Technologies, San Diego, California, USA, vol. 6171, pp. 617102–8 (2006) 6. Lawry, T., Saulnier, G., Wilt, K., Ashdown, J., Scarton, H.: Adaptive system for efficient transmission of power and data through acoustic media, 19 October 2012 7. Zaid, T., Saat, S., Yusop, Y., Jamal, N.: Contactless energy transfer using acoustic approach a review. In: 2014 International Conference on Computer, Communications, and Control Technology (I4CT), pp. 376–381 (2014) 8. Yang, D.-X., Hu, Z., Zhao, H., Hu, H.-F., Sun, Y.-Z., Hou, B.-J.: Through-metal-wall power delivery and data transmission for enclosed sensors: a review. Sensors 15(12), 31581–31605 (2015) 9. Gorostiaga, M., Wapler, M.C., Wallrabe, U.: Analytic model for ultrasound energy receivers and their optimal electric loads. Smart Mater. Struct. 26(8), 85003 (2017) 10. Connor, D.J., Cummings, G.F., Star, M.J.: Acoustic transformer with non-piezoelectric core, US5594705 (A), 14 January 1997

Chapter 29

Fault Detection and Classification of Rolling Bearings Using Extreme Function Theory Xiwen Gu, Shixi Yang, and Evangelos Papatheou

Abstract The failure of rolling bearings causes performance deterioration and even unplanned downtime of rotating machinery. Intelligent classifiers based on machine learning algorithms have been well-established recently to recognize different types of rolling bearing failure. Usually, the faulty data on field is incomplete in fault types and lacks in quantity. Conventional classification algorithms may fail to detect a new class and identify one class wrongly as the other classes. In this paper, extreme function theory is introduced to detect new classes and perform fault classification under the assumption that limited class and small datasets are available for training. A Gaussian process (GP) model is created from the training data containing only one class. The probability density of any observations can be obtained from that model. According to the extreme value theory, that Probability Density Function (PDF) will have a corresponding Extreme Value (EV) distribution belonging to an extreme distribution. Choosing a proper confidence interval, a threshold can be calculated from a fitted Cumulative Distribution Function (CDF). The outliers of the EVs suggest a new class not seen in the training set, detecting thus potential faults, by also keeping a low ratio of false alarms. With the GP models trained from different classes, different thresholds indicating whether the observation belongs to the trained class are obtained. The multiclass classification can be realised in such a one-against-all architecture. The experimental data of X. Gu
S. Yang (&) The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China e-mail: [email protected] X. Gu e-mail: [email protected] X. Gu
S. Yang The Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China E. Papatheou College of Engineering, Mathematics and Physical Sciences, University of Exeter, North Park Road, Exeter EX4 4QF, UK e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_29

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rolling bearings in normal operation and three different faulty conditions are used in this paper to verify the effectiveness of extreme function theory for fault detection and classification. This approach is also compared with Artificial Neural Network in terms of false identification ratio.

29.1

Introduction

Rolling bearings are essential components of rotating machinery. Faults of rolling bearings such as fatigue wear, peeling and even failure of the bearings bring abnormal vibration and reduce the reliability and stability of the whole system. To avoid unplanned downtime and unnecessary financial loss, fault detection and classification of rolling bearings become increasingly important. Traditionally, fault diagnosis of rolling bearings is carried on through signal-based approaches. Signal-based methods are based on the prior knowledge on the faulty symptoms of the system [1]. For rolling bearings, the characteristic frequencies related with the impulsiveness caused by the defects are the key diagnostic information to be enhanced and extracted from the measured signals [2]. A review of the signal-based methods for rolling bearings can be found in Ref. [3]. With a large volume of monitoring data collected, data-driven methods based on machine learning algorithms have been well-established to conduct fault diagnosis in a more intelligent and automatic way [4]. Various data-driven methods are proposed and applied for rolling bearings like support vector machine [5], decision tree [6], artificial neural network [7], deep learning method [8] and so on. Usually, a good intelligent classifier relies on plenty of historical data for training or strong features distinguishing all the possible faults. However, in practical conditions, the monitoring data cannot cover all the fault types and are relatively insufficient in quantity to train a classification model with high accuracy. In the case of small-size samples, how to detect abnormality and distinguish different fault types are two main challenges for rolling bearings. Novelty detection, identifying a new class different from the training samples can be realised in various approaches [9]. Extreme function theory (EFT) proposed by Clifton et al. [10] is an effective method for novelty detection with a low ratio of false alarms. Combining Gaussian process regression (GPR) and extreme value theory, EFT performs novelty detection by assessing functions as a whole, instead of discrete points. It has been successfully modified and applied on wind turbines [11] and cantilever beam structures [12]. In this paper, EFT is applied on rolling bearings for novelty detection and further developed for fault classification. The time series of vibration responses are considered as individual functions. Only small samples are needed for training taking advantage of GPR. The layout of the paper is as follows: the next section demonstrates the EFT based on GPR. The third section describes application of EFT on rolling bearings including fault detection and fault classification. Finally, a discussion is shown in the fourth section.

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Extreme Function Theory

29.2.1 Gaussian Process Regression A GP is defined as a collection of random variables, any finite number of which have a joint Gaussian distribution [13]. Given a training set T of n observations, T ¼ fðxi ; yi Þ ji ¼ 1;


; ng, a GP model M is trained based on the dataset T. Assuming f ðxÞ the training output value of the input vector x, the GP can be written as f ðxÞ  GPðmðxÞ; kðx; x0 ÞÞ

ð1Þ

where mðxÞ is the mean function, set to be 0 in this paper for simplicity [11] and kðx; x0 Þ is the covariance function. The GP is specified by its mean function and covariance function. Let X  be the matrix of testing input. To perform regression using GPs would require training a model on a training dataset and then predicting the output values f  at the test locations X  . In practice, the training target values differ from the output function values by a Gaussian noise e with variance r2n , which is y ¼ f ðxÞ þ e. The prior of the noisy training output and the testing output is [13]
 y  N 0; K ðX; X Þ þ r2n I

ð2Þ

f   N ð 0; K ðX  ; X  ÞÞ

ð3Þ

and

respectively. The K ðX; X Þ and K ðX  ; X  Þ are respectively the covariance matrixes evaluated at all the training points and the testing points. The joint distribution of the training output y and the testing output f  is defined as [13] 

   y K ðX; X Þ þ r2n I  N 0;  f K ðX  ; X Þ

K ðX; X  Þ K ðX  ; X  Þ

 ð4Þ

where K ðX; X  Þ is an nn matrix of the covariance calculated by all the training and testing input. Thus,the predictive conditional distribution is derived as [13] f  jX; y; X   N ðmðf  Þ; covðf  ÞÞ

ð5Þ

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where  1 mðf  Þ ¼ K ðX  ; X Þ K ðX; X Þ þ r2n I y;

ð6Þ

 1 covðf  Þ ¼ K ðX  ; X  Þ  K ðX  ; X Þ K ðX; X Þ þ r2n I K ðX; X  Þ:

ð7Þ

Here, the squared-exponential covariance function is used, which can be expressed in one dimension as [13]   1 2 ky ðx; x0 Þ ¼ r2f exp  2 ðx  x0 Þ þ r2n d: 2l

ð8Þ

where ky is the covariance function of y and the signal variance r2f , length-scale l and r2n are all hyperparameters for GPR. These parameters can be optimized by the maximum marginal likelihood function, which in the logarithmic form is [13]

1 1 1

log pðyjX Þ ¼  yT ðKðX; X Þ þ r2n I)1  log KðX; X Þ þ r2n I  log 2p: 2 2 2

ð9Þ

29.2.2 Extreme Function Theory Based on GPR Based on the GPR model, the test function f  corresponding to input vector x can be evaluated to have a probability density z ¼ fn ðf  Þ, where fn ¼ pðf  jx; y;x Þ is the multivariate Gaussian distribution defined over f  [10]. According to the definition of multivariate Gaussian distribution,  T  1   1 1  z ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi e2ðf mðf ÞÞ covðf Þ ðf mðf ÞÞ ð2pÞd jcovðf  Þj

ð10Þ

where d is the dimension of the test input vector x and covðf  Þ and mðf  Þ can be calculated by Eqs. (6) and (7). The EFT is concerned with the extrema distribution and here the lowest density z is defined as the ‘most extreme’, meaning the functions is most far away from the trained mean function [10]. The low values of z converge to a feasible extreme distribution. There are three common EV CDFs: Weibull, Frechet or Gumbel [14]. As it is unsure which distribution will converge to, the generalised extreme value (GEV) distribution is used here. Taking the logarithmic form of z (lnz ¼ lnðzÞ) to make its values larger, the GEV minima distribution is [12] 1 lnzl c r

Lðlnz; l; r; cÞ ¼ 1  eð1c

Þ

ð11Þ

where l, r and c are location, scale and shape parameters. Optimization of these parameters are based on the Differential Evolution (DV) as introduced in Ref. [15].

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Fig. 29.1 CWRU test rig [16]

^ ðlnzÞ is obtained. The evolving With the estimated parameters, the fitted CDF P process through generations is realized by a normalised mean squared cost function [15]. ^; r ^; ^cÞ ¼ Cðl

 100 Xn
^ ðlnzÞ 2 PðlnzÞ  P 2 i¼1 nrP

ð12Þ

where PðlnzÞ is the empirical CDF, rP is the variance of the CDF data and n is the total number of the observations fitting the CDF. A threshold S can be calculated from the fitted Cumulative Distribution Function (CDF) with a confidence interval of 99%.

29.3

Application on Rolling Bearings and Results Discussion

29.3.1 Data Description The bearing data from Case Western Reserve University (CWRU) [16] is used to verify the proposed method. Figure 29.1 shows the test rig. A benchmark analysis of the CWRU bearing data can be found in Ref. [17]. All the data used in this paper is collected at the drive end with the shaft speed of 1750 rpm. The bearing faults data used here cover three types: ball faults, inner race faults and outer race faults with fault centred in the load zone. The fault width is 0.18 mm. The time series of rolling bearings in each condition is divided into 206 samples with one sample corresponding to 50 datapoints randomly picked from half a second of data. These 206 samples of each condition are randomly divided into the

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three sets: training set with 6 samples, validation set with 100 samples and test set with the rest 100 samples.

29.3.2 Fault Detection of Rolling Bearings In this paper, fault detection is carried on under the assumption of limited normal data and lack of fault samples. 6 normal samples are used for training which is 300 datapoints in all. A GPR model M1 is trained using the normal training set and then 100 normal validation samples are put in the model to obtain the lnz values. As shown in Fig. 29.2, the generalized extreme distribution is fitted by the left tail of the probability density, which is 10% of lnz values. With the emulated parameters of the CDF, the threshold for the normal data is calculated to be −46.19. The test samples contain both the normal and faulty scenario including 100 normal samples and 100 faulty samples of rolling ball fault, inner race fault and outer race fault respectively. These test samples are also applied to the above GPR model and obtain the corresponding lnz values. The result of fault detection is shown in Fig. 29.3. To be consistent with common perception and for improved visualisation, lnz is plotted to be –lnz and also the negative value of the threshold, which is S1 = 46.19 is taken in Fig. 29.3. All the normal samples used are different from each other in this case. As can be seen in Fig. 29.3, the normal test samples are all identified as normal which are below the threshold while above the threshold are abnormal samples of three faulty types. 1

cumulative distribution function

Fig. 29.2 Fitting the GEV distribution on 10% of data

0.8

fitted CDF empirical CDF points used

0.6

0.4

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order statistics

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Fig. 29.3 Fault detection with EFT

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54 52 50 48 46 0

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29.3.3 Fault Classification of Rolling Bearings by EFT As shown in Sect. 3.2, the threshold can indicate whether the observation belongs to the trained class and the lnz values of different fault types are also separated well. Different thresholds can be obtained to classify between the classes under the one-against-all structure if training different GPR models. A new GPR model M2 is trained by the training set of ball faults, which also contains 6 samples with 300 datapoints in all. The validation set of ball faults is used to create sets of lnz values and then DE algorithm is applied to fit the CDF of GEV. The threshold S2 corresponding to the model M2 is estimated to be 46.81. Similarly, the GPR model M3 is trained by the training set of inner race faults and the corresponding threshold S3 is 52.33. The steps of fault classification are shown in the flow chart in Fig. 29.4. The test samples are all the test sets from 4 classes, which are 400 samples in all and different from the training and validation sets. First of all, the test samples are put in the model M1 of normal class to create 400 -lnz values. Assuming these values lnz1, comparison is carried on between lnz1 and S1. The samples with lnz1  S1 are classified as normal class. Next, the rest of test samples are further put in the model M2 of ball fault and samples with lnz2  S2 are classified as rolling ball faults. Also, the rest of the test samples are then tested by the model M3 of inner race fault. Samples with lnz3  S3 are classified as rolling ball fault while lnz3 > S3 refers to the outer race fault. The classification results can be seen in Fig. 29.5. As shown in Fig. 29.5, the misclassification is three samples in the outer race case, which are identified as inner race fault incorrectly and one sample with the

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Fig. 29.4 Flow chart of fault classification

Fig. 29.5 Fault classification results of 400 test samples from 4 class

60 threshold for the normal class threshold for the rolling ball fault threshold for the inner race fault normal samples rolling ball fault samples inner race fault samples outer race fault samples

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rolling ball fault. The false classification ratio is 1.00%. Back propagation neural network (BPNN) is also applied to the same training and testing set to compare the classification results. Extracting 11 features in the time domain like Ref. [18] before training the BPNN model, the false classification rate is 33.93% in average. The classification results are sensitive to the feature extraction process by BPNN, so more work is needed to select good features compared with EFT. Also, since only 6 samples are used for each class in this paper, EFT shows much better results in small sample training case than BPNN. For the BPNN model, when 100 training samples are chosen for each class, the false classification rate is much lower to be 1.69% in average.

29.4

Conclusion

This paper introduced the Extreme Function Theory (EFT) for fault detection and fault classification of rolling bearings under the assumption that small samples are available for training. There were 6 samples, 300 datapoints in all for each class to train different classification model by Gaussian Process Regression. Different thresholds were obtained showing how extreme the test samples were compared to the training samples. The bearing data from CWRU were used to verify the method. There were four types considered here: normal type, ball fault, inner race fault and outer race fault. The classification was conducted under the one-against-all strategy. A preliminary comparison was also done between EFT and BPNN. The false classification ratio obtained by EFT is much lower than BPNN if small-size samples are available for training. Acknowledgements The authors acknowledge the support from the National Natural Science Foundation of China under Grant No. U1809219.

References 1. Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques-part i: fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 62(6), 3757–3767 (2015). https://doi.org/10.1109/TIE.2015.241750 2. Randall, R.B., Antoni, J.: Rolling element bearing diagnostics-a tutorial. Mech. Syst. Signal Process. 25(2), 485–520 (2011). https://doi.org/10.1016/j.ymssp.2010.07.017 3. Rai, A., Upadhyay, S.H.: A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol. Int. 96, 289–306 (2016). https://doi.org/10. 1016/j.triboint.2015.12.037 4. Gao, Z., Cecati, C., Ding, S.: A survey of fault diagnosis and fault-tolerant techniques part ii: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Trans. Ind. Electron. 62(6), 3768–3774 (2015). https://doi.org/10.1109/TIE.2015.2419013

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5. Li, Y., Yang, Y., Wang, X., Liu, B., Liang, X.: Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine. J. Sound Vib. 428, 72–86 (2018). https://doi.org/10.1016/j.jsv.2018.04.036 6. Song, L., Wang, H., Chen, P.: Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery. IEEE Trans. Instrum. Meas. 67(8), 1887–1899 (2018). https:// doi.org/10.1109/TIM.2018.2806984 7. Ben Ali, J., Fnaiech, N., Saidi, L., Chebel-Morello, B., Fnaiech, F.: Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl. Acoust. 89, 16–27 (2015). https://doi.org/10.1016/j.apacoust.2014. 08.016 8. Shao, H., Jiang, H., Zhang, H., Duan, W., Liang, T., Wu, S.: Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech. Syst. Signal Process. 100, 743–765 (2018). https://doi.org/10.1016/j.ymssp.2017.08.002 9. Pimentel, M., Clifton, D., Clifton, L., Tarassenko, L.: A review of novelty detection. Sig. Process. 99, 215–249 (2014). https://doi.org/10.1016/j.sigpro.2013.12.026 10. Clifton, D., Clifton, L., Hugueny, S., Wong, D., Tarassenko, L.: An extreme function theory for novelty detection. IEEE J. Sel. Top. Signal Process. 7(1), 28–37 (2013). https://doi.org/10. 1109/JSTSP.2012.2234081 11. Papatheou, E., Dervilis, N., Maguire, A.E., Campos, C., Antoniadou, I., Worden, K.: Performance monitoring of a wind turbine using extreme function theory. Renew. Energy 113, 1490–1502 (2017). https://doi.org/10.1016/j.renene.2017.07.013 12. Martucci, D., Civera, M., Surace, C., Worden, K.: Novelty detection in a cantilever beam using extreme function theory. J. Phys. Conf. Ser. 1106(1) (2018). https://doi.org/10.1088/ 1742-6596/1106/1/012027 13. Rasmussen, C.E., Williams, C.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2005). https://doi.org/10.7551/mitpress/3206.001.0001 14. Fisher, R.A., Tippett, L.H.C.: Limiting forms of the frequency distribution of the largest or smallest member of a sample. In: Mathematical Proceedings of the Cambridge Philosophical Society, vol. 24 (1928). https://doi.org/10.1017/s0305004100015681 15. Worden, K., Manson, G., Sohn, H., Farrar, C.: Extreme value statistics from differential evolution for damage detection. In: Proceedings of the 23rd International Modal Analysis Conference, Orlando, Florida, USA (2005) 16. Case Western Reserve University Bearing Data Center Website. http://csegroups.case.edu/ bearingdatacenter/home 17. Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech. Syst. Signal Process. 64–65, 100–131 (2015). https://doi.org/10.1016/j.ymssp.2015.04.021 18. He, J., Yang, S.X., Gan, C.B.: Unsupervised fault diagnosis of a gear transmission chain using a deep belief network. Sensors 17(7), 1564 (2017). https://doi.org/10.3390/s17071564

Chapter 30

Modelling and Analysis of Slewing Bearings with Quenched Soft Zone Jianxin Gui , Guangbin Wang, Xiaoli Tang, Zhou Zhou, and Yongzheng Jiang

Abstract The slewing bearing has an unquenched area, which is called the bearing soft zone. In this paper, through the three-dimensional modelling software, considering the factors such as the depth of the quenching layer, a slewing bearing model with bearing soft zone is established. The load distribution of the slewing bearing is calculated by finite element simulation, and the outer ring of the slewing bearing and the bearing capacity of the cage are analysed under four extreme wind conditions. The results show that under continuous extreme loads, the load on the soft zone is very large and the most vulnerable parts of the cage appear at their joints.

30.1

Introduction

As an important part of the wind turbine system, slewing bearings are usually installed in the engine room located tens of meters high above the ground where the service environment is significantly harsh because the temperature and humidity J. Gui (&)
Y. Jiang Hunan Provincial Key Laboratory of Mechanical Equipment Health, Xiangtan, China e-mail: [email protected] Y. Jiang e-mail: [email protected] G. Wang Linnan Normal University, Zhanjiang, China e-mail: [email protected] X. Tang Center for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK e-mail: [email protected] Z. Zhou Xemc-Wind Co., Ltd, Xiangtan, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_30

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vary greatly [1, 2]. Moreover, during the operation of the slewing bearing, in addition to bearing the aerodynamic force, gravity, centrifugal force and inertial force transmitted from the blade, it also has to withstand various loads under complex conditions [3]. Commonly, the slewing bearing will have the soft zone breakage after 3–5 years of operation if it works in a complex service environment. Even various methods are used in the engineering, the soft zone failures occur frequently resulting in the life of the slewing bearing does not meet expectations. However, the research on the soft zone of wind turbine slewing bearings is still rare, and more studies concentrate on the analysis of the mechanical properties and bearing capacity of slewing bearings without considering the soft zone of bearings. For example, Wang et al. [4] analysed the trends of contact angle under axial force, established a mathematical model of slewing bearing load distribution, and discussed the relationship between the clearance and bearing capacity; Li et al. [5, 6] analysed the relationship between the key parameters and capacity of the bearing, and gave suggestions for the selection of parameters for large wind turbine slewing bearings. Wu et al. [7] used ANSYS workbench to analyse the slewing bearing and discussed the influence of bolt preload on the load distribution. Wang et al. [8] gave the accurate calculation method of the bearing curve of the large double-row four-point contact ball bearing and drew the static carrying surface. Liu et al. [9] calculated the bearing capacity of a single-row four-point contact ball slewing bearing by the finite element method. Amasorrain et al. [10, 11] developed a program suitable for calculating the load distribution of single-row large-sized slewing bearings, considering the importance of slewing bearing load distribution for bearing selection and calibration, and confirmed the rigidity. The support structure is based on the cosine contact load distribution; Aguirrebeitia et al. [12] established a mathematical model for calculating the bearing capacity of a four-point contact slewing bearing under the combined action of axial, radial and overturning moments. Smolnicki et al. [13, 14] studied the whole system including the slewing bearing support structure, and studied its load distribution by finite element; Kania et al. [15, 16] used truss structure to simulate the steel ball in the slewing bearing With the action of the raceway, a complete finite element model of the slewing bearing is established, and the bearing capacity of the single-row, double-row and three-row slewing bearings under the rigid ferrule and the flexible ferrule were discussed respectively. In these studies, not only the influence of the soft zone was automatically ignored in the mechanical modelling of bearings, but also the structural strength and load characteristics of the soft zone were not investigated as the influencing factors during the mechanical analysis. Taking into account the importance and vulnerability of the slewing bearing soft zone, this paper establishes a model of a slewing bearing with the soft zone based on its mechanical properties and capacity analysed by other researchers.

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Fig. 30.1 Physical diagram of the slewing bearing

S

(a) Global view of the slewing bearing

(b) Part of the slewing bearing

Fig. 30.2 Three-dimensional model of the slewing bearing

30.2

Establishment of the Model with Bearing Soft Zone

Generally, the material of slewing bearings is 42CrMo with the raceway surface quenched and heat treated. However, since the trajectory of the heating head on the raceway cannot overlap (because the overlap will cause cracks due to secondary quenching), a soft band is formed. When the product leaves the factory, the position of the soft zone on the work piece will be marked with the word “S” to indicate the specific soft zone position as shown in Fig. 30.1. The hardness of the slewing bearing soft zone is generally around 35 HRC, which is lower than the hardness range of 55–62 HRC in the quenching zone, thus becoming the weakest zone in the slewing bearing. In this paper, the three-dimensional modelling software (Solid Works) is used to model and assemble the outer ring, inner ring, rolling elements and cage of the slewing bearing. The model is shown in Fig. 30.2. The specific parameters of the

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Table 30.1 A slewing bearing design parameter Parameter

Value

Diameter of outer race mounting hole distribution circle D1 /mm Nominal bore diameter d/mm Diameter of rolling element’s circle Dm /mm Diameter of inner race mounting hole distribution circle d1 /mm Nominal outer diameter D/mm Rolling element diameter Dw /mm Inner channel radius of curvature ri /mm Outer channel radius of curvature re /mm Ditch row spacing Dc /mm Number of mounting holes N Number of rolling elements Z Initial contact angle a/(°)

2000 1716 1900 1800 2080 45 23.625 23.625 80 54 2*120 45

slewing bearing are listed in Table 30.1. The quenching depth of the slewing bearing is 5 mm [17], and the unquenched part accounts for 1/72 of the entire outer ring, which is the bearing soft zone.

30.3

Finite Element Analysis

30.3.1 Definition of Materials Because the material properties change before and after quenching, the relevant parameters of the materials are shown as follows: in the quenching area, elastic modulus is 2.12e011 Pa, Poisson’s ratio is 0.3, tensile strength is 2180 MPa, in the unquenched area (bearing soft zone), elastic modulus is 2.00e011 Pa, the pine ratio is 0.28 and the tensile strength is 1125 MPa. The material of each part is defined by the above data in the material library in the Workbench.

30.3.2 Meshing Grid processing plays an important role in finite element analysis, the problem with cell type selection is the choice between tetrahedral and hexahedral elements. The hexahedral element has high calculation accuracy also with high requirements on the geometric topology. The inner and outer rings of the slewing bearing belong to the revolving body, which meets the requirements of the hexahedral mesh. While, the tetrahedral element has good adaptability and has no special requirements for the

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(a) Global Grid

355

(b) Local grid

Fig. 30.3 Meshing

geometric structure. There are many contact pairs in the slewing bearing, so it is necessary to perform mesh encryption on the inner and outer ring raceways and the spherical rolling elements. If the inner and outer rings are meshed by a hexahedron, the mesh distortion is likely to occur when the mesh of the raceway is encrypted. In summary, the automatic division method can be selected. If the geometry can’t be swept, the program automatically generates a tetrahedron, and vice versa produces a hexahedron [18]. The meshing results in a total of 707,419 cells and 238,606 nodes, as shown in Fig. 30.3.

30.3.3 Settings of Contact Pairs In the contact analysis, there are two difficulties: Firstly, the boundary conditions of the solution are uncertain because the actual contact area is unknown, or whether the two surfaces are in contact with each other or not is unclear. Secondly, most practical contact analysis problems require consideration of nonlinear friction which increases the computational difficulty of the convergence of the solution results. In this case, a rigid-flexible contact type with a contact element is employed to simulate. In this example, a total of 480 contact pairs need to be defined, as shown in Fig. 30.4. It consists of the surface of the steel ball and the left and right raceways of the inner and outer rings. In these contact pairs, the surface of the steel ball serves as the contact surface, and the four raceway surfaces serve as the target surface.

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Fig. 30.4 Settings for the contact pair

Table 30.2 External load under four extreme conditions

Design load condition

Fa (KN)

Fr (KN)

M (KN
m)

Extreme Extreme model Extreme Extreme

running gust turbulence fatigue

3599 1764

725 1418

942.13 2155.28

wind speed model wind shear model

−252 2053

999 1418

1297.97 2210.64

30.3.4 Boundary Condition Loading Under Four Extreme Conditions The outer ring of the slewing bearing is fixed on the hub, and the inner ring rotates. According to the actual working condition of the bearing, a full constraint is applied to the circumferential surface of the outer ring of the bearing to simulate the constraint of the outer ring of the wheel. A radial bearing load is applied to the lower half of the inner surface of the inner ring to simulate the radial force from the shaft. Finally, the axial force and the overturning moment are applied to the inner ring of the bearing. At the same time, the balls are numbered (where 1# balls are in the soft zone). Table 30.2 shows the external loads acting on the slewing bearings under four extreme conditions [19].

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30.4

357

Analysis of Results

In actual working conditions, the external load of the slewing bearing is extremely complicated. The load states of the bearing soft zone under four operating conditions can be obtained by analysis, which will affect the bearing capacity of the bearing. Figure 30.5 shows the load distribution of the bearing outer ring and the contact stress between the corresponding ball and the outer ring under the first condition. It can be obtained from Fig. 30.5 that the maximum load of the outer ring is 292.55 MPa, and the maximum load is at the contact position of the upper row raceway and the rolling element. In addition, the contact load curve of the rolling element and the outer ring substantially conforms to the cosine function, and the maximum contact load of 71.55 MPa is located near the 1# rolling element (the area where the rolling element is located is exactly the bearing soft zone). Therefore, under the first extreme conditions, the bearing capacity of the bearing soft zone is weak, and the area should be protected from stress.

80

Contact load/MPa

70 60 50 40 30 20 10 0 0

20

40

60

80

100

120

Ball number

(a) outer ring load

(b) Contact load of the ball and the outer ring

Fig. 30.5 Load distribution of the outer ring under the first extreme conditions

(a) cage global load

(b) cage local load

Fig. 30.6 Load distribution of the cage under the first extreme conditions

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140

Contact load/MPa

120 100 80 60 40 20 0

(a) outer ring load

0

20

40

60

Ball number

80

100

120

(b) Contact load of the ball and the outer ring

Fig. 30.7 Load distribution of the outer ring under the second extreme conditions

(a) cage global load

(b) cage local load

Fig. 30.8 Load distribution of the cage under the second extreme conditions

In addition, the cage of the slewing bearing often has a fracture fault, so that the carrying capacity of the cage is briefly analysed. It can be seen from Fig. 30.6 that the maximum load of the cage is 136.37 MPa and located at the joint of the cage. Therefore, the strength of the joint can be strengthened by thickening, widening and so on in the manufacturing process. Figure 30.7 shows the load distribution of the bearing outer ring and the contact stress between the corresponding ball and the outer ring under the second working condition. It is illustrated in Fig. 30.7 that the maximum load of the outer ring locates at the contact position of the upper row of races and the rolling elements with the value of 315.91 MPa. In addition, the maximum contact load of the rolling element and the outer ring is 154.81 MPa, which is located near the 1# rolling element (the area where the rolling element is located is exactly the bearing soft zone), and the larger load is mostly distributed in the 1#–10# rolling element. Near the area, this indicates that the bearing soft zone is subjected to most of the load

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359

45 40

Contact load/MPa

35 30 25 20 15 10 5 0

0

20

40

60

80

100

120

Ball number

(a) outer ring load

(b) Contact load of the ball and the outer ring

Fig. 30.9 Load distribution of the outer ring under the third extreme conditions

(a) cage global load

(b) cage local load

Fig. 30.10 Load distribution of the cage under the third extreme conditions

under this condition, which is not conducive to the safe operation of the wind turbine. Therefore, under the second extreme operating conditions, the bearing capacity of the bearing soft zone is the weakest. Figure 30.8 depicts the load distribution of the slewing bearing cage in the second operating condition. It can be seen that the maximum load of the cage is 154.81 MPa, and the maximum load is located at the connecting portion on the cage. Therefore, in the process of manufacture, the bearing capacity of the cage can be enhanced by thickening, widening and the like. Figure 30.9 shows the load distribution of the bearing outer ring and the contact stress between the corresponding ball and the outer ring under the third condition. It is clear that the maximum load of the outer ring is 154.95 MPa, and the maximum load is at the contact position of the upper row of races with the rolling elements. In addition, the contact load of the rolling element and the outer ring is substantially in accordance with the cosine function, and the maximum contact load of 43.71 MPa

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100 90

Contact load/MPa

80 70 60 50 40 30 20 10 0

(a) outer ring load

0

20

40

60

Ball number

80

100

120

(b) Contact load of the ball and the outer ring

Fig. 30.11 Load distribution of the outer ring under the fourth extreme conditions

(a) cage global load

(b) cage local load

Fig. 30.12 Load distribution of the cage under the fourth extreme conditions

is located near the 70# rolling element. Therefore, under the third extreme conditions, the bearing capacity of the bearing soft zone has little effect. Figure 30.10 describes the stress distribution of the cage under the third operating condition. The maximum load is 84.75 MPa and is also located at the junction where the two rolling bodies are fixed. Therefore, the joints of the cage are all places where the carrying capacity is weak. Figure 30.11 shows the load distribution of the bearing outer ring and the contact stress between the corresponding ball and the outer ring under the fourth working condition. It is shown that the maximum load of the outer ring is 336.79 MPa, and the maximum load is at the contact position of the upper row of races with the rolling elements. In addition, the contact load of the rolling element and the outer ring is substantially the same as the contact load curve of the second working condition, and the maximum contact load of 94.92 MPa is located near the 1# rolling element (the area where the rolling element is located is the bearing soft

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zone). Therefore, the bearing capacity of the bearing soft zone is also weak when it works at the fourth extreme condition. Figure 30.12 shows the stress distribution of the cage in the fourth operating condition, which indicates that the maximum load is 164.11 MPa and is also located at the junction where the two rolling bodies are fixed. As a result, the load on the cage under this condition is the largest.

30.5

Conclusions

In this paper, the soft zone of slewing bearings obtained by the finite element static simulation and analysis is a large load distribution area, and its load distribution provides an important basis for the design of the slewing bearing. (1) The soft zone of the slewing bearing is the weakest area of the entire bearing for carrying capacity. Especially in the case of extreme turbulence and wind shear, most of the load is carried by the soft zone of the bearing. While, it has little effect on the bearing capacity of the bearing soft zone in the case of short-term sudden load. However, the carrying capacity of the bearing soft zone will be greatly weakened, and a soft zone fracture accident may occur if it continuously works. at the extreme working condition. (2) The difference in working conditions does not affect the carrying capacity of the slewing bearing cage. The weak link of the cage is at the joint. Therefore, it is necessary to fundamentally improve the carrying capacity by strengthening the structural design and improving the processing technology. Acknowledgements Financial support from National Natural Science Foundation of China (51575178), financial support from Hunan Natural Science Foundation of China (2018JJ2120).

References 1. Andersen, J.L.: Wind turbine and a pitch bearing for a wind turbine. VESTAS Wind Systems A/S, USA (2011) 2. Yagi, S.: Bearings for wind turbine. NTN Tech. Rev. 71, 40–47 (2004) 3. Burton, T., Jenkins, N., Sharpe, D., Bossany, E.: Wind Energy Handbook. Wiley, New York (2005) 4. Wang, S.M., Luo, J.W., Xu, M.H.: Mechanical analysis of wind turbine slewing bearing. Chin. J. Construct. Mach. 01, 73–76 (2011). https://doi.org/10.15999/j.cnki.311926.2011.01. 024 5. Li, Y.F., Wu, Z.X., Lu, B.H.: Influence of clearance on load distribution of single-row four-point contact ball slewing bearing. Mech. Transm. 03, 56–58 (2010). https://doi.org/10. 16578/j.issn.1004.2539.2010.03.012 6. Li, Y.F.: Influence of design parameters of wind power slewing bearing on bearing capacity. Bearing 12, 7–11 (2011). https://doi.org/10.19533/j.issn1000-3762.2011.12.002

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7. Wu, J.X., Ma, W., Liu, Y.: Finite element analysis of load distribution of large slewing bearings. Mech. Transm. 08, 71–73 (2014). https://doi.org/10.16578/j.issn.1004.2539.2014. 08.043 8. Wang, Y.S., Yuan, Q.Q.: Research on bearing capacity of extra-large double row four point contact ball bearings. J. Mech. Eng. 05, 65–70 (2014). https://doi.org/10.3901/JME.2014.09. 065 9. Liu, R., Li, Y.C.: Research on bearing capacity of single row four-point contact ball slewing bearing. Bearing 12, 34–38 (2018). https://doi.org/10.19533/j.issn1000-3762.2018.12.009 10. Amasorrain, J.I., Sagartzazu, X., Damián, J.: Load distribution in a four contact-point slewing bearing. Mech. Mach. Theory 38(6), 479–496 (2003). https://doi.org/10.1016/S0094-114X (03)00003-X 11. Hernot, X., Sartor, M., Guillot, J.: Calculation of the stiffness matrix of angular contact ball bearings by using the analytical approach. J. Mech. Des. 122(1), 83–90 (2000). https://doi. org/10.1115/1.533548 12. Aguirrebeitia, J., Avilés, R., Bustos, I.F.D., et al.: Calculation of general static load carrying capacity for the design of four-contact-point slewing bearings. J. Mech. Des. 132(6), 64–81 (2010). https://doi.org/10.1115/1.4001600 13. Smolnicki, T., RusińSki, E.: Superelement based modeling of load distribution in large-size slewing bearings. J. Mech. Des. 129(4), 88–92 (2007). https://doi.org/10.1115/1.2437784 14. Smolnicki, T., Derlukiewicz, D., Stańco, M.: Evaluation of load distribution in the superstructure rotation joint of single-bucket caterpillar excavators. Autom. Construct. 17 (3), 218–223 (2008). https://doi.org/10.1016/j.autcon.2007.05.003 15. Kania, L., Krynke, M., Mazanek, E.: A catalogue capacity of slewing bearings. Mech. Mach. Theory 58(58), 29–45 (2012) 16. Kania, L.: Modelling of rollers in calculation of slewing bearing with the use of finite elements. Mech. Mach. Theory 41(11), 1359–1376 (2006). https://doi.org/10.1016/j. mechmachtheory.2005.12.007 17. Liu, S.M.: Winding slewing bearing channel soft belt grinding process and detection technology. Bearing, 06(20–21) (2014).https://doi.org/10.19533/j.issn1000-3762.2014.06. 007 18. Aini, M.M.T.M., Chen, H.L.: ANSYS Workbench 18.0. Introduction and Application of Finite Element Analysis. Mechanical Industry Press, Beijing (2018) 19. Chen, L.: Calculation and analysis of working load of pitch bearing. Harbin Bearing 32(4), 1– 6 (2011)

Chapter 31

Multiple-Model Fault Diagnosis Method for Gas Turbine Based on Soft Switch Yunpeng Cao, Kehui Zeng, Shuying Li, Fengshou Gu, Yuandong Xu, and Bo He

Abstract Fault diagnosis based on a multiple-model (MM) approach is an analytical redundancy method. In this paper, we mainly focus on the model switch optimizing problem of the MM method. First, a MM method for gas turbine gas path fault diagnosis was proposed, and the gas turbine state space models are established based on analytical linearization which can simulate the nonlinear dynamic characteristics at the operating points. Then model soft switch method was proposed based on recursive Bayesian to solve the problem that the established state space model is accurate merely in the vicinity of the operating points, and generates the combined generic model for the full operating condition and make it smoother. Finally, the fault simulation was carried out on a marine gas turbine which shows that the proposed method can diagnose both single gas path fault and multiple gas path faults.

31.1

Introduction

Multiple-model (MM) provides the architecture for a bank of estimators or filters for isolation and identification of faults. It was first proposed by Magil [1] in the study of the optimal adaptive estimation of random processing of samples. In gas turbine related applications, Maybeck [2] were the first to apply MM methods based on the extended Kalman filter (EKF) to detect gas turbine actuator failure and sensor failure, then applied it on gas path fault diagnosis. In order to detect, isolate, and estimate gas turbine gas path fault, an IMM-GLR approach based on interacting Y. Cao (&)
K. Zeng
S. Li College of Power and Energy Engineering, Harbin Engineering University, Harbin, China e-mail: [email protected] F. Gu
Y. Xu Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK B. He College of Automation, Harbin Engineering University, Harbin, China © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_31

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MM and generalized likelihood ratio estimation were developed [3]. To overcome the coupling effect between the gas path fault and the sensor fault, an MM-based detection and estimation scheme for gas turbine sensor and gas path fault diagnosis is proposed [4]. In order to suitable for systems with significant gain variation due to nonlinearity, Strutzel FAM [5] introduces a new multi-model state-space formulation called simultaneous multi-linear prediction (SMLP), this method presents more accurate results than the use of single linear models while keeping much of their numerical advantages and their relative ease of development. The MM based fault diagnosis method mainly needs to solve three problems, including the determination of the model set, the design of the filter and the estimation fusion. The determination of the model set is the main difficulty and key point, and it is also the basis for ensuring the accuracy of diagnosis. It is divided into two parts, firstly the establishment of the model set, followed by the selection and switching of multiple models in the running process. It is well known that one of the main difficulties in the modelling of gas turbine systems is the determination of the mathematical laws that describe these systems. However, obtaining a linear model simplifies the analysis of their dynamic behaviours, as well as the design of their control and surveillance strategies. Hadji [6] deals with a linearization strategy of the non-linear model, developed a novel method for modelling nonlinear dynamic variables of a gas turbine, the effective and equivalent linear approximation of its nonlinear model is realized, and the nonlinear dynamic model identification around the operating point obtained from the actual data is proposed. Qingcai Yang [7] proposed a nonlinear mode set automatic generation method that enables automatic generation of the modes of each level in the MM model. Pourbabaee [8] extended the MM method based on multiple hybrid Kalman filters which represent an integration of a nonlinear mathematical model of the system with a number of piecewise linear models. In this paper, the MM gas path fault diagnosis method for gas turbine based on the soft switch method is studied. In Sect. 31.2, the MM method is briefly introduced, including the principle of Kalman filter and the flow chart of gas path diagnosis based on the MM method. In Sect. 31.3, a method for establishing a general linear model of gas turbine based on piecewise linearization is studied. In Sect. 31.4, the soft switch method is proposed to generates the combined generic model at the full operating process and makes it smoother. Section 31.5 is the single fault and multiple faults simulation testing, and the conclusion is presented in Sect. 31.6.

31.2

Multiple-Model Gas Path Diagnosis Method

Figure 31.1 shows the gas turbine gas path diagnosis based on the MM method [9]. The basic idea of the MM method is to solve a current stochastic system [10, 11]. In order to estimate its current operating state, a set of models is constructed by establishing a hypothetical model of the current possible operating state of the

31

Multiple-Model Fault Diagnosis Method for Gas Turbine …

System y ouput

Control u vector

Filer 1

r1 xˆ1

Filter 2

r2 xˆ2

365

p1

Conditional Hypothesis Probobility Evaluator

p2

...

∑

xˆ MMAE

pM +1

... rn

Filter M+1

xˆM + !

Fig. 31.1 Gas path diagnosis based on MM method

system, and the corresponding filtering is designed for each hypothetical model. By comparing the size of the filtered residuals, the degree of approximation of each hypothesis model in the model set with the actual operating state is characterized, and the filtering residuals are transformed into probability density by probability density function and the conditional probability of each hypothesis model is calculated by the recursive solution. The state estimate of each hypothesis model is fused as the current actual state estimate by the obtained conditional probability. The MM method is characterized by the (M + 1) sets of filters running in parallel, and M is the number of faults to be detected. The fault parameters ai (i = 1, 2, …, M + 1) represent the mode of the system, and a1 is the healthy mode. Assuming that the conditional probability Pi(k) is the probability that the corresponding gas turbine operating state is ai when the measurement parameter is Yk at time k, that is:
 Pj ðk Þ ¼ Pr a ¼ aj jY ðk Þ ¼ Yk

ð1Þ

The corresponding conditional hypothesis probability can be recursive by the Bayesian method using the value of the previous time and the conditional probability density function of the current measurement as shown in Eq. (2).    fyðkÞja;Y ðk1Þ yi aj ; Yk1 Pj ðk  1Þ Pj ðkÞ ¼ M P fyðkÞja;Y ðk1Þ ðyh jah ; Yk1 ÞPh ðk  1Þ h¼1

ð2Þ

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   where fyðkÞja;Y ðk1Þ yi aj ; Yk1 Pj ðk  1Þ is the currently measured Gaussian density function, and it is given by the following equation:    fyðkÞja;Y ðk1Þ yi aj ; Yk1 Pj ðk  1Þ  1 1  j T  j ð1Þ  j  qffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ cc ðk Þ  ffi exp 2 cc ðkÞ Sc ðk Þ ð2pÞq=2 S j ðk Þ

ð3Þ

c

where j = 1, 2,…… (M + 1), ccj ðk Þ and Scj ðk Þ is the residual and covariance matrix of the (M + 1) combination models associated with the fault parameter. The state of the system can be detected and isolated based on the maximum value of Pj(k), whether it is healthy or faulty, and then we can locate the specific failure. The selection of the filter is also very important. For the linear model used in this paper, the Kalman filter is selected for the calculation of multiple models. Equation (4) shows the general form of the state equation of linear discrete system: (

xk þ 1 ¼ Ak xk þ Bk uk þ wk yk ¼ Ck xk þ Dk uk þ vk

ð4Þ

where xk is the state vector, yk is the output vector, and uk is the control vector. There are specific definitions for the x, y, u, respectively, as described in Sect. 31.3. w and v are independent of the Gaussian white noise vector. While w denote the process noise and v denote the measurement noise, assuming they are Gaussian white noise and the covariances are Q and R, respectively. The Kalman filter is applied to the analytical linearization model of the two-shaft gas turbine, as shown in the equation: (

^xk ¼ Ak
xk1 þ Bk
uk ^yk ¼ Ck
^xk þ Dk
uk

ð5Þ

where ^x is the “predicted value”, x is the “update value”. 8 xk ¼ ^xk þ Kk ðy  ^yk Þ > > > > < K ¼ P C T CP C T þ R1 k k k  > Pk ¼ P  K CP > k k k > > :  Pk ¼ APk1 AT þ Q

ð6Þ

where Kk is the Kalman gain. A, B, C, and D are the linear model matrix coefficients. P is the error covariance matrix.

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31.3

367

Gas Turbine State Space Model

Since the two-shaft gas turbine is a complex nonlinear system, and its parameters have a strict dynamic relationship, so we can use the analytical method to obtain a linear model at operating points, then build the overall state space model [12]. The main components of the two-shaft gas turbine studied in this paper are shown in Fig. 31.2. The module is divided into six modules [13], namely, compressor, combustion chamber, turbine, compressor turbine and power turbine between the volume module (volume module 1), power turbine, the power turbine volume module (volume module 2), these modules have clear physical meanings and the corresponding entities, making the system module more intuitive. The gas turbine continuous-time nonlinear model is: (

x_ ¼ f ðx; uÞ

ð7Þ

y ¼ hðx; uÞ where x is the state variable, y is the output variable, u is the control variable. control variable u: u ¼ ½ wf

Hmc

Hgc

Hmh

Hgh

Hgp T

Hmp

ð8Þ

output variable y: y ¼ ½ nl

p5  T

np

t2

p2

t4

p4

t5

x ¼ ½ nl

np

t3

p3

p4

p5  T

ð9Þ

state variable x: ð10Þ

Where f ðÞ and hðÞ are system nonlinear functions. nl is turbine speed, np is power turbine speed, t2 is compressor outlet temperature, t3 is combustion chamber outlet temperature, p2 is compressor outlet pressure, p3 is combustion chamber outlet

Gas

Combustion chamber Compressor

High pressure and high temperature gas

High pressure turbine

Fig. 31.2 Basic compositions of the two-shaft gas turbine

Exhaust

Power turbine

Load

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pressure, t4 is turbine outlet temperature, p4 is turbine outlet pressure, t5 is the power turbine outlet temperature, and p5 is the power turbine outlet pressure. Hmc, Hηc, Hmh, Hηh, Hmp and Hηp are the fault parameters of the control variables. The corresponding types of faults are compressor mass flow reduction, compressor efficiency reduction, turbine mass flow reduction, turbine efficiency reduction, power turbine mass flow reduction, Turbine efficiency reduction. The piecewise linearization is considered in this paper [14], so the steady-state deviation can be defined near the steady-state operating point. Linearizing the

 above-mentioned nonlinear model at a steady-state operating point Sp : xp ; yp ; up : D_x ¼ ADx þ BDu Dy ¼ CDx þ DDu

ð11Þ

Where A, B, C, D are the Jacobian matrices, and The matrix coefficients of each module are solved by using the partial derivative method, replacing the intermediate variable and extracting common factor method. A¼

31.4

@f ðx; uÞ @f ðx; uÞ @hðx; uÞ @hðx; uÞ ;B¼ ;C¼ ;D¼ @x @u @x @u

Model Soft Switch Method

In order to smoothen the general model of the combined run-wide generation, we use soft switch between piecewise linear models. Each linear model is only valid around its associated operating point. Although the range of work has been increased by using a non-linear model rather than a steady-state variable, none of the piecewise linearization models is valid throughout the input variable range. Thus, each piecewise linearization model can obtain the validity function based on its normalized weight obtained by the Bayesian formula. In this paper, the Bayesian method is used to calculate the weight. For this reason, the likelihood sequence and the covariance matrix generated by the Kalman filter bank are used to calculate the likelihood function of the jth sensor pattern of the ith operating region as follows: cði;jÞ ðkÞ ¼ YðkÞ  Y^ ði;jÞ ðkÞ Sði;jÞ ðkÞ ¼ covðcði;jÞ ðkÞÞ f

ði;jÞ

ðc

ði;jÞ

ð12Þ

 1 1 ði;jÞ T ði;jÞ ð1Þ ði;jÞ ðc ðkÞÞ ðS ðkÞÞ ðc ðkÞÞ ðkÞÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  exp 2 ð2pÞq=2 jSði;jÞ ðkÞj ð13Þ

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Where i ¼ 1; . . .; L, j ¼ 1; . . .; ðM þ 1Þ; and L denotes the number of operating points, M denotes the number of faults to be detected, and q denotes the dimension of the measurement parameter. The message sequence cði;jÞ ðkÞ is generated by the Kalman filter bank, whose mean is zero and is a Gaussian white noise process, and the covariance matrix Sði;jÞ ðkÞ is calculated numerically. The normalized weight for the jth sensor is updated recursively using the Bayes formula as follows: wði;jÞ ðkÞ ¼

f ði;jÞ ½ðcði;jÞ ðkÞÞwði;jÞ ðk  1Þ L P f ði;jÞ ½ðcði;jÞ ðkÞÞwði;jÞ ðk  1Þ

ð14Þ

i¼1

The weights calculated above should be kept bounded to avoid them close to zero using the following formula: if wði;jÞ ðkÞ [ a; then wði;jÞ ðkÞ ¼ wði;jÞ ðkÞ if wði;jÞ ðkÞ  a; then wði;jÞ ðkÞ ¼ a

ð15Þ

Where a is the design parameter, which is obtained by experiment, and its size has an effect on the speed of the model switch. The larger values of a result in the faster switch, while smaller values will result in slower conversion between piecewise linearization models. Here, take a ¼ 0:001 to switch the model. After calculating the normalized weight, the weighted new message sequence and the weighted covariance matrix of the general combinatorial model are designed by its piecewise linearization model and the associated normalized weights as follows: cjc ðkÞ =

L X

wði;jÞ ðkÞcði;jÞ ðkÞ

i¼1

Sjc ðkÞ

=

L h X

ði;jÞ

w

i2

ð16Þ ði;jÞ

S

ð kÞ

i¼1

The process above is called the soft switch between piecewise linearization ðjÞ ðjÞ models. Where cc ðkÞ and Sc ðkÞ represent the weighted new message sequence and the covariance matrix for the healthy and faulty sensor modes. Thus, (M + 1) Kalman filters run in parallel to form a multiple Kalman filter structure for diagnose. At this point, the problem of determining the model set has been solved. The MM diagnose method based on the soft switch is applied to the two-shaft gas turbine. The Bayesian algorithm is used to design the soft switch, and the fault or health model of each operation mode is obtained. The probability density of each Kalman filter is obtained by using the hypothesis test algorithm, and the diagnose is realized by finding the maximum value.

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r(1),S(1)

p1

rc(1),Sc(1) L

r(2),S(2)

r(i),S(i)

p2

pi

∑ ⎡⎣ w (

⎤ S( i, j) ( k ) ⎦ Combination of j linear

models L

∑ w( i =1

r(L),S(L)

i, j)

i=1

2

pL

i, j)

rc(2),Sc(2) Conditional hypothesis probability calculation

M A X

FDI

rc(j),Sc(j)

( k ) γ (i , j ) ( k ) rc(M+1),Sc(M+1)

Fig. 31.3 Flow chart based on MM method and soft switch

Based on the design of Kalman filter banks and the application of soft switch in the MM approach, the flow chart based on the MM method and soft switch is showed in Fig. 31.3. In practical applications, there are often more than one failure. The MM based fault diagnosis method established in the previous article is not suitable for multiple fault situations. To overcome this problem, we apply a layered structure to multiple fault diagnosis method. In this approach, a set of Kalman filters operate in parallel to detect and isolate the first fault and then activate the corresponding second-order Kalman filter, and the data-model it records is also updated to detect and isolate the second fault. Compared with the method of running all the models in parallel (single failure model and multiple failure models), this method can reduce costs and save time.

31.5

Case Study

The fault types that are simulated in this paper are all summarized as follows. The simulation tests of 3 different cases with a single fault and multiple faults were carried out respectively. And the single fault cases are divided into noiseless and noisy effects. Hmc = 0.01 in the table indicates the compressor mass flow fault with an amplitude of 0.01 and so on, and Hv indicates the noise fault parameter. Table 31.1 In this paper, p1 to p7 represent the probability of health status and the probability of fault types corresponding to Hmc, Hηc, Hmh, Hηh, Hmp and Hηp respectively. The definition of those fault parameters are shown in Sect. 31.3.

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Table 31.1 Simulation cases Case 1 Case 2 Case 3

371

Hmc

Hηc

Hmh

Hηh

Hmp

Hηp

Hv

0.02 0.05 0.01

0.02 0.05 0.01

0.02 0.05 0

0.02 0.05 0

0.02 0.05 0

0.02 0.05 0

0 0.01 0

31.5.1 Single Fault (1) Single fault case without noise In this section, we discuss the diagnosis result in the case of fault amplitude was 0.02, and the relative fuel flow = 0.95 (select the transition conditions here, so that the soft switch can detect and isolate the faults more stable). Figure 31.4 shows the fault amplitude of 0.02, and at t = 20 s, a failure occurred. Figure 31.4 (a) for the compressor mass flow (p2), Fig. 31.4 (b) for the compressor efficiency (p3) (other failures are similar, not enumerated). The diagnose results under 0.02 fault amplitudes are shown in Table 31.2. td represents the detection time, and ti represents the isolation time. It can be seen from the table that the detection and isolation can be completed in a short time, and there is no misdiagnosis of the situation. Therefore, the MM diagnose method based on the soft switch is of great accuracy. (2) Single fault case with noise In this section, we discuss the accuracy of the MM diagnose method based on the soft switch under the noise effect of the actual nonlinear model of the two-shaft gas turbine. The transition condition (w = 0.95) is also selected. In this section, the change in the size of the noise is achieved by adding an exceptional value, adding an exception to the output of the noise, with exception amplitude of 0.01. The partial measurement parameters after the injection of abnormal noise shown in Fig. 31.5, here the turbine speed (nl) and power turbine speed (np)are presented. 1

1

0.8

0.8

0.6

0.2 0

10

p1 p2 p3 p4 p5 p6 p7

p

p 0.4

0

0.6

p1 p2 p3 p4 p5 p6 p7

0.4 0.2 20

30

40

0

0

10

20

t/s

t/s

(a) p2

(b) p3

Fig. 31.4 Probability of each fault model under case 3

30

40

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p2

p3

p4

p5

p6

p7

td (s) ti (s)

0.86 3.12

0.34 1.04

0.14 0.52

1.28 3.66

0.20 0.66

0.32 1.32

9300

3650

9200

3600

np/(r/min)

nl/(r/min)

Table 31.2 Case 1 simulation results

9100 9000 8900 8800

3550 3500 3450

0

500

1000

1500

2000

2500

3400

0

500

1000

1500

2000

2500

t/s

t/s

(a) turbine speed nl

(b) power turbine speed np

Fig. 31.5 Partial measurement parameters when Hv = 0.01

Table 31.3 Case 2 simulation results

Time

p2

p3

p4

p5

p6

p7

td (s) ti (s)

3.08 11.44

1.14 3.60

0.48 1.94

3.54 11.98

0.64 2.34

1.22 5.18

The diagnose result figures are similar to those in case1, and Table 31.3 is a list of the diagnose results for the presence of abnormal noise. After analysis, we can see that the MM diagnose method based on the soft switch can adapt to the condition of noise, and detect and isolate faults in a short time under the influence of noise.

31.5.2 Multiple Faults This section verifies the accuracy of the MM diagnose method in the case of multiple faults, as well as the transition condition (w = 0.95). The first failure is the compressor mass flow failure. And the second failure occurs in the compressor efficiency. And both of the amplitude of the fault is 0.01 occurred at t = 15 s and t = 20 s, respectively. The results are shown in Fig. 31.6.

Multiple-Model Fault Diagnosis Method for Gas Turbine … 1

1

0.8

0.8

0.6

0.6

p1

p

p2

0.4 0.2

p3 p4 p5 p6

p2

0.4

p3

0.2

p5

p7

00

10

20

30

373

p1

p

31

0

40

p4 p6 p7

0

10

t/s

20

30

40

t/s

(b) the second failure

(a) the first failure Fig. 31.6 Probability of each fault model under case 3

Table 31.4 Case 3 simulation result

First Second

Fault detect result

td (s)

ti (s)

p2 p3

0.34 0.48

0.80 1.72

It can be seen from Fig. 31.6 that Fig. 31.6. (a) is the first diagnose result, (b) is the second diagnose result. The diagnose method detected the first fault at t = 15 s, that is p2, and it was isolated successfully. After isolated the first fault, the first Kalman filter group was expired, and the diagnose method transfer to the second one, which detected the second fault near t = 20 s, and rose to the main failure(p3) to complete the isolation of the second fault. Table 31.4 shows the diagnose results for multiple faults. From the analysis of Fig. 31.6 and Table 31.4, the MM diagnose method based on the soft switch can detect multiple faults accurately and isolate faults in a short period.

31.6

Conclusions

In this paper, a MM gas path fault diagnosis method for a two-shaft gas turbine is established and the model switching problems are optimized. Considering the nonlinear dynamic characteristics of gas turbines, a general state space model based on analytical linearization was established, which can directly calculate the linear model of the full operating process, and the nonlinear dynamic characteristics of the gas turbine are well simulated. The MM diagnosis method with a layered structure is proposed to detect and isolate gas turbines with a single fault and multiple faults.

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According to the problem of switching the linear model between different operating points, the probability-based soft switch technique is used to determine the model according to the different proportion occupied by different piecewise linearized models to make the running area smoother. Finally, the fault simulation of the marine gas turbine is carried out. Through the fault simulation of the marine gas turbine, the verification results show that the MM diagnosis method based on the soft switch can diagnose single gas path faults and multiple gas path faults at different operating conditions. Acknowledgements This paper is supported by Special Project for Aircraft Engine and Gas Turbine (KQB170550-2.1) and the Fundamental Research Funds for the Central Universities (HEUCFP201722).

References 1. Magill, D.T.: Optimal adaptive estimation of sampled stochastic processes. IEEE Trans. Autom. Control 10(4), 434–439 (1965). https://doi.org/10.1109/TAC.1965.1098191 2. Maybeck, P.S.: Multiple model adaptive algorithms for detecting and compensating sensor and actuator/surface failures in aircraft flight control systems. Int. J. Robust Nonlinear Control 9(14), 1051–1070 (1999). https://doi.org/10.1002/(SICI)1099-1239(19991215)9:14%3c1051: AID-RNC452%3e3.0.CO;2-0 3. Yang, Q., Li, S., Cao, Y.: An IMM-GLR approach for marine gas turbine gas path fault diagnosis. Math. Probl. Eng. 2018, 1–12 (2018). https://doi.org/10.1155/2018/1918350 4. Yang, Q., Li, S., Cao, Y.: Multiple model-based detection and estimation scheme for gas turbine sensor and gas path fault simultaneous diagnosis. J. Mech. Sci. Technol. 33(4), 1959– 1972 (2019). https://doi.org/10.1007/s12206-019-0346-6 5. Strutzel, F.A.M., Bogle, I.D.L.: A simple multi-model prediction method. Chem. Eng. Res. Des. 138, 51–76 (2018). https://doi.org/10.1016/j.cherd.2018.08.016 6. Hadroug, N., Hafaifa, A., Kouzou, A., et al.: Dynamic model linearization of two shafts gas turbine via their input/output data around the equilibrium points[J]. Energy 120, 488–497 (2017). https://doi.org/10.1016/j.energy.2016.11.099 7. Yang, Q., Li, S., Cao, Y.: A strong tracking filter based multiple model approach for gas turbine fault diagnosis. J. Mech. Sci. Technol. 32(1), 465–479 (2018). https://doi.org/10.1007/ s12206-017-1248-0 8. Pourbabaee, B., Meskin, N., Khorasani, K.: Sensor fault detection, isolation, and identification using multiple-model-based hybrid kalman filter for gas turbine engines. IEEE Trans. Control Syst. Technol. 24(4), 1184–1200 (2016). https://doi.org/10.1109/ACC.2013.6580567 9. Maybeck, P.S., Hanlon, P.D.: Performance enhancement of a multiple model adaptive estimator. IEEE Trans. Aerosp. Electron. Syst. 31(4), 1240–1254 (1995). https://doi.org/10. 1109/CDC.1993.325104 10. Meskin, N., Khorasani, K., Naderi, E.: Nonlinear fault diagnosis of jet engines by using a multiple model-based approach. J. Eng. Gas Turbines Power 13(1), 63–75 (2011). https://doi. org/10.1115/GT2011-45143

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11. Pourbabaee, B., Meskin, N., Khorasani, K.: Multiple-model based sensor fault diagnosis using hybrid Kalman filter approach for nonlinear gas turbine engines. In; American Control Conference, pp. 4717–4723 (2013). https://doi.org/10.1109/acc.2013.6580567 12. Yang, Q., Li Cao, Y., Gu, F., Ann, S.: A gas path fault contribution matrix based MM-FDI approach for marine gas turbine diagnosis. Energies 11(12), 3315–3338 (2018). https://doi. org/10.3390/en11123316 13. Camporeale, S.M., Fortunato, B., Mastrovito, M.: A modular code for real time dynamic simulation of gas turbines in simulink. J. Eng. Gas Turbines Power 128(3), 506–517 (2006). https://doi.org/10.1115/1.2132383 14. Meskin, N., Naderi, E., Khorasani, K.: Fault diagnosis of jet engines by using a multiple model-based approach, pp. 319–329 (2010). https://doi.org/10.1115/gt2010-23442

Chapter 32

Information System Requirements Elicitation for Gravel Road Maintenance – A Stakeholder Mapping Approach Jaime Campos , Mirka Kans , and Lars Håkansson

Abstract Gravel road maintenance is a complex endeavour comprising physical, technical and social aspects that have to be considered, and resources to be coordinated. The maintenance requirements are affected by weather conditions, geography, and traffic frequency, but also by the physical properties of the road and the previous maintenance performance. The physical properties and maintenance requirements are area dependent; in Northern Europe, ground frost, for instance, is a problem, while flooding is a problem in East Asia. Understanding the specific context-dependent variables is therefore important when designing a maintenance information system. In this paper, the maintenance information system requirements are identified focusing on the Swedish gravel road ecosystem. The system elicitation process is crucial to be able to specify the requirements of the future system and its users, and in this paper, a stakeholders’ approach is utilized. Different stakeholders are described, including their maintenance related needs, information needs, and roles in the maintenance system. The interdependencies between different stakeholders are also illustrated in an ecosystem diagram. From these descriptions, requirements for computerized maintenance management systems are elicited, and main users are identified. User scenarios are thereafter illustrated using the User Case technique.

J. Campos (&)
M. Kans
L. Håkansson Linnaeus University, 351 95 Växjö, Sweden e-mail: [email protected] M. Kans e-mail: [email protected] L. Håkansson e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_32

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Introduction

The maintenance is an essential part of the life cycle cost of a gravel road. In order to achieve cost efficiency, continuous assessment of the road condition is required. Improved efficiency in the maintenance keeps the road in satisfying condition, rejuvenates the road network and reduces the climate load for the road’s total life. The relationship between the ability to assess the infrastructure condition and efficient decision-making is recognized by several researchers [1–4], as well as the lack of suitable decision-making systems [5]. Several of the researchers also recognise the need for developing information systems for the efficient utilisation of the data. The ROADEX Network (a collaboration between Northern European road organisations) for instance has carried out a number of projects concerning the use of modern technology for condition monitoring of gravel road, and a number of commercially available and organisational specific solutions were evaluated [2]. In addition, proposals at the conceptual level for a web-based information system were presented [2, 3]. Alzubaidi [1] developed a new methodology for the objective measurement of road condition and recognised the need for future research in the form of planning systems that support road managers and contractors. Edvardsson et al. [4] carried out tests to evaluate objective measurement methods versus the currently used subjective methods for gravel road conditions, with good results. A national audit conducted in 2009 concluded that the Swedish Transport Administration lacked an efficient system for maintenance management that could be utilised for evaluating maintenance deficiencies, prioritize infrastructure objects, and choose the appropriate maintenance measure [7]. Moreover, the knowledge that exists at the Swedish Transport Administration level is not shared with other stakeholders to a high extent. A digital solution that collects real-time data and provides decision support for the maintenance operator during maintenance work as well as for the maintenance planner to create an optimal climate-smart maintenance plan enables sustainability and cost efficiency. In order to achieve this kind of decision support, different actors have to meet and collaborate in the development of solutions. In addition, by collaborating and each actor will increase their knowledge horizon. Moreover, new technology can give rise to, or be a prerequisite for, new business models for infrastructure maintenance [8–10]. More research is therefore required in understanding business ecosystems, knowledge-driven business models [10], and open source-based information sharing [11], especially in the area of gravel road maintenance. The purpose of this paper is twofold. An information systems requirements elicitation process based on a stakeholder approach is proposed and exemplified in the Swedish gravel road ecosystem context. One purpose is thus to gain a better understanding of the information needs of stakeholders in the Swedish gravel road ecosystem. Another purpose is to extend the knowledge in requirements elicitation from a systems perspective. In addition, digitizing is on top of many agendas as one solution to industrial problems and in realizing Industry 4.0. The reason is to achieve process efficiency and flexibility as well as resource effectiveness

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throughout the whole life cycle [12]. The need for digitalization of gravel road maintenance is also emerging. The current paper is part of the result of the project named Sustainable maintenance of gravel roads, which aims to develop and evaluate innovative technical solutions for the reuse of gravel and digital tools that provide better decision support for planning and maintenance work. The paper emphasizes on the system application requirements engineering part of the system development. The continuation of the paper is ordered in the following sections. In Sect. 2 the Swedish gravel road network and is maintenance requirements is presented. different requirement elicitation methods/approaches are described in Sect. 3. In Sect. 4 a stakeholders’ approach to requirements elicitation is described and exemplified within the Swedish gravel road ecosystem; a stakeholder analysis is performed, the user context is analysed, and thereafter use case scenarios are presented. Finally, conclusions are drawn, and further developments are discussed.

32.2

Maintenance for the Swedish Gravel Road Network

Gravel roads are typically found in areas with low traffic frequency. The Swedish gravel road network consists of two main types of roads: the forest roads owned by forest companies and organizations and trafficked mainly by forestry and transport vehicles, and normal gravel roads, which are owned by the state, the municipality or private road owners and trafficked by private and public vehicles, but also by heavy vehicles to and from the forest roads. The forest roads are robustly built in one layer (sub-base), while normal gravel roads have both a sub base and a surface layer that undergoes regular grading and re-gravelling. Roads could be private, i.e. not open for public use, or the public. Some roads that are privately owned are however also open for public use. Therefore, the private roads in Sweden are categorized into private non-public and private-public roads. The latter receives subsidies from the government. For a road to receive subsidies in form of annual operating grants, it must be at least one kilometre or longer and meet a communication need for the permanent residents, the business sector or the outdoor recreation [13]. The conditions for gravel road maintenance are dependent on the geographical location. In Sweden and other countries in the Northern hemisphere, snow and ice during winter, for instance, must be taken into consideration, as well as during springtime [4]. Maintenance activities in Sweden are therefore highly seasonal. In the springtime, when the road surface layer is naturally humid, and the weather conditions are suitable, grading is conducted. Grading is a critical maintenance activity as it corrects the road geometry in terms of slope, camber, potholes, corrugation, and other incorrectness [14]. If the surface is too dry watering before grading could be required. Moreover, larger stones that occur at the surface have to be removed before grading. For roads with higher traffic frequency grading is done more than once per year. During summertime, dust is a huge problem [15], especially on highly trafficked roads and on roads near houses. Dust control is made in early spring in connection to grading. Chlorides are commonly used as stabilizers, but

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other stabilizers such as lignin, clay, and oil are also available [1]. For certain roads and/or weather conditions, a second round of dust control might be necessary during the summer. In the summertime, maintenance of the edges, ditches and surrounding areas is made. Edges are trimmed to improve the road geometry and for hindering vegetation to be brought on the road by the grader. During edge trimming, recycling of aggregate, i.e. gravel, could be made in order to reduce the cost as well as environmental impact [1, 14]. Ditches that are filled with soil or debris loose drainage abilities and must, therefore, be cleaned [4]. The area nearby must also be cleaned from scrubs and overhanging trees or branches. The gravel surface is controlled regularly and if deemed as needed, gravel is added to the road. Re-gravelling is made for two main reasons; to ensure sufficient surface thickness and particle distribution on the road [1]. If the road surface suffers from irregularities in the form of potholes, corrective maintenance is undertaken by manually filling the holes with gravel. This is a temporary solution that could be conducted during the whole year around. In wintertime, calcium chloride is added to the gravel. In wintertime, the roads are kept snow free by plowing. As a preparation for the winter, the roads are marked using orange sticks, which are evenly placed on either side of the road.

32.3

Requirements Elicitation Methods and Approaches

It is widely known by both industry practitioners and academia that adequately performed requirements-related activities are crucial for a successful outcome of any software project [16]. The software development process consists of several stages, i.e. requirements gathering/specification, analysis, design, architecture, implementation and maintenance [17]. The requirements gathering/specification stage consists of four main activities: (1) elicitation, (2) analysis, (3) specification and (4) validation of software requirements [16]. The software elicitation and its requirements precise the necessities and limitations on a specific software application/service that is aimed at a solution of some real-world problem. The software requirements elicitation can be divided into some steps, i.e. into a breakdown of topics for the software requirements. For instance, the software product/service requirements fundamentals involve a need or some constraints on the software application/service being developed, and the requirement is a limitation on the development of the specific software, for instance, that it will be developed following some specific development process like RUP. The functional requirements define the different functions that the application is intended to execute/perform. Thereafter, are the system and software requirements. The system aspects highlight the requirements for the whole system, i.e. which includes hardware, software, the users, data, information, facilities, and other support elements. Thus, the software components and system requirements are extracted from the system requirements. In addition, the system requirements involve the user requirements as well as the interest of the various stakeholders. The requirement

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process involves the so-called process models, which highlights certain aspects, such as, that the requirement process needs which must be adapted to the necessities to the particular organization and its context. Thus, it highpoints especially the aspects of different activities and its validation in connection with different kind of projects and their constraints. In the same phase are the different actors involved, i.e., the roles of the people who participate in the requirement process. Thus, it involves the stakeholders, such as the users, customers and software engineers etc. The requirements elicitation has to do with aspects of how the software engineer or system developer collects the software application needs. It is believed that it is crucial to have effective communication between the various stakeholders to achieve an optimal requirements elicitation process. In addition, when it comes to the requirement sources, it is essential that they are identified and evaluated. For instance, the goals need to be assessed, i.e. the value (relative to priority) and cost of the different goals. In this stage, the domain knowledge is also a crucial feature that the system developer needs to hold. Next, when the requirement sources have been identified, it is when they can start the elicitation process can start. These elicitation techniques are, for instance, interviews, observation, user stories, scenarios, prototypes etc. For the requirements, elicitation a straightforward approach that uses the well-known UML is to understand the different classes of being the software of application is based on the Use case driven approach [18]. It identifies or derives with the support of the use case diagram and its different scenarios the classes from use cases and illustrates the involvement of classes in use cases of a system.

32.4

A Stakeholder Mapping Approach to Requirements Elicitation

The requirements elicitation process of this paper uses a stakeholders’ approach, i.e. identifying relevant stakeholders and their respective requirements as well as requirements and needs on the ecosystem level. The process is described next in the text below by the steps Stakeholder analysis, User context analysis, Use case scenario development, and Requirements specification and validation. 1. Stakeholder Analysis: The first step is to identify main stakeholders and their interrelationships in order to get a holistic understanding of the ecosystem. This also includes understanding power distribution in the ecosystem, if this could be affecting the information system design. Relevant documents to study in this step are governmental reports, statistics and organizational descriptions, such as web sites and annual reports. In addition, interviews with stakeholders give a deepened understanding of the ecosystem. The result is in the form of an ecosystem diagram describing main stakeholders and their interrelationships. If necessary, zooming in on a particular area of the ecosystem is done, i.e. the focus is put on a specific part of the ecosystem that is of most interest from a systems design perspective. Another option is to focus only on specific

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stakeholders and their requirements. Thereafter, each relevant stakeholder and/ or actor is described with respect to overall objectives, processes and information requirements. The information is gathered through interviews or acquired via focus groups. Organizational documents, such as process and job descriptions, organizational charts etc., are used for gaining a better understanding of the current situation. As the last step, the main users are identified for further analysis. 2. User Context Analysis: All stakeholders that have been identified as main users will be further analysed with respect to context-specific prerequisites. This is done by additional interviews and observations of the work environment in which the system will be operated. Continuous communication with the users is important from this step and onwards. The user context analysis results in a detailed description of each main user and their system related context. 3. Use Case Scenario Development: Several users are often identified for a specific information system. Each user could have different user requirements, which in turn could be overlapping or even contradicting. The use case scenario method gathers all user requirements on overall functionality level. Use case scenarios are developed based on the stakeholder descriptions and user context description. The result is in the form of one or more use case diagrams describing the overall functionality of the information system, and how the users interact with the system. Several use case scenarios could be developed for covering different use cases, and thereafter merged into one big system description. The use case diagrams are presented for the users and thereafter adjusted in an iterative manner. 4. Requirements Specification and Validation: Functional as well as non-functional requirements are specified on the system and subsystem level. The requirements specification is presented and discussed with the users for validation purposes. The requirements elicitation process, steps 1-3, was applied for the case of the Swedish gravel road maintenance. Structured interviews were made with all main stakeholders as listed in the section below. In addition, governmental reports, statistics and organizational documents were used as input when available and relevant. The results from the elicitation process are found in Sects. 4.1–4.2. The formal requirements specification is not presented in this paper.

32.4.1 Stakeholder Analysis and Stakeholder Identification Figure 32.1 depicts the gravel road maintenance ecosystem in Sweden and its main stakeholders. Contractors are companies that carry out maintenance.

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Fig. 32.1 The Swedish gravel road maintenance ecosystem

The contractors are supplied by two main stakeholders; suppliers of equipment, mainly heavy vehicles such as graders and trucks, and suppliers of material in the form of gravel. The contractors coordinate the maintenance activities with the road owners. The private gravel road owners eligible for subsidies receive financial help from the National government agency (The Swedish Transport Administration), but also the municipality. Different kind of road users comprises the final stakeholder type. In Fig. 32.1, these are represented by private road users (normal car traffic), public road users (buses, mail delivery, garbage collection etc.), and users related to the forestry companies (forest harvesters, wood transporters, etc.). The illustration is a simplification of the ecosystem and does not claim to be an exact representation of the full ecosystem. In reality, more stakeholders such as sub-suppliers, trade organizations, legislators, and financiers do exist. The relationships between different stakeholders are also more complex than shown in the figure. The municipality for instance often execute maintenance and are thus having relationships with equipment suppliers and gravel suppliers. In addition, the different actors could be covering one or several of the stakeholder roles. Some of these double roles will be described in the following, while others are unmentioned. The simplification is justified by the purpose of the model - it is describing main stakeholders that could be of interest in the creation of a computerized gravel road maintenance management system. From the stakeholder analysis, four generic stakeholders are identified as candidate system users, i.e. road users, road owners, governing bodies and maintainers. These are explained in the following The road users are the ones driving vehicles on the gravel roads. This is a heterogeneous group of actors spanning from private drivers of cars to forest companies carrying heavy forestry vehicles and transport trucks. Other frequent road users are mail delivery vehicles and garbage trucks. All road users have in common the need for accessibility. In addition, high driving comfort is desired. Emergency vehicles such as ambulances, for instance, require both accessibility and driving comfort; otherwise, the patient would unnecessarily suffer [4]. The road users require information regarding the

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road condition within a specific point in time, and notifications when the road is closed due to, for instance, maintenance. When it comes to the road owners, these are responsible for the management and maintenance of the roads. These actors could be private (persons, organizations or companies) or public (on the municipality or state level). The private road owners are organized in local road associations. According to new regulations, a private road owner has to join such an association in order to receive subsidiaries for annual operations and maintenance. The road owners have a need to achieve an efficient and adequate level of maintenance of the road, thus to not under-maintain or over-maintain the road. The road owners need to keep track of the road condition as well as the maintenance that is planned and conducted on the specific road. In addition, they have to be able to follow up finances in the form of maintenance budgets and costs. Road owners who do not execute maintenance by themselves must be able to procure maintenance from contractors and manage contracts. The governing bodies are mainly represented by the government agency in charge of gravel road maintenance, i.e. The Swedish Transport Administration. The overall transport related goals of the Swedish government are to ensure economically efficient and long-term sustainable transport possibilities for citizens and the business community, reflected in the objectives Accessibility and Safety, environment and health [19]. The governing bodies need to be able to manage subsidies and the requirements connected with the subsidies, mainly that roads are more than 1 kilometres long, open for public access, sufficiently maintained, and managed by a road association. Finally, the maintainers represent the actors who are planning, executing and following up maintenance. These actors could be municipalities, the state (The Swedish Transport Administration), private companies or organizations, or a third party contractor. The maintainers strive for cost-efficient maintenance processes and high resource utilization. Information regarding the road sections, condition of roads, weather forecasts and ground conditions (for instance if that exists or not), annual and long-term maintenance plans are important for the operator executing maintenance, but also for the planner. In addition, the ability to follow up maintenance activities and optimize plans, and manage economy and contracts is required.

32.4.2 User Context Analysis and the Use Case Scenarios In the following, user context analysis and use case scenarios are described for the generic stakeholder Maintainers. Figure 32.2 below shows the contextual aspects for the grader operator, i.e. the driver, in the form of the driver’s cab. The operator requires information regarding the road section, and most graders and similar heavy vehicles today include GPS positioning systems that could be used. The assessment of road condition is today made manually and in a subjective manner by looking out on the road section in front of the grader. Being able to reflect the road condition in an objective and condensed way is thus needed. This is enabled by a

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Fig. 32.2 The contextual aspects of the grader operator

robust and mobile device with appropriate screen size, for instance a laptop. The mobile device needs to fit into the cab and be placed in front of the operator. The use case scenario diagram in Fig. 32.3 highlights the interaction of the different actors, especially for the maintainer (the grader operator), system administrator as well as the planner. The grader operator first login into the system. After that, there are possibilities to see the road condition (the slope, the cross fall, and the road quality) in overall, or the gradation of the aggregate that is put back on the road, if a gravel recycling device is attached to the planner. The data is retrieved from the digital signal processor (DSP), which is connected with the analog to digital converter (ADC) that processes sensor data. Two types of sensors are proposed; ultrasonic sensors measuring road roughness and aggregate gradation, and accelerometers measuring vertical vibrations. Aggregate gradation data is sent via the cloud to the truck driver, so he/she can estimate suitable amount of new gravel to add to the road after planning. In addition, the system provides the option to follow up the work using data that is stored on the database in combination with information regarding positioning using GPS data. This data is uploaded in batch mode into the planning system when a reliable internet connection is availale. In the planning system at the extreme right the planner is shown, which created the annual maintenance plan and provides the operator with weekly and daily schedules. The administrator is able to, for instance, add and remove users.

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GPS log in

plan the work

upload condition data

follow up the work

Operator

see the road condition

see aggregate gradation

Planner

database

Cloud

see slope see crossfall see road roughness

DSP Truck

add users

ADC

Administrator

edit user/s

sensors

sensors

Data acquisition of real time data sensors

Fig. 32.3 A conceptual overview in a use case scenario diagram

32.5

Conclusions

The paper highlights the complexity of the gravel road area where several stakeholders are involved, and the requirements elicitation process with the purpose of understanding of the data/information/knowledge needs of the stakeholders involved in the Swedish gravel road ecosystem, in this case especially the maintainers. In addition, the requirements elicitation process was emphasised from both the specific users as well as from a systems perspective. All identified stakeholders require information regarding the road condition. Thus, this is key information for the ecosystem, and especially for the maintainers. Optimal maintenance execution, as well as planning and decision making, require relevant and reliable information about road condition to be collected in real time while road maintenance is performed. The information could be extended with information regarding resource (such as equipment and human resource) availability, weather conditions and forecasts, and financial data. A use case scenario approach for the requirements engineering and elicitation was shown to be appropriate to achieve the objectives of the current project. However, more effort is still needed in order to understand the business ecosystem and the ways of collaboration that can take place for efficient gravel road maintenance, based on a knowledge-driven business approach. Acknowledgements The research has been conducted as part of the project named Sustainable maintenance of gravel roads funded by the The Kamprad Family Foundation. The project develops new methods and technologies for gravel road maintenance.

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References 1. Alzubaidi, H.: Operations and maintenance of gravel roads: a Literature study. VTI meddelande 852a. Swedish National Road and Transport Research Institute, Linköping (1999) 2. Saarenketo, T.: Monitoring, communication and information systems & tools for focusing actions. Roadex II report. www.roadex.com (2005) 3. Saarenketo, T.: Monitoring low volume roads. Roadex III report. www.roadex.com (2006) 4. Edvardsson, K.: Lågtrafikerade vägar: En litteraturstudie utifrån nytta, standard, tillstånd, drift och underhåll. VTI Rapport 775. Swedish National Road and Transport Research Institute, Linköping (2013) 5. Enkell, K., Svensson, J.: Grusvägsstyrsystem Förstudie: VTI notat44-2000. Swedish National Road and Transport Research Institute, Linköping (1999) 6. Edvardsson, K., Lundberg, T., Sjögren, L.: Objektiv mätmetod för tillståndsbedömning av grusväglag, VTI rapport 863. Swedish National Road and Transport Research Institute, Linköping (2015) 7. Riksrevisionen: Underhåll av belagda vägar, RiR 2009:16. Riksdagstryckeriet, Stockholm (2009) 8. Lingegård S.: Integrated product service offerings for rail and road infrastructure – reviewing applicability in Sweden. Ph.D. Dissertation. Linköping University, Linköping (2014) 9. Pilli-Sihvola, E., Aapaoja, A., Leviäkangas, P., Kinnunen, T., Hautala, R., Takahashi, N.: Evolving winter road maintenance ecosystems in Finland and Hokkaido. Japan. Intell. Transp. Syst. 9(6), 633–638 (2015) 10. Kans, M., Ingwald, A.: A framework for business model development for reaching service management 4.0. J. Maintenance Eng. 1, 398–407 (2016) 11. Metso, L., Kans, M.: An ecosystem perspective on asset management information. Manage. Syst. Prod. Eng. 25, 150–157 (2017) 12. Kans, M., Campos, J., Salonen, A., Bengtsson, M.: The thinking industry: an approach for gaining highest advantage of digitalisation within maintenance. J. Maintenance Eng. 2, 147– 158 (2017) 13. Trafikverket, Ansök om bidrag för enskilda vägar. https://www.trafikverket.se/tjanster/ansokom/ansok-om-bidrag/ansok-om-bidrag-for-enskild-vagar/. Accessed 29 Apr 2019 14. Sveriges Kommuner och Landsting: Mer grus under Maskineriet. Handbok för tillståndsbedömning och underhåll av grusvägar. LTAB, Stockholm (2015) 15. Vatikus, A., Vorobjovas, V., Tuminiene, F., Grazulyte, J.: Experience in rehabilitation of low-volume roads using soft asphalt and Otta seal technologies. Transp. Res. Proc. 14, 2441– 2448 (2016) 16. Bourque, P., Fairley, R.E.: Guide to the software engineering body of knowledge (SWEBOK (R)): Version 3.0. IEEE Computer Society Press, Washington, D.C. (2014) 17. Wong, L.R., Mauricio, D.S., Rodriguez, G.D.: A systematic literature review about software requirements elicitation. J. Eng. Sci. Technol. 12(2), 296–317 (2017) 18. Liang, Y.: From use cases to classes: a way of building object model with UML. Inf. Softw. Technol. 45(2), 83–93 (2003) 19. Swedish government: Mål för framtidens resor och transporter. Regeringens proposition, 93 [e-document], September 2008. https://www.regeringen.se/rattsliga-dokument/proposition/ 2009/03/prop.-20080993/ (2009)

Chapter 33

A Structured Approach to Risk Assessment of Machine Learning Applications Justin Fackrell, Jon Arne Glomsrud, and Siegfried Eisinger

Abstract The accelerating development of better tools and techniques in Machine Learning (ML) is continually lowering the threshold for participation in this field, fuelling increased interest in applying ML techniques to problems related to condition monitoring and asset management. But not all models are good models, and care is required to ensure that the many pitfalls of ML modelling are avoided. In both the consumer/domestic and industrial spaces, recent failures of systems involving ML have highlighted the need for regulatory frameworks for assurance of such systems. In this paper, we identify two key aspects of assurance of data-driven systems: assurance of the modelling process and; assurance of the model itself, and describe how practitioners can take steps to ensure that their data-driven models will work as expected.

33.1

Introduction

Recent years have witnessed an explosion in interest in the application of Artificial Intelligence (AI), Machine Learning (ML) [1] and other data-driven modelling techniques to a wide variety of problems. In the consumer space, the internet giants have rolled out a string of successful deployments of ML-based technologies, ranging from speech-enabled assistants [2] to chess-playing automatons [3] to self-driving cars [4]. In the industrial space, large industry players who have invested heavily in establishing massive IIOT sensor systems [5] to monitor their assets now look to ML to give a return on that investment through improvements in efficiency and safety [6]. Parallel to the increase in availability of data, there has been an increasing effort to democratize the tools of ML: open-source projects such as scikit-learn [7] and the J. Fackrell (&)
S. Eisinger DNV GL, Høvik, Norway e-mail: [email protected] J. A. Glomsrud DNV GL, Trondheim, Norway © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_33

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growing interest in easy-to-learn languages well-suited to data science such as Python and R [8] have lowered the threshold for getting involved in ML. Data extraction, feature engineering, training, evaluation and deployment are all possible in just a handful of lines of code. Progress is even being made in ML models which automatically do all these steps [9]. But the accessibility of data and the ease-of-use of tools for ML should not mask the fact that doing ML right is still hard. There are many pitfalls which the practitioner must avoid, and as Richard P. Feynman said “The first principle is that you must not fool yourself and you are the easiest person to fool” [10]. Problems relating to ML models in high-consequence scenarios have been reported in many fields [11]: In 2018 autonomous car testing in Arizona, USA was interrupted by a fatal accident [12]; A trial of an ML-based oncology system in several countries showed a high lack of agreement between doctors and the system’s predictions [13]. There is growing evidence that lack of public trust is a barrier to adoption of ML-based systems [14]. Regulatory bodies are showing growing interest in establishing trust in systems which use ML. In the US health sector, new FDA guidelines [15] seek to extend the concept of Software as a Medical Device (SaMD) to models for which performance may change over time. In the EU, new guidelines [16] seek to establish a basis for trustworthy AI. And in industry, ISO standards [17] establish guidelines for the use of ML for condition monitoring, while in the Maritime industry class rules are beginning to include requirements on data-driven models [18]. Our recent work is directed towards industrial applications of ML, with focus on applications related to industrial asset management, condition monitoring and condition-based maintenance, but with hopes that our efforts will have wider applicability. This paper first discusses the main motivating factors for turning to ML in an industrial context. It then identifies two key aspects of such ML-based applications where trust can be established: in the modelling process, and in the model itself. It then describes a framework for assurance which can be used for systems which use ML/AI and/or other data-driven techniques. This is organized according to the de facto industry standard CRISP-DM [19], but it can be applied to ML systems regardless of how they were developed. Using the framework, a checklist-driven qualitative risk assessment of any ML project is presented, which is illustrated with examples from some internal projects carried out at DNV GL. This illustrates how the approach can highlight shortcomings in the ML project and be used to organize mitigating action. It is recognized that ML projects are by nature multidisciplinary, and the people holding the domain-knowledge expertise (about oil platforms, ships, wind turbines, etc.) are typically not the same people who hold the modelling expertise (about feature engineering, avoiding overfitting, testing, etc.). The approach presented here has the added benefit that it facilitates communication between these key project stakeholders. Finally, we outline intended future work on this topic.

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The Need for Assurance of ML

There are several reasons why ML can be an attractive solution: 1 Do it faster: do something which is currently performed by humans, but faster 2 Do it better: do something which is currently performed by (one or more) humans, but in a more consistent way 3 Do something new: do something which is currently infeasible To provide trust that an ML application works as expected/required when there are no sufficiently established standards or regulations, trust can be established through a process of assurance. An assurance process aims to identify the application’s critical success criteria and unacceptable consequences, and to establish a sufficient body of evidence showing that these success criteria are met, and the risks managed. This is a pragmatic approach to establishing trust, which at its essence is founded in understanding elements of risk and taking mitigating steps to reduce risk where it is identified as being too high. It is of course essential that any ML application is exhaustively tested to demonstrate its performance prior to acceptance. But relying only on model performance assessment has the consequence that ML modelling projects are often not subject to acceptance/rejection until very late in the development process: the shortcomings of a modelling approach may not be apparent until too late in the project. To provide assurance that an ML application is fit-for-purpose it is therefore useful to divide the assurance problem into two parts: assurance of the model development process (i.e. have all the ML pitfalls been avoided, or were questionable choices made which mean the application is unlikely to fulfil its users’ expectations?) and assurance of the model itself (i.e. is the performance acceptable?). Assurance of the process provides indications of problems early in the process, and assurance of the model itself provides evidence that the performance is satisfactory. Together these establish trust that a given model/application will perform as required and trust that risk presented by potential failures or limitations in the model output during operative use are kept within tolerable levels.

33.3

A Framework for Assurance of ML

Our framework for assurance of ML (and other data-driven models) is due to be presented in full detail in the Recommended Practice DNVGL-RP-0510 [20], but the key concepts are presented here. The framework is structured around the CRISP-DM workflow [19] shown in Fig. 33.1 below, but can be used on any ML project regardless of whether or not CRISP-DM was explicitly used in its development. The CRISP-DM workflow was developed in the late 1990’s and has gained wide acceptance and popularity in the field of data science and machine learning.

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Fig. 33.1 CRISP-DM: Cross Industry Standard Process for Data Mining [12]

Trust in a model is established through the construction of an assurance case: this is a structured document containing detailed claims. Associated with each claim is one or more items of evidence which together support that claim. For example, in the activity data preparation, the claim “the data is representative” may be supported by evidence showing that “the data is drawn from a group of 15 assets over 5 years, plus analysis confirming that this sample satisfactorily covers the feature space”. The claims are listed in a standard document, [20], but the evidence is typically stipulated on a project-by-project basis. Table 33.1 shows examples of some claims and evidence for each of the six activities in the CRISP-DM cycle. The stringency of the requirements on the evidence depends on the criticality of the application: higher consequence applications have a higher level of rigour, and thus more strict requirements on evidence. For example, an ML model whose job is to suggest classifications of documents to a human user has a low criticality (there is no substantial risk to safety or asset integrity if a suggestion is wrong, since the human user can ignore the suggestion) but an ML model which pre-sorts asset condition data into “OK”/“needs attention” where the human operator only looks at the “needs attention” cases has higher criticality and so has more extensive requirements on evidence.

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Table 33.1 Examples of claims and corresponding typical evidence in an assurance case Activity (from CRISP-DM see Fig. 33.1)

Claim

Typical evidence

Business understanding

Business Objectives are clearly defined and understood

Data understanding Data preparation

Data quality is profiled

A value proposition exists identifying the target user of the model, their need, what the model is, how the model meets the user need, how it is different to existing solutions [21] A data quality report [22] exists

Modelling

Missing data records are handled adequately Modelling assumptions appropriate The data used to assess the model is representative

Evaluation

Model is properly assessed

Deployment

Maintenance has been considered

A documented imputation strategy is in place for all features A list of model assumptions is available, with arguments supporting each assumption Documentary evidence is provided that the data used in model evaluation is representative (in terms of feature space coverage) of the domain Documentary evidence is provided that model performance meets business success criteria, data science success criteria A maintenance plan is provided

To date our focus has been restricted to non-critical applications, but this limitation is something which will be addressed in the future. The assurance case is a structured document of claims and supporting evidence and constitutes a living document which can be updated over time as new data/models become available, facilitating rolling assurance that the ML system involved meets expectations.

33.4

Using the Framework

One way in which this framework can be used is to support a qualitative risk analysis (RA) of the process underlying development of an ML application. This aims to provide documented evidence that the numerous potential pitfalls of ML development have been avoided Fig. 33.2.

checklist

risk register

risk matrix

Fig. 33.2 Workflow for checklist-based risk assessment

miƟgaƟng acƟons

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The risk analysis starts with a checklist (Example in Table 33.2) which asks questions about the modelling process. The person doing the ML modelling completes this checklist, possibly with help from someone familiar with the RA process. After completing the checklist, any questions which answer “No” are entered into a risk register (Table 33.3), and a probability and consequence are assigned to each entry in the register. Since it is difficult to assign a numeric probability and consequence to each risk event, broad categories are defined instead. For example, probability and consequence can each be divided into a 3-point scale (low, medium, high). The probability and consequence of each item in the register are then multiplied together and placed in a risk matrix (see Fig. 33.3) which classifies the risks using a traffic-light classification. Any items classified as red are identified as bearing unacceptable risk and require mitigating action. A list of suggested mitigating actions is then produced which can be acted on during the next iteration of the development cycle. Since ML models are inherently probabilistic, there is always a degree of uncertainty in their predictions. Uncertainty is linked to risk, and this should be accounted for in the RA: uncertainty in either estimates of probability and/or of consequence represents an increase in risk, an increase which may ultimately render a model unsuitable for high-consequence applications. In some cases, the addition of more training data, and/or of physics- and causality-constraints can help reduce the uncertainty [23] and thus the risk associated with a model. This can increase the applicability of a model towards its use in higher consequence applications. Table 33.2 Examples of checklist questions: items answered “No” are entered in the risk register (Table 33.3) Activity

Question

Business understanding Data understanding

Have you established the criticality of the model? Do you have a plan for accessing data? Have you consulted with domain-knowledge experts? Have you quality-profiled the data? Is the data GDPR-sensitive? Does all the raw data make it through preprocessing? Is the data representative? Is the test data independent of the training data? Classifiers: are the classes balanced? Was evaluation metric established prior to modelling? Does model evaluation cater for missing data? Does the model performance meet the business needs? Do you have a deployment plan? Do you have a retraining plan?

Data preparation Modelling

Evaluation Deployment

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Table 33.3 Example of risk register: probability and consequence are estimated for each item Item

Risk event

Cause

1 2

Unusable model when deployed Model is unnecessarily complicated, increased cost in training, data storage, maintenance and improvement Test set performance not a good indicator of system performance and deployed model fails to deliver required performance

No plan for accessing data No baseline model

3

4

Model crashes when data with missing features is input

Test data not independent from training data: data is a time series (so neighbouring samples are highly correlated) and test data is randomly sampled from data set No plan for imputing data in case of missing values

Consequence

Probability

low

medium

high

high

medium

high

high

medium

low

medium

high

low

low

low

medium

Fig. 33.3 Risk matrix

This risk assessment is necessarily qualitative in nature, and it is of course challenging to be objective in assigning both probabilities and consequences. But experience has convinced us that this approach has real value to the team involved in the project: it helps project stakeholders who are not familiar with data science to understand where the risky choices are, and it provides a channel through which any expert data scientists within the organization can usefully contribute to a project without having to getting too deeply into the details. And if it is later complemented with a thorough quantitative performance analysis it can provide a level of trust that the ML application is fit-for-purpose. This approach has been successfully trialled in internal ML projects including: 1. Predicting failure of mooring lines of moored assets (e.g. FPSOs and wind turbines) 2. Predicting bearing failure in wind turbine gearboxes 3. Pre-sorting failure detections in subterranean electric supply cables ahead of manual verification An example finding from an in-house ML project was that project was focusing on a rather complex approach to the prediction problem with insufficient heed to developing a simple baseline model against which the performance of the complex

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model could be compared. Once a baseline model was developed, it became apparent that the initial approach, while innovative, was overly complex. Prior to using the checklist, the other stakeholders in the project had not thought to question if the model was overengineered. Use of the RA highlighted this fact in a way that was simple for all involved parties to understand and discuss. In this case, refocusing the project towards a simpler ML approach led to comparable performance, but with much faster training times, better explainability, and a more satisfied customer.

33.5

Future Work

Standards and guidelines are emerging both from regulatory bodies [15, 16] and within industry [18] which are making the first steps towards regulating how ML models are developed and used, but this is at an early stage. Beyond assuring the modelling process, additional trust can be provided by assuring the model itself. A model can be assured in two complementary ways: (1) Assurance of model performance, (2) Assurance of model explainability. Sensible approaches to the evaluation of model performance are part of the assurance process covered by the framework described above, but exactly how acceptance tests for ML should be defined is not yet clear. Indeed, the requirements on model performance may not be static [15]. Some regulatory bodies have started considering requirements on ML explainability (for example [15, 16]). Such requirements can range from being able to enumerate the most important features (possibly with importance scores) to being able to forensically explain why a particular prediction turned out the way it did. In the latter case the search is often on for a “smoking gun” to explain the prediction, but the truth of the matter is that ML models are probabilistic models and may exhibit highly non-linear behaviour whereby a combination of very small changes in feature values are together responsible for a model changing its predicted output. In the future we hope to apply assessments based on this assurance framework to more ML projects, both internally and externally, and to further develop ways of direct model assurance. Ultimately, the aim is to offer a suite of assurance services which together provide the user of any ML model with trust that it will work as expected. Acknowledgements The authors thank our colleagues at DNV GL who assisted in the development of this work.

References 1. Raschka, S.: Python Machine Learning. Packt Publishing, Birmingham (2015) 2. Hoy, M.B.: Alexa, Siri, Cortana, and more: an introduction to voice assistants. Med. Ref. Serv. Q. 37(1), 81–88 (2018)

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3. Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., Hassabis, D.: A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science 362(6419), 1140–1144 (2018) 4. Chan, C.-Y.: Advancements, prospects, and impacts of automated driving systems. Int. J. Transp. 6, 208–216 (2017) 5. Boyes, H., Hallaq, B., Cunningham, J., Watson, T.: The industrial Internet of Things (IIoT): an analysis framework, Comput. Ind. 101, 1–12 (2018) 6. Janakiram, M.: How machine learning enhances the value of industrial Internet of Things, 27 August 2017. https://www.forbes.com/sites/janakirammsv/2017/08/27/how-machine-learningenhances-the-value-of-industrial-internet-of-things/. Accessed 29 Apr 2019 7. scikit-learn. https://scikit-learn.org. Accessed 29 Apr 2019 8. Dialani, P.: Top 10 machine learning programming languages, 2 February 2019. https://www. analyticsinsight.net/top-10-machine-learning-programming-languages/. Accessed 29 Apr 2019 9. h2o corporate website, “H2O” (2019). https://www.h2o.ai. Accessed 16 Apr 2019 10. Feynman, R.P.: Cargo Cult Science, Some remarks on science, pseudoscience, and learning how to not fool yourself. Caltech’s 1974 commencement address (1974) 11. Synced Review, 2018 in Review: 10 AI Failures, 10 December 2018. https://medium.com/ syncedreview/2018-in-review-10-ai-failures-c18faadf5983. Accessed 29 Apr 2019 12. The Guardian, Self-driving Uber kills Arizona woman in first fatal crash involving pedestrian, 18 March 2018. https://www.theguardian.com/technology/2018/mar/19/uber-self-driving-carkills-woman-arizona-tempe. Accessed 29 Apr 2019 13. Strickland, E.: IBM Watson, Heal Thyself. IEEE Spectr. 56(04), 24–31 (2019) 14. Kaur, K., Rampersad, G.: Trust in driverless cars: investigating key factors influencing the adoption of driverless cars. J. Eng. Tech. Manage. 48, 87–96 (2018) 15. US Food and Drug Administration (FDA), Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback (2019). https://www. fda.gov/media/122535/download. Accessed 29 Apr 2019 16. European Commission, Ethics Guidelines for Trustworthy Artificial Intelligence (AI), April 2019. https://ec.europa.eu/futurium/en/ai-alliance-consultation. Accessed 29 Apr 2019 17. ISO: “ISO 13379-1:2012 Condition monitoring and diagnostics of machines – Data interpretation and diagnostics techniques – Part 1: General guidelines,” May 2012. https:// www.iso.org/standard/39836.html. Accessed 29 Apr 2019 18. DNV GL: DNVGL-CP-0484: class programme: approval of service supplier scheme (2019). http://rules.dnvgl.com/docs/pdf/DNVGL/CP/2019-02/DNVGL-CP-0484.pdf. Accessed 29 Apr 2019 19. Wikipedia: Cross-industry standard process for data mining. https://en.wikipedia.org/wiki/ Cross-industry_standard_process_for_data_mining. Accessed 29 Apr 2019 20. DNV GL: DNVGL-RP-0510: Recommended Practice: Framework for assurance of data-driven models (Draft, expected to be published later in 2019) (2019) https://www. dnvgl.com/rules-standards/ 21. Moore, G.A.: Crossing the chasm. Harper, New York (2014) 22. DNV GL: DNVGL-RP-0497 Recommended practice: data quality assessment framework (2017). http://rules.dnvgl.com/docs/pdf/dnvgl/rp/2017-01/dnvgl-rp-0497.pdf. Accessed 29 Apr 2019 23. Eldevik, S.: AI + SAFETY: Safety Implications for Artificial Intelligence, DNV GL, 28 August 2018. https://ai-and-safety.dnvgl.com/. Accessed 29 Apr 2019

Chapter 34

Model Based Monitoring of Dynamic Loads and Remaining Useful Life Prediction in Rolling Mills and Heavy Machinery Pavlo Krot , Ihor Prykhodko , Valentin Raznosilin , and Radoslaw Zimroz

Abstract Operation of industrial metallurgical plants is associated with significant wear in spindles, gearboxes and bearings where difficult to implement digital diagnostic tools due to harsh operating conditions. Angular and radial gaps produce extremely high dynamic loads and abrupt failures in the multi-stand hot rolling mills. Reliable vibration monitoring is very complicated due to inherent changes of technological regimes, treated material and drive speed. It appears more beneficial to monitor dynamic torques in addition to vibration signals, but this is restricted to the installation of strain gauges. The more acceptable approach is to monitor static torques of electric motors and, having identified multi-body models, to calculate remaining useful life (RUL) of elements. Based on this approach, the new monitoring system is developed for the multi-stand mill with integration into plant automation infrastructure. Parameters adaptation of nonlinear dynamical models is provided and technological loads optimization by the criterion of RUL in rolling stands. System supports a database of maintenance actions and elements failures. Reports are generated on overloading and RUL.

P. Krot (&)
R. Zimroz Department of Machine Systems, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wroclaw, Poland e-mail: [email protected] R. Zimroz e-mail: [email protected] I. Prykhodko
V. Raznosilin Department of Metal Forming Processes and Machines, Iron and Steel, Institute of Z.I. Nekrasov, National Academy of Sciences of Ukraine, Dnipro, Ukraine e-mail: [email protected] V. Raznosilin e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_34

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Currently, steel manufacturers are facing a problem to increase the reliability of existing heavy mills to produce wide (up to 5000 mm) and thin (up to 0,8 mm) hot rolled strips of high-grade steels required for different new applications. The main aggregates of rolling mills drivelines were not initially designed for such products and in addition, has deterioration. The increase of reliability can be achieved by passive damping of excessive dynamic loads [1–3], active torsional vibration control [4–7], optimization of technological regimes [8–10] and mill units tuning [11]. Cryogenic treatment is an efficient method to increase wear resistance [12]. Meanwhile, severe failures occur [13–15] in many parts of hot rolling mills (see Fig. 34.1). Investments in the new rolling mills are a rare case and maintenance staff needs computerized tools to predict abrupt failures and to plan repairs. Global digitalization of steel plants [16] promotes effective approaches to prognostics and health management (PHM) of machines [17–19]. Data-driven techniques are developed for rolling mills monitoring [20], on-line identification [21] and model-based diagnostics [22]. The main players in steel processing machinery: Primetals Technologies, SMS Siemag, Danieli and other software companies propose the Computerized Maintenance Management Systems (CMMS) in the market. CMMS are integrated into Enterprise Resource Planning (ERP) systems [23]. Advanced function of remaining useful life (RUL) estimation, if included, is usually based on either failures statistics of certain parts or by special signal processing methods with a short horizon of failure prediction when a first-level alarm has been generated. Algorithms of failure prediction include machine health indicators composed of different parameters of vibration [24, 25]. Advanced methods of condition monitoring and vibration diagnostics systems developed for heavy machinery should account non-stationary loads and speeds [26], be adaptive to variable working conditions [27–29] and support RUL prediction function [30, 31]. Implementation of CMMS on rolling mills is a long-lasting process because some parts have a mean time between failures (MTBF) more than 1 year. Using similar elements from neighbouring stands for building models of RUL and failure prediction is not feasible because of different working conditions. Therefore, world steel manufacturers like Tata Steel, Arcelor Mittal are developing their own systems for specific equipment and processes [32, 33].

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Fig. 34.1 The most critical failures in hot rolling mill drivelines: a roll coupling [1]; b spindle head [1]; c gear [12]; d roll bearing [14]

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The major efforts are applied in cold rolling mills to chatter vibration, which affects output product quality. Results of cold rolling mills research and developed chatter monitoring and control system are represented in [34, 35]. Hot rolling mills are characterized by high torsional dynamics, correspondingly, torque monitoring systems are used, e.g. of ACIDA Torqcontrol [36], KMT Telemetry [37] and some others [38, 39], which are based on wireless telemetry and strain gauges installed on rotating shafts. Methods and models are developed of torque signal processing for diagnostic purposes [40–43] based on torsional modes analysis. Although mechanical torque monitoring systems are the efficient tool for rolling mills, their implementation is limited to Cardan spindles with long service time, because strain gauges mounting is problematic on frequently replaced spindles with bronze slipper pads, which are mainly used in hot rolling mills due to high specific loads. Therefore, some methods are developed in the electric motor current analysis [44–47] for condition monitoring. However, either mechanical or electrical torque measured in only one point of multi-body drivelines does not allow determining dynamic loads and RUL predicting in all elements without dynamic models. Based on numerous research projects conducted in different types of rolling mills [48, 49], authors state that angular and radial backlashes in the gear drivelines and stands are the most important parameters of technical condition and the root causes of local defects and fatigue failures [50]. A common trend on predictive maintenance requires new approaches and software based on adaptive models of complicated industrial plants. This paper represents the multi-disciplinary research and development of intelligent RUL monitoring system for hot rolling mills. Our approach is based on physical models and consistent with the recently introduced term of Dynamics Based Maintenance [51]. Our developed CMMS incorporates such distinctive features as reported in other works: relation of dynamic response and static load [52], rolling stands interaction [53]; RUL prediction using physical degradation model [54]; influence of roller bearing clearances on dynamics [55]; using modal analysis for gear wear assessment [56]; online estimation of driveline dynamic properties [57]; and stress-based high cycle fatigue analysis [58]. Some simplifying assumptions, e.g. neglecting the influence of shafts misalignment on contact pressure in gears, do not restrict the proposed approach, which can be applied for any plants, e.g. heavy mining machinery working in non-stationary conditions.

34.2

Information Structure

The information structure of the developed CMMS is represented in Fig. 34.2. Initial or rarely updated information, supplied on a daily or weekly basis and online signals are shown by different arrows.

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Workshop level Fig. 34.2 General chart of CMMS integration and data flows

Groups of main users and data sources are divided at the enterprise level: plant mechanical manager, reliability department, automation department; and workshop level: head of production, rolls grinding workshop, technical department, mill maintenance mechanics and electricians. Reliability department can provide vibration trends from the vibration monitoring system recently installed on the mill [59]. However, communication with this system is currently under development and planned for the next stage of CMMS deployment.

34.2.1 Mill Equipment and Materials Continuous hot rolling mill consists of 5 roughing stands with 3 edgers and 7 finishing stands. Typical view of hot rolling mills aggregates is represented in Fig. 34.3. Spindles may have Cardan type couplings or slipping pads (Fig. 34.3a).

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In this mill, slipping pads are used with screw-type balancing units that causes inaccurate weight compensation and periodical gaps opening. This leads to high torsional dynamics since about 70% of total angular backlash is concentrated in spindles. CMMS supports hierarchical structure of mill equipment at four levels: stand – aggregate – unit – detail. Each type of elements is associated with the place of its installation. Real exemplars of equipment are coded in corresponding catalogues to track them after replacement. Some details can be placed to a warehouse, and the system continues to accumulate load cycles after their reinstallation in the mill as a member of maybe another assembly units and stands. This important function of CMMS (warehouse support) is necessary for accurate RUL prediction. CMMS contains a catalogue of materials with steel grades, their properties (ultimate and yield stress, endurance limit, hardness). Different heat and chemical treatments of alloy steel are represented as additional records. Materials are linked to types of details. This is basic information for RUL prediction. Ultimate stress (SU) and yield stress (SY) are taken, when available, from steel specifications or calculated from experimental Brinell hardness number (BHN) by the (1), (2); endurance limit (SE) by (3): SU ¼ a1 BHN þ a0 ;

ð1Þ

SY ¼ b1 BHN þ b0 ;

ð2Þ

SE ¼ c1 BHN þ c0 ;

ð3Þ

where ai, bi, ci – coefficients of steel. Fracture point of endurance limit on the S-N curve is taken equal to 5  106 cycles. Endurance limit for bending stress of carburized gear tooth with a hardened layer (2–3 mm) is considered for core material. CMMS contains a catalogue of bearings with their static (C0) and dynamic (C1) load capacity. Properties of material are rarely known from suppliers, hence, standard high carbon, chromium steel is assumed. Bearings are differentiated by suppliers for the analysis of reliability.

(a)

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Fig. 34.3 Typical elements of hot rolling mill: a spindles with balancing unit (www.buma.at); b herringbone pinion stand (galbiatigroup.com); c helical gears reducer (www.indiamart.com)

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34.2.2 Digital Data of Mill Operation The steel plant, where hot rolling mill is operated, has database server in the ERP system where data from different sources are stored with 1 s sampling. Much higher sampling frequency (100 Hz) is provided by ibaPDA system, but only in finishing stands of the mill. The most important signals for CMMS operation are the electric drives rotational speed and rotor armature current. Three types of main drives are used in different stands: direct current and alternative current synchronous in roughing stands and variable frequency drive in finishing stands. The preprocessing procedure includes motor current (A) conversion into torque (kN m), averaging and transforming static torque to work rolls with taking into account electric losses and driveline efficiency. Then, this torque is applied as an input disturbance into a dynamic model of stand and driveline. Technological rolling parameters are as well received and used for rolling force model verification in CMMS optimization application.

34.2.3 Mill Parts Failures and Repairs The time between scheduled repairs varies from 7 to 12 days with a general overhaul at the end of the year. Service personnel cannot repair a single stand while other stands of a continuous rolling mill are working. Hence, in case of failure, the whole mill is stopped for urgent repair. The most critical severe failures of continuous hot rolling mills occur shortly after one of stands overloading causing 6–8 h downtimes for repair (see Fig. 34.1). In the majority of cases, these damages are the result of fatigue as it follows from material microstructure analysis after accidents. Textual and graphical information on maintenance actions of mill staff has been systematized and digitized in accordance with the structure of mill stands equipment. The CMMS catalogue of failures and maintenance actions has the following fields: Position: stand, aggregate, unit, side, position. Failure: crack, deformation, wear, pitting, breakage, heating. Action: inspection, replacement, measurement, grinding, tightening. Repair: urgent, scheduled, overhaul. State: installed, stored, supplied, scrap. This is the only manual input of information in CMMS, which is not possible to avoid. The rest data, coming into the system, automatically processed and visualized via the client application.

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The calculation schemes are composed and dynamical models are identified in the previous research works [40, 41]. The following assumptions are admitted in physical models of mill: • • • • • • • • • • •

Wear of liners in stands is accounted by axial forces on bearings. Work rolls bending is accounted by average values (not measured). Torques in lower and upper spindles are calculated separately. Gap opening in spindles depends on weight balancing (roll position). Gaps in gears are considered closed before strip biting by rolls. Two parallel parts of gears are considered as a whole stiffness. Gear shafts inclination is accounted by a coefficient of bearings wear. Gear shafts bolting is represented by screws tightening forces. Speed of transportation table and vertical rolls are adjusted to stands. Mechanical damping and electrical motor losses are constant. Lubrication influence on specific wear rate is neglected.

34.3.1 Rolling Stands In order to obtain a dynamic response for different elements in the rolling stand, a detailed spring-mass model has been designed (see Fig. 34.4) including hydraulics [10]. This model allows simulating any dynamic processes and determining loads in rolls bearings to predict their RUL. Rolling force is calculated by the drive torque, strip geometry and material reduction in the stand, which are received from the mill automation system.

Fig. 34.4 Rolling mill 4-high stand with hydraulics: a automatic gauge control (AGC), work rolls bending and backup rolls weight balancing; b spring-mass model; and c vertical vibration modes

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One of the important factors of bearings early failures is the work rolls bending for strip profile control. This factor is acting only in two finishing stands and accounted by average value as a non-measured parameter.

34.3.2 Drivelines The typical driveline of the hot rolling mill is given in Fig. 34.5, where: 1 – rolled strip; 2 – work rolls; 3 – spindles; 4 – pinion stand; 5 – main gear coupling; 6 – gearbox; 7 – motor shaft coupling; 8 – single or twin electric drives. Detailed modal analysis of multi-body drivelines is conducted in [41]. Torque measurements in different conditions of mill allowed to verify models and to achieve accuracy of loads calculation about 5% (Fig. 34.6). Although torsional vibration damping ratio is assumed constant in time, it varies for different stands depending on driveline layout.

Fig. 34.5 Elements of the driveline in hot rolling mill (a) and layout of the gearbox (b)

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Fig. 34.7 Simulation of gear shaft motion in the bearing gap (a) and the relation of dynamic load and the radial gap in bearing (b)

34.3.3 Bearings The main feature in dynamics of rolling mills gearboxes is that inertial forces of shafts (up to 50 t) is comparable with a tangential component of torque [40, 41]. Simulation of transient trajectories of gear shafts within a radial gap of bearing resulted in relations of loads and wear (Fig. 34.7). The similar functions obtained for work rolls bearings when they bite the strip.

34.3.4 Dynamical Models Adaptation During the normal process of mill operation, wear of elements in stands and drivelines is gradually increasing with different rates depending on a position in aggregates and history of loading. Degradation of contact surfaces resulted in angular and radial backlashes, which in their turn increase torsional dynamics. Therefore, adaptation is required of dynamic models, which is realized based on known wear models [60–62]: w ¼ k p s;

ð4Þ

where w – wear (mm); k – coefficient of material and contact conditions; p – contact pressure (MPa); s – sliding distance (mm). This approach is used for spindles and bearings. Work rolls bearings are considered in [63]. Known wear (mm) is then transformed into backlash (rad): D¼

h ; R

ð5Þ

where R – radius of rotating part (mm). Wear differs for pinion and wheel and is inversely proportional to the hardness of contact surfaces. The hardness of pinions is 550…630 BHN (case-hardening),

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and wheels – 320…470 BHN (inductive hardening). Sliding distance and contact pressure are calculated from the gear geometry [62]. Contact ratio (overlap) describes tooth load sharing. Influence of gears degradation on contact pressure is neglected. Kinematic viscosity of lubricating oil and contact friction are assumed constant for every pair of gears. Assumption of abrasive wear is supposed without scuffing that is based on the fact that pitting is a rare case in this mill. Spindles are the most difficult for analysis elements due to complicated geometry and simultaneous torsional and bending stress [3]. FEM simulation is used for the analysis of weight balancing force influence on the stress state of spindle shaft [11] and head design optimization [64]. Intensive wear of bronze pads within 14–30 days modifies contact stress distribution and specific wear models are proposed [65]. CMMS model of spindle wear is verified by numerous measurements of bronze pads geometry in stands with a wide range of working conditions. Two failure modes are supposed for spindle parts: • Abrasive wear of bronze pads (up to 5–6 mm). • Cyclic fatigue of heads. CMMS runs the procedure of dynamical models parameters adaptation after every cycle of strip rolling and database is updated before the next strip entering into the stand.

34.3.5 Model of Fatigue Contact and bending stresses of gears are determined by the real-time torque: pffiffiffiffi s ¼ a T;

ð6Þ

r ¼ bT;

ð7Þ

where a, b – coefficients of gear geometry. Since the spindle head is subjected to multi-axial stress, its equivalent value is calculated as: req ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r2 þ 3s2 ;

ð8Þ

To determine cyclic fatigue in elements of rolling mill, the S-N diagrams (Wöhler’s curves) are used. Typical transient torque and fatigue diagram for alloy steel are shown in Fig. 34.8. When torque is about zero, the backlash is opening, and cycles became symmetrical and most dangerous for gears. CMMS uses Goodman’s law to process combined asymmetrical cycles and transforms them into equivalent reversal stress cycles. RUL prediction is based on Miner’s relation.

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Fig. 34.8 Transient torque (a) and S-N diagram for RUL estimation (b)

34.4

Software Applications

CMMS includes three software modules: Server, Client and Optimization. They are the Windows applications developed during research projects carried out in hot rolling mill. Applications have a Cyrillic interface, but can be localized on demand.

34.4.1 Server Application The server application is working online 24/7 independently from other parts of CMMS and using disk space at plant automation department. The main functions of the server application are as following: • • • • •

Automated search and statistical processing of electric drives loads. Time synchronization of electric drives loads with product processing. Calculation of dynamic loads and accumulated damages in elements. Support of maintenance database on repairs, failures and changes. Estimation of RUL in elements by the historical data.

Retrieving online data on electric motors loads, speeds and rolling parameters by stands is carried out by SQL queries from plant automation database. The server has a minimal user interface allowing to start, load parameters of calculation from the configuration file and to install network connection with databases and client application.

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34.4.2 Client Application CMMS client application has the following user-oriented functions: • • • • •

Visualization of loads by elements. Visualization of elements failures. Manual updating of maintenance database. Visualization of accumulated fatigue by elements. Generating reports on overloads, failures and RUL.

Reports on driveline elements overloading include the following parameters: time of mill operation, number of cycles above the limit, maximal/minimal values, and absolute/relative deviation over the limit. Some of the screenshots in client application are shown in Fig. 34.9. The linear trend is used for RUL prediction. Probability of detail failure by overloading is estimated by the tails overlap of stress and material strength distributions. Material properties deviation is estimated, e.g. by gear tooth hardness due to imperfect heat treatment process. In many software applications, calendar time or working hours are used for RUL, which is a simple and proven way for stationary working conditions. In rolling mills, RUL can as well be given in: tonnage of the product; length of rolled strips; and the number of rolled strips (coils). CMMS represents RUL for future periods in both calendar days and number of strips approximated by daily mill output. This is a quite reasonable approach for continuous steel production because maintenance staff needs to plan next scheduled repair, while technology department builds a production plan by rolling cycle (about 30 s).

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Fig. 34.9 Resource usage (a); distributions of stress and material properties (b)

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34.4.3 Optimization Application This software module is designed as an offline application (see CMMS Optimization in Fig. 34.2). The main functions of this module are: • • • • • •

Adaptation of rolling force models by measured drive loads in stands. Verification of models by the actual plan of the rolled product. Calculations of rolling process parameters using adapted models. Calculations of fatigue in the rolling mill equipment. RUL prediction for virtual rolling plans with initial values from CMMS. Optimization of rolling schedules according to RUL related criteria.

Technological department of the hot rolling mill, having such software tool, can make corrections in mill schedules before some troubles may happen in stands due to overloading in the real production process. Beside it, they can estimate what influence will have the new steel grades or critical sizes on overall mill reliability. User can assign various batches of the existing and planned assortment, especially wide thin strips of hard-to-deform steels. Optimization module uses information from the plant and CMMS databases on current RUL of elements, but the user can assign any initial values, e.g. after gears replacement in a stand (RUL = 100%). The generalized criterion is proposed of optimal rolling schedule as the deviation of rolled strips numbers with constraints on strength capacity by torques and minimal RUL values:   min SDðNi Þ; Ti Tmaxi ; RULij RULmin ;

0  ki  1

ð9Þ

where Ni – number of rolled strips; i – stand number; j – element number; ki – load share coefficients; Ti – rolling torque in the stand; Tmaxi – strength capacity of driveline in stand; RULij – remained useful life of elements in stands; RULmin – minimal value (assigned 10–20%). CMMS optimization module accounts constrain imposed by strength capacity and RUL in stands, then adjusts reductions in mill stands by load share coefficients (ki = 0…1). In addition, a useful function is also available to rearrange reductions in stands in case of one stand is out of work (ki = 0), e.g. due to its abrupt failure, and on the go repair.

34.5

System Implementation

CMMS deployment consists of several stages. At the first stage, users need to design catalogues of equipment, materials, maintenance actions and models parameters. Then, to assign the degree of wear and RUL values for all considered in calculations elements currently installed in the mill. This procedure is based on

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expert assessments and historical data for a representative amount of strips (by size and steel grades) with corresponding S-N data for elements. This is some kind of retrospective simulation of the real process. At the second stage, CMMS begins to accumulate S-N data based on real signals. Wear measurements and mill inspections are used for models adaptation by historical data since the previous installation of elements. Statistical analysis of RUL prediction accuracy is possible after a certain period of plant operation when the full service period becomes available for elements after CMMS starting.

34.6

Discussion and Conclusions

A new approach is proposed in hot rolling mills for condition monitoring and RUL prediction based on dynamical models. Angular and radial backlashes are considered as the main parameters influencing high dynamics and severe failures. Although some simplified assumptions are made, they do not reduce models applicability on real equipment, because dynamical models are verified by numerous measurements. Moreover, procedures of parameters adaptation are proposed for more accurate RUL prediction and abrasive wear calculation of driveline elements (spindles, bearings, gears). Client and server software applications are developed and integrated into plant automation infrastructure. The offline module for rolling schedule optimization includes a new criterion – deviation of strips numbers available for rolling by strength capacity and RUL constrains, which provides equalization of loads and safe operation of the continuous mill. The CMMS is developed in correspondence to current trends in condition monitoring and RUL prediction and is applicable in any industrial plants, e.g. heavy mining machinery working in non-stationary conditions. Acknowledgements This work is partly supported by EIT RawMaterials GmbH under Framework Partnership Agreement No. 17031 (MaMMa-Maintained Mine & Machine). Authors are appreciated to plant maintenance and engineering staff for assistance.

References 1. Gharaibeh, N.S., et al.: Loading decrease in metallurgical machines. Res. J. Appl. Sci. Eng. Tech. 8(12), 1461–1464 (2014). https://doi.org/10.19026/rjaset.8.1122 2. Mazur, V., Artyukh, V., et al.: Current views on the detailed design of heavily loaded components for rolling mills. Eng. Designer 37(1), 26–29 (2012). http://eir.pstu.edu/handle/ 123456789/6948

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Chapter 35

Recognizing Life Cycle Benefits of Real Time Fatigue Monitoring for Ecosystems Matti Rissanen, Lasse Metso, Tiina Sinkkonen, and Timo Kärri

Abstract Real time fatigue monitoring creates various benefits in multiple categories appearing at different points of the item’s life cycle. One monitoring and analytics service can be beneficial for a large set of actors, an ecosystem. An ecosystem consists of actors that work towards a common goal, a focal value proposition. In our case, this focal value proposition is management of an item’s life cycle in real time. The purpose of this paper is to recognize benefits related to real time monitoring and analytics of fatigue in welded steel structures and to recognize a set of actors that can form a new ecosystem for creating these benefits. The benefits are recognized in interviews with practitioners who observe the lack of reliable analytics in the remaining life of the items they design, manufacture, maintain or operate. The interviewees represent different parts of an item’s life cycle and during the interviews are asked to recognize and rank benefits and to form a potential new ecosystem from their perspective. The recognized benefits are for example in improving communication between product design and production, improved maintenance scheduling, prolonged production time, correctly timed replacement investment decisions. The interviewees do not form a unified opinion of a new ecosystem but rather we recognize two categories of companies that form the basis for future research in the subject. The interviewees agree that real time monitoring holds huge potential for benefits but is not yet adopted in large scale in practice.

35.1

Introduction

Welded steel structures are designed to sustain certain amount of fatigue in certain conditions but when the conditions are not default but rather varying, e.g. weather or operational effects, the life of the whole item does not follow its design. This causes problems when trying to operate the item affected by fatigue damage optimally. For example, based on our interviews, maintenance is scheduled more often M. Rissanen (&)
L. Metso
T. Sinkkonen
T. Kärri Industrial Engineering and Management, LUT University, Lappeenranta, Finland e-mail: matti.rissanen@lut.fi © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_35

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than necessary to prevent failures and the item is replaced before the real end of its life because reliable information on the remaining life is not available. Refamo is a research project of a real time fatigue monitoring and analytics system for determining the remaining life of a welded steel structure. The interviews conducted in Refamo discussed the benefits of such a system in bigger scope than the specific system can be applied to currently. The interviews provide views on benefits of real time fatigue monitoring in a general level and at the same time help develop the system under research further. In this paper, Refamo refers to real time fatigue monitoring. By applying Refamo to the item facing varying conditions through natural or human causes, the real end-of-life point can be determined and momentary fatigue to the item at any time during monitoring can be determined and analysed. For example, for heavy equipment used in mining, one of the main issues is fatigue caused by natural causes [1] and Refamo can help manage it. We use the term ‘item’ throughout this paper to discuss the different equipment, machines and structures that were subjects of our interviews. [2, p. 12] defines ‘item’ as “part, component, device, subsystem, functional unit, equipment or system that can be individually described and considered”. An ecosystem in a business context is a concept that considers companies and their relations that are not limited to any one industry [3, 4]. The actors within one interact with each other to realize a common goal, a focal value proposition [3]. When we consider an ecosystem that forms around Refamo, we can have various actors operating in different points of an item’s life cycle. For example, an ecosystem can consists of the manufacturers of an item (even if they compete with each other), a monitoring and analytics provider, an operator of the item, a maintenance partner, a research institute studying something related to the item, regulators making regulations based on confirmed data, professional organisations, and so on. This paper discusses the benefits of using Refamo throughout the life cycle of an item and the new ecosystem to be created. In this study, we look to answer two research questions through interviews with practitioners: RQ1: What are the main benefits of using Refamo? RQ2: Which set of actors can form a new Refamo ecosystem?

35.2

Literature Review

Condition-based maintenance (CBM) has been used in automated manufacturing in which condition monitoring is understood to contain data acquisition, processing, analysis, interpretation and extracting information [5]. Vibration analysis has been done with rotating parts in an engine because vibration is one element to cause minor or serious problems to a machine [6]. Structural health monitoring (SHM) can be seen as a strategy for damage identification in aerospace, civil and mechanical engineering infrastructure. SHM is

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based on periodical measurements [7]. SHM in real time can reduce inspection and repair costs. The other advance in SHM is lifetime monitoring of any construction projects. For example, Jindo Bridge was a collaborative project between participants from US, South Korea and Japan to real time monitoring. It was the largest project to monitor a bridge with wireless sensors. A lot of data collected and analysed in the project [8]. [9] examined the implementing of long-term SHM in large-scale bridges. Bridges are unshielded from environmental conditions—humidity, wind, solar-radiation and temperature as well as operational effects—traffic and other loads. SHM can give a new way to inspect and monitor the safety of bridges. Condition monitoring systems have been used in wind turbines and helicopter gearboxes online measurements [10]. Condition monitoring is needed in wind turbines because those are unmanned and located far away from maintenance service providers. Wind turbines are under unkind weather conditions, e.g. wind, heat, cold, lightning, rain, ice and snow. Monitoring the condition of wind turbines preventative maintenance can be adopted instead of corrective maintenance [11]. Many operational fields (e.g. ports) operated with heavy equipment are becoming staffed with less people and therefore there is a need to monitor the equipment and their use in real time [12]. The monitoring and analytics can help in improving the operation of the fields and provide data for operational planning [13]. Successful preventive maintenance operations reduce the unscheduled downtime of an item and therefore can improve the productivity during its life cycle [14]. [15] note that deterioration and failures in items that do not wear evenly during their use can cause high costs or safety hazards. Excessive maintenance can, however, eliminate the cost benefits [16]. [17] consider condition monitoring, life extension, repair versus replace, and optimized life cycle management the most important issues of item life cycle management. Said issues are also of the essence in our research as real time fatigue monitoring in some cases aims to extend production time of an item, provides accurate information on when the items end-of-life is, and allows optimal life cycle management through flexible scheduling of maintenance operations. In some instances, the fatigue on items is highly dependent on how it is operated and the benefits from monitoring the item extends further than maintenance management.

35.3

Research Design

This study is based on data from seven qualitative semi-structured interviews. For structuring the interviews, we adopted the localist position (see [18]) since we are looking into a complex organizational phenomenon of organizations forming an ecosystem to work towards a common goal with a real time fatigue monitoring and analysis technology and tool. Semi-structured interview consists of prepared questioning within pre-determined themes [18].

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The interviewees were chosen based on initial market analysis on national level that recognized these companies as potential benefiters of Refamo and key actors in a new Refamo ecosystem. The companies were also chosen to represent a wide scale of industries and positions in an item’s life cycle. The interviewees’ titles and the fields of business of their companies are presented in Table 35.1 and their position in item’s life cycle is presented in Fig. 35.1. The companies are encoded with letters from A to G and in further sections of this paper the interviews are referred to with the respective company letter. Company G gave us two interviewees and many others mentioned that the questions we sent them beforehand were discussed by a group of people to give us wider perspective from the interviewed company rather than just one view. The interviews were conducted by a team of three university researchers—two concentrated on the benefits and ecosystem of Refamo, one on the technical perspective and expertise in welded steel structures. With this kind of team we could acknowledge the different backgrounds of interviewees by being able to contribute to the discussions raised by our interviewees. Our interview consisted of four themes: product, data, value, and business model. Each theme formed around 2–4 pre-determined questions. The interview themes and questions can be found in Appendix 1. In addition, two surveys were conducted during the interviews. Surveys are used to gather information systematically in a quantitative form [20]. Our surveys were used at the end of value and business model themes to summarize the otherwise qualitative interview and potentially highlight important points that went unheeded. The first survey used the Likert-type scale to rank benefits relevant in the opinion of the interviewee. In the second survey, the interviewees created their own Refamo ecosystem by placing potential stakeholders in a circle where the scale was from one to five—one meaning little relevance and five high relevance within the ecosystem. The mapping of the ecosystem was done in similar manner as in research by [21]. The interviewee could also leave pre-determined potential stakeholders out of the circle or add their own ones. Table 35.1 Interviewees and their fields of business Field of business

Company

Title of interviewee

Construction engineering Maintenance and engineering services Power plant operation Civil engineering Heavy equipment production and services Heavy equipment production and services Power plant operation Power plant operation

A B C D E

Senior structural expert Maintenance manager Investment portfolio manager Bridge specialist Test engineer

F

Research and development manager Managing director Project manager

G G

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C E, F

A Concept and definition

Design and development

B, D, G Production

Installation

Operation and maintenance

Disposal

Fig. 35.1 Life cycle of an item and involvement of interviewed companies during it. [19]

35.4

Results

35.4.1 Benefits from Using Refamo The main results in regards to benefits recognized from interviews are gathered here from each interview. Table 35.2 summarizes the results of the first survey with the most important benefits highlighted in colour in the case of company preferring some 5 rated benefits over others. If the company found all 5’s as important, they are all coloured. A considers that they are not a potential user of Refamo since the items they design have to be designed in a way that they last in any conditions the designed amount of lifetime. They see their field of business as so conservative that even when there are benefits to using Refamo but the value might not be great enough for Refamo to be used. However, the benefits from using Refamo in large items with massive amount of welded structures could come from monitoring critical components and then the interested party would be the supplier of said components. Another benefit comes from automating some scheduled manual measurement operations. Instead of analysing the end-of-life point, the conservative industry could be interested in an analysis of how the item is not failing, and how some standardized scheduled measurements could be made automatic and safer. B emphasizes the role suppliers of items in using Refamo in concept, and design and development phases of item’s life cycle. In process industry, the biggest benefit and financial value from real time monitoring comes through reduced unexpected and planned shutdown time. C noted that in process industry fatigue might not be the biggest issue since the loads often are kept as stable as possible. However, they have process items that endure varying loads where Refamo would be beneficial. These items are not the most important ones for C and therefore the financial value to be gained is unclear. In a bigger picture, some items, e.g. steam boilers, are very critical and the company attempts to calculate and simulate the remaining life of these critical items. A failure of such critical item would cause huge costs as downtime. Real time monitoring is something the company has been looking to improve on since there is also interest in using controlled overload to produce more when market demand is higher. Currently, the take on overload on some occasions according to interviewee is: “’Now we will make money—the machine can take it.’ Sometimes they last, sometimes they do not. It (the decision of overloading) is not based on any data.”

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Table 35.2 Survey on benefits of Refamo Concept, design and development Load and fatigue information for research and development Data storaging and later analyses Development of design methods and practices Production Estimating effects of production quality to item health Installation, operation and maintenance Maintenance planning and execution Monitoring of momentary fatigue Real time monitoring of remaining life of an item Automated actions in regards of item health Sporadic measurement and analytics services Risk management Work safety Monitoring the user of a machine Training of optimal use of a machine Verifying claims for compensation Disposal Forecasting and optimising the remaining life of an item Investment decision making

A

B

C

D

E

F

G

Avg

Mode

5 5

5 4

5 4

2 2

5 5 5

5 5 2

3 5

4,3 4,3

5 5

1

5

4

2

5

4

3

3,4

4; 5

3 3 3 3 3 3 3

4 5 5 4 2 4 5

5 5 5 2 2 5 4

5 2 5 5 4 4 2

4 5 5 4 2 4 2 5 5 4

5 4 2 2 4 2 2 5 2 4

4 4 4 5 5 5 5

4,3 4,0 4,1 3,6 3,1 3,9 3,3

4; 5 5 5 2 4 2

5 3

4 5

5 5

5 5

5 4

4 2

4 4

4,6 4,0

5 5

D noted the same as A, that the items are designed conservatively to last their whole lifetime without further monitoring, in D’s case for a hundred years. However, the loads have increased during the life cycle of current items maintained by D and the loads are expected to increase in future. This means that the items are unlikely to last as long as designed and in some cases monitoring is already applied to failing items. D considers the same as A that the interest towards Refamo could be more among the companies manufacturing the items and it is difficult to see who would gain value from Refamo and therefore be willing to pay for it. E involved in large parts of the item’s life cycle and producing a heavy equipment directly to end customers sees the benefits from Refamo in two broad categories: benefits in the design and production of the item and benefits in monitoring the end users of items that are also maintained by company E. A big issue for E is that tight competition drives the R&D processes to go through faster and time spent on testing is reduced. Therefore, all monitoring and analytics, including Refamo, that could improve the design process with reduced time spent on testing is highly beneficial. The benefits from the end user category come through improved training and knowledge of items along end users. Better use leads to less maintenance from E’s side. F does not do measuring themselves and mainly does the design and development of their items with established data and methods. Refamo would provide benefits in design through more optimized structures but the interviewee is sceptical if it would be cost-effective. A clear benefit would come through end user training and “forcing the end user to use the machine correctly”. F manufactures in a smaller scale same heavy equipment as E and it can be recognized in the interview. Both emphasize the end user perspective and that is why for these interviewees it was included in the first survey.

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G is a small service company involved in the installation, operation and disposal of items and their perspective in the interview was on how to benefit the owner of the items they operate. The main benefit from Refamo would be to reliably prove that the items life cycle is longer than designed and therefore would prolong the production time of items. G operates a fleet that is designed for twenty years of production time but they have a gut feeling that the production time could be much longer. However, they do not currently have a reliable data to prove it and Refamo could be beneficial in that regards. On average, all the benefits surveyed are seen as beneficial by our interviewees (average over 3,0). The ones that have a mode answer of 5 are the most important across the board and can be found in all parts of the item’s life cycle. Different interviewees emphasize different parts of the life cycle and for future research they have to be grouped to be fully comparable and the results to be generalizable.

35.4.2 The New Refamo Ecosystem The second survey conducted during the interviews asked the interviewees to consider Refamo as a focal value proposition (see [3]) in a new ecosystem. Interviewees placed stakeholders they deemed relevant to form the ecosystem and the results are summarized in Table 35.3. Other suppliers included multiple company specific stakeholders and is presented as one. For companies B, F and G, Other suppliers is an average of two answers. Five stakeholders that were only considered relevant by one or two interviewees are not included in the summary. Variances calculated for the answers show that the only consensus about the new ecosystem is that there is no need to involve the competitors in a central role even if there could be benefits through faster development of the service. From the

Table 35.3 Survey on potential stakeholders in a new Refamo ecosystem Company represented by interviewee Interviewee’s customers Competitors Maintenance service provider Other monitoring and analytics providers Steel suppliers Other suppliers End user Refamo company Refamo’s other customers University

A

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2 5 2 5 2 1 5 1 5 2 5

5 5 1

5 3 2

5

5 4 2

4 4

3 2,5

2 1 2

5 5 1 1 4 4

5

5 1 3

1,10 0,56 0,24 2,19 0,67 1,14 1,08 2,25 2,56 1,47 1,10

4 3 2 2 1 4 3

4 1 4 4

3 1 3,5 4 2 2 2

3 4 2 3 5 2 3

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interviewed companies, only A did not consider themselves a potential user of Refamo, the rest placed themselves in the very center of the ecosystem (5) with the exception of company G placing themselves at 4. B, C, E and F offer maintenance services themselves so they did not include other maintenance service providers in the ecosystem creating and sharing benefits of Refamo. In general, the suppliers of interviewees were not seen important in the ecosystem—with some exceptions in A, E and F—but rather the customers of interviewees were considered very important. For E and F the customers include the end users of their items. The role of university and research results in regards to Refamo in the ecosystem varies depending on the interviewees personal background. For example, A has a long research career before their current position and B and E have collaborated with university in research projects. Without A, the answers from other companies vary less with some stakeholders but still, it is difficult to form one unified ecosystem. However, combining the surveys and interview data, we can form two categories of companies (B, C, D, G and E, F) for future research purposes. The main result from the second survey is the position of a Refamo service provider. E and F manufacture items that are used by their customer and have strong research and development departments within their organizations. They are very interested in using the technology but would rather produce the service themselves to gain competitive advantage. The rest of the interviewees would use Refamo in items that are used to produce the offering for their customers. They would not directly gain competitive advantage with Refamo but potentially gain value in other ways. B did not place Refamo company in the ecosystem as they did not want to take a stand on the procurement of Refamo.

35.5

Discussion and Conclusions

The goal of this study was to get a wide understanding about possible benefits created by Refamo and the views on the sets of actors to form an ecosystem around the value proposition. The interviews were approached as a source of good qualitative data with two surveys to summarize the discussions and provide part of the results in quantitative form. Interviewee A did not consider themselves in central position within the Refamo ecosystem whereas all other interviewees did. As can be seen in Fig. 35.1, all but A are involved in the operation and maintenance phase of item’s lifecycle. E and F are involved mainly by providing maintenance services to the machinery they have manufactured, thus they are shown to participate only halfway within the phase. The companies that see themselves in central role within the ecosystem can be placed in two categories. The first category is companies B, C, D and G, who emphasize the operation and maintenance and disposal phases of the item’s lifecycle where Refamo would be applied. They are open to getting Refamo services from an external service provider and are not seeking competitive advantage through Refamo but rather seek to reduce maintenance costs, costs related to lost

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production and increase the production time of their items. The most important benefits discussed with these companies were delaying new or re-investments, reducing production facility downtime due to both scheduled and unscheduled maintenance operations, and more accurate maintenance planning and organisation. The second category is formed by companies E and F. They have monitoring and analytics capabilities within their organisation due to them producing high technology heavy machinery. They are engaged in a very competitive market and while they are clearly looking to gain competitive advantage by using Refamo, they would rather provide the service themselves to prevent competitors from gaining access to the benefits of Refamo. The most important benefits within this category were the feedback loop between product design and production, real usage data in real time for product design, usage data from end users, and optimisation of structures to for example make them lighter. The new ecosystems for each category are presented in Fig. 35.2. The stakeholders scored as 3 or better on average are positioned in the average position for each category. When we consider the benefits within the life cycle of an item, we notice that both recognized categories of companies aim to prolong the operation and maintenance phase through the benefits and the second category looks to also shorten the first three phases of the lifecycle. This is expected since all interviewed companies, with the exception of A, create increasing amount of their revenue in the operation and maintenance phase. The most potential benefits within the Refamo ecosystem are the ones positioned in design and development, production, and operation and maintenance phases within the life cycle. The benefits positioned in other phases cannot be overlooked but based on our interviews; benefits in aforementioned phases are the potential ones. The set of actors to form an ecosystem to realize the value proposition of Refamo differs in the two categories of companies formed based on the interviews. In most answers, the key actors include the company of the interviewee, their customers, a supplier, company providing Refamo as a service, and university.

Refamo’s other customers (3)

Maintenance University (3,5) provider (3,5) Interviewee’s Customers (4)

B, C, D and G

Interviewee (4,75)

Interviewee’s Customers (4,5)

E and F

Other suppliers (3,5)

Interviewee (5) University (3)

Refamo provider (4,67) End user (4) Other monitoring and analytics providers (3)

Fig. 35.2 New Refamo ecosystems for each category

Other monitoring and analytics providers (3,5)

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Limitations and Further Research

35.6.1 Limitations The interviewed companies did not currently have methods to accurately determine the remaining life of their items. The use of real time monitoring and analytics was very low as well. In general, they were very interested in the proposed system and could come up with benefits associated with it but since they have no experience from using such systems, there undoubtedly are still undiscovered benefits involved in using Refamo. Many of the interviewees pointed out some of their stakeholders that could in their opinion be even better ones to interview. In the scale of Refamo research project, the number of interviews were not increased from the initially planned ones. The industries our interviewees are part of differ very much. Some are very conservative, some in rapid growth and some very competitive. The same recognized benefit could create value in different ways depending on the company it is applied in, and for some companies it could be difficult to create any value even if the benefit is very clear.

35.6.2 Further Research The scope of this paper was to identify the potential benefits of using Refamo and recognizing the set of actors to form a new value creating Refamo ecosystem. The value created within the ecosystem considers the costs and risks associated in achieving the identified benefits. The value creation of using Refamo in an ecosystem is to be modelled in further research. It is also necessary to study how the created value is distributed between the actors of the Refamo ecosystem. The value created in an ecosystem should be distributed and used to improve the ecosystem, not just one key actor.

Appendix 1. Interview Themes and Questions Theme 1: Product – Is managing the fatigue damage on a steel structure interesting? – How real time fatigue monitoring (ReFaMo) could be used in items designed, manufactured and/or operated by your company? – What other items could ReFaMo be used with?

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Theme 2: Data – What data do you gather related to steel structures? – What new measurement data ReFaMo would bring to you and how is it compatible with your current measurement and analysis system? – How should data ownership be considered within a service like ReFaMo? Theme 3: Value – What benefits and value ReFaMo could hold? – What kind of decision making situations ReFaMo could be used in? – How would you financially measure the value of ReFaMo? Theme 4: Business Model – Do you buy measurement and analysis services from external providers? – How do you see the role of measurement and analysis services in future (in 2025) in your items and in your industry?

References 1. Barnes, N., Joseph, T., Mendez, P.F.: Issues associated with welding and surfacing of large mobile mining equipment for use in oil sands applications. Sci. Technol. Weld. Joining 20(6), 483–493 (2015) 2. Finnish Standards Association SFS: Maintenance. Maintenance terminology. SFS-EN 13306:2017 (2017) 3. Adner, R.: Ecosystem as structure: an actionable construct for strategy. J. Manag. 43(1), 39– 59 (2017) 4. Moore, J.F.: The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. Harper Business, New York (1996) 5. Campos, J.: Development in the application of ICT in condition monitoring and maintenance. Comput. Ind. 60(1), 1–20 (2009) 6. Goyal, D., Pabla, B.S.: The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Arch. Comput. Methods Eng. 23(4), 585–594 (2016) 7. Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philos. Trans. Roy. Soc. A: Math. Phys. Eng. Sci. 365(1851), 303–315 (2006) 8. Spencer, B.F., Cho, S.: Wireless smart sensor technology for monitoring civil infrastructure: technological developments and full-scale applications. In: Proceedings of the World Congress on Advances in Structural Engineering and Mechanics, Seoul, pp. 4277–4304 (2011) 9. Ko, J.M., Ni, Y.Q.: Technology developments in structural health monitoring of large-scale bridges. Eng. Struct. 27(12), 1715–1725 (2005) 10. Hyers, R.W., McGowan, J.G., Sullivan, K.L., Manwell, J.F., Syrett, B.C.: Condition monitoring and prognosis of utility scale wind turbines. Energy Mater. 1(3), 187–203 (2006) 11. Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T., Ekemb, G.: Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7(4), 2595–2630 (2014)

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12. Mi, C., He, X., Liu, H., Huang, Y., Mi, W.: Research on a fast human-detection algorithm for unmanned surveillance area in bulk ports. Math. Probl. Eng. 2014, 1–17 (2014) 13. Azar, E.R., Dickinson, S., McCabe, B.: Server-customer interaction tracker: computer vision-based system to estimate dirt-loading cycles. J. Constr. Eng. Manag. 139(7), 785–794 (2013) 14. Takata, S., Kirnura, F., van Houten, F.J.A.M., Westkamper, E., Shpitalni, M., Ceglarek, D., Lee, J.: Maintenance: changing role in life cycle management. CIRP Ann. – Manuf. Technol. 53(2), 643–655 (2004) 15. Grall, A., Bérenguer, C., Dieulle, L.: A condition-based maintenance policy for stochastically deteriorating systems. Reliab. Eng. Syst. Saf. 76(2), 167–180 (2002) 16. Yang, Z.M., Djurdjanovic, D., Ni, J.: Maintenance scheduling in manufacturing systems based on predicted machine degradation. J. Intell. Manuf. 19(1), 87–98 (2008) 17. Brown, R.E., Willis, H.L.: The economics of aging infrastructure. IEEE Power Energy Mag. 4 (3), 36–43 (2006) 18. Qu, S.Q., Dumay, J.: The Qualitative Research Interview. Qual. Res. Acc. Manag. 8(3), 238– 264 (2011) 19. International Electrotechnical Commission IEC: Dependability management - Part 3-3: Application guide - Life cycle costing, 60300-3-3:2004 (2004) 20. Groves, R.M., Fowler Jr., F.J., Couper, M.P., Lepkowski, J.M., Singer, E., Tourangeau, R.: Survey Methodology, 2nd edn. Wiley, Hoboken (2011) 21. Jaspersen, L.J., Stein, C.: Beyond the matrix: visual methods for qualitative network research. Br. J. Manag. 30, 748–763 (2019)

Chapter 36

Data Openness Based Data Sharing Concept for Future Electric Car Maintenance Services Lasse Metso , Ari Happonen , Matti Rissanen , Kalle Efvengren, Ville Ojanen , and Timo Kärri Abstract This paper presents a new support concept to electric car owners for better services, based on an idea of utilizing cloud computing, artificial intelligence and real-time condition monitoring. In base principle, the implementation of the concept will be based on distributed data collection and data sharing in order to support electric car services by giving access to the data, which is collected from the cars and offered by a car manufacturer. The reason for focusing on electric cars is that for them the industry standards have still not been set, and from emissions point of view, the electric cars can achieve lower total output of greenhouse gases, than combustion engine-based cars would. By supporting the long life and easier maintenance service offering growth, we can support the effort to mitigate the climate change. However, currently the challenge is that the new electric car manufacturers do not have wide service networks. This is a real barrier for them to enter to market and to take part in the competition. The main idea is to create a concept in where manufacturers could use the cars on the road to generate the information, which the independent car service companies would need in order to offer maintenance service for these new car models. In this manner, the new electric cars maintenance data access costs would be actually lower for the maintenance service providers, than what it is nowadays per brand for the well-established traditional brands. For customer, this can lower life-cycle service costs and improve safety.

36.1

Introduction

The whole automotive industry is currently in transition phase. It seems that the industry will actually be facing a tremendous transition in a long run, as sales numbers and political talks area boosting the shift from internal combustion engine power cars towards electric and autonomous driving cars. From political point of L. Metso (&)
A. Happonen
M. Rissanen
K. Efvengren
V. Ojanen
T. Kärri Industrial Engineering and Management, LUT University, Lappeenranta, Finland e-mail: lasse.metso@lut.fi © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_36

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view, e.g. Norway has stated to deny petrol cars selling by 2025 [1]. Several cities e.g. Berlin, Hamburg, Copenhagen, Rome, Athens, Paris, Madrid, Mexico City, Brussels have or will soon have limitations to use diesel or/and petrol cars in city center, as well as some countries like Denmark, India, Ireland, Israel, Netherlands, France, United Kingdom, Taiwan, China, Germany and some US states will have restrictions for use of combustion engines between 2025 to 2050 [2]. Worldwide plug-in electric vehicles (PEVs) and plug-in hybrid vehicles (PHEVs) sales has increased in recent years, see Fig. 36.1 [3–5]. In Europe in Norway the number of sold electric car has increased rapidly because the state has supported the acquisition of electric vehicles. In year 2018 31,2% of new car sold in Norway were pure electric cars [6]. On the other hand, e.g. in Finland a total number of cars sold on 2018 was 120 499 cars and from those only a tiny amount of 776 were PEVs (0,64%) and 4 797 were PHEVs [7]. It is now expected, that the limitations and bans for internal combustion engine-based cars will increase the demand of PEVs in near future and the development of battery capacity and longer ranges shall be influencing highly positively to PEVs markets shares. The forecast of the growth of electric car vary of lot depending the organization who gives the forecast, see Fig. 36.2. The optimistic forecast is done by Bloomberg New Energy Finance (BNEF) with number 548 million electric cars in 2040, which makes 32% of share in world cars. Organization of the Petroleum Exporting Countries (OPEC) and ExxonMobil gives much more pessimistic predictions. ExxonMobil is a multinational oil and gas corporation. ExxonMobil predictions are 70% lower than BNEF’s [8]. So given the current market situation background and the future development forecasts, the goal for the study is to propose a novel concept of electric car services generation support model, by utilizing cloud computing and real-time condition monitoring. Electric cars are changing the whole car industry because their design nature demands completely new and different services and this allows new players,

Fig. 36.1 Electric car sales 2014–2018 [based on [3–5]]

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Fig. 36.2 Electric car sales forecasts to 2040 [8]

e.g. new car brands from Chinese electric car manufacturers, to come and offer their solutions for interested customers. On the other hand, these new players in the markets, where traditional brands tend to have high hold, have a challenge as they do not have existing facilities and this can in principle be a barrier to starting a new business. However, a new concept in electric car services could remove those barriers, by contacting the service providers, end user customers and the manufacturers together, trough utilization of IoT, cloud-based data sharing models and shared information platform. All this will improve electric car service quality for current and new players with a new way to execute and optimize services for customer.

36.2

Theoretical Background

The Internet of Things (IoT) has the potential to change business processes while wireless sensors can collect data from almost everywhere. IoT can be understood as a socio-technical phenomenon in which is included Technological, Physical and Socio-Economic Environment [9]. Cars are high data output generating platforms on wheel when those are connected to each other and cloud services. Concepts like assistance systems, autonomous driving, proactive maintenance and smart parking are common in presentation of new car models. Open interfaces from cloud services are offered to customers [10]. Business model framework for IoT has been created. In the framework key partners, key activities, key resources, value propositions, customer relationships, channels, customer segments, cost structure and revenue streams were defined as building blocks which contain various elements [11, 12].

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A recent study by Athanasopoulou [13] explores what types of technology-enabled services would have significant impacts on business models within the automotive industry. They found four perspectives on mobility services that could affect business models: personalisation and enhanced user experience services, generic mobility services, shared mobility, and IoT-enabled connected cars. These perspectives show the relevance of servitisation, service bundling and auxiliary services in understanding how business models in the automotive industry will change. The perspectives also illustrate how experts have different perspectives on the abstraction level of technology-induced business model change [13].

36.3

Concept of Data Sharing in Electric Car Services

New players like Tesla, Chinese car manufacturers, Google and Über are all in market by developing disruptive business models. Together with other emerging players in the ecosystem, they keep continuously developing novel platform-based smart services that challenge traditional and de facto models in car industry and by so doing they shape how the business models themselves work, but on another hand, they steer these ecosystems towards more environmental and less polluting global ideologies how transportation from A to B is actually achieved. The concept is based on an idea of data sharing with artificial intelligence (AI) utilizing real-time data from cars. New cars are equipped with sensors which are monitoring the status of the car, and when needed can even take a connection to cloud service. In similar manner, with the shared database, all known (customer accepted) history and car related fault data and cars sensory based event data could be made available, and AI could then efficiently predict the change in the status or analyze any issues in car condition. The concept is illustrated in Fig. 36.3. When implemented properly, the concept can make a disruptive change to the automotive service industry working models, and lower the bar for new brands to enter into the markets. The discussion of some recent platform-based service models like Mobility as a Service (MaaS), Data as a Service (DaaS), Knowledge as a Service (KaaS), anything as a Service (XaaS) and joint use of cars have increased. Globally, this research promotes the growing environmental awareness and coping with tightening emission regulations in automotive industry by the raising the awareness around these novel clean technologies and related services. In addition to the environmental factors, the utilization of AI in the concept will be beneficial also from optimization of safety perspective. AI can be used to suggest the need of service when sensors send warning about components broken. For example Tesla model 3 fault diagnostic can detect broken parts and can suggest to owner to accept the order of spare part and service request [14]. From the customer/user viewpoint, currently car manufacturers are giving all services to customer through licensing, and they have a monopoly to the service of a car. Additionally the position of car servicing companies is not that much better, as they have to pay high fees for the original brands for getting the access into the

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Car owner

Service provider - Collect data - Manage data - Analyse data

Car manufacturer

- Share data Mobile application

Car service Electric car

Fig. 36.3 Concept of data sharing in electric car ecosystem

maintenance data. All of these costs around the data will definitely be expensive to customers. And in fact most car users are more than well educated in this manner as we all know, the maintenance of a vehicle is highly cost centric part of owning a vehicle. On another hand, the new electric cars do need less traditional maintenance services than petrol and diesel cars, for example Tesla gave up annual service keeping some periodic maintenance [15]. The motor of an electric car contains less moving parts than an combustion engine [16]. For maintenance companies themselves, the issue is that the faults in electric and automation systems need different analysis processes than faults in mainly mechanical car systems. CBM (Condition Based Monitoring) is needed with fault diagnostic with AI. A search for hidden faults can take a lot of time, and customer must pay for it. Customer satisfaction increases while service time can be minimized and at the same time, the quality of service increases and costs decrease. The main challenge is to convince new electric car manufacturers that this concept is beneficial for them, as well as to new potential players in the ecosystem. For traditional car manufacturer, it can be difficult to persuade the benefits the new concept can achieve, for example better quality of service and cost efficiency for executing services. The proposed concept is a global data sharing concept based maintenance and service support data ecosystem. This concept makes it possible to new electric car manufacturers to organize car services highly cost efficiently. The role of car manufacturers changes in the new ecosystem and customers have freedom to select car services.

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Based on the developed data sharing service ecosystem concept and the adoption of the business model development framework [11, 12], the building blocks and elements in future electric car service can be derived to be: • Key partners: Cloud service provider, Data analytics service provider, Electric car manufacturers, Car services, Car owners, Application developer • Key activities: Software development, Partner management, Cloud service integration, Registration, Connectivity • Key resources: IoT sensors and networks, Software services, Capability for Data Analytics • Value propositions: Scalability, Performance, Customization • Relationships: Creation, Updating, Managing • Customer segments: Car owners, Car services, Car manufacturers, Software providers, IT-connection organizer • Channels: Internet, mobile • Cost structure: IT costs, Management costs, IT maintenance costs • Revenue Streams: Profit sharing, Fees from users, Software and analysis sales.

36.4

Discussion and Conclusions

Currently, the traditional car manufacturers do have their dedicated and brand connected (own) service networks, while all the new (electric) car manufacturers do have to do without. Building a full branded service chain is a huge overtaking, so basically for any new players, it will definitely be wiser (and faster) to invest deep collaboration with independent service provider, to help new brands customers to get their vehicles safely maintained, within the rules of guarantee and service program. But for in depended operators to be able to start to maintain completely new vehicle types, training programs and data from the vehicles would most like be highly preferred. This data can actually be partially collected from electric cars themselves, by applying sensor technologies [10]. Almost all new modern electric cars can be hooked up to Internet and via that to cloud service, which collects, analyses and interprets the car health related data. The status of car can be monitored basically almost in real-time and the need for service can be defined based on collected data and reference data in cloud service. For independent car services, it will most likely be essential to have access to training material, instructions to execute service and spare part lists in cloud service. By sharing the maintenance data collection and delivery platform, with each other, the new players would not need to invest into own branded and private service networks. This leads to huge drops in per service point related cost and this means that the maintenance companies could be offered substantial discounts for the fees to get the access into the vehicle related maintenance and condition data, for every possible new car brand.

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The CO2, NOx and small particle emissions related future use amount limitations of petrol and diesel cars will create the potential for electric car to increase the market share rapidly. New players have open access to come to market (rapid demand rise will open up slots to offer vehicles in markets, that have been blocked by traditional players at the moment) and some of these new players will not have possibility to collaborate with traditional ones so they shall not have their own service networks. They need the independent car services to take care of their electric car service. The business model of data sharing model makes possible for independent car service to become to service offers for new electric car. For servicing a vehicle, a data about its condition will be needed. For condition data, condition monitoring in electric cars is actually used widely. Information of condition of equipment of cars are collected and analyzed. Sensors are included in electric cars and almost every electric car can be connected to data sharing concept. Real-time data will be collected and data will be shared and AI will be used in analyzing the fault situations and the need for service and spare parts. In addition, an interesting topic is the amount of services with electric cars. Electric motor has less parts and in general, should need less service. For car manufactures, it is expensive to build up their own service networks for electric cars, especially for new players. This is another reason to create data sharing system for electric car manufacturers and services as well as to electric car owners. Future research topics in data sharing concept are in security matters, risk management and data related matters. Huge data amounts, collected in short time periods, with nowadays high-speed censoring capabilities, will demand more and more research in edge computing, 5G and 6G mobile network technologies and e.g. Digital Twin analysis of vehicle conditions. In addition, the other side of the coin will be to consider, how to prevent any sort of misuse of this high detail vehicle specific data, collected from electric cars for maintenance purposes. Driving with electric car from which data is collected to cloud must be safe. Cyber-attacks to moving car must be pre-empted. Another future research topic is the analysis of the value of data in car service ecosystem and based on that the design and concretization of alternative business models for the ecosystem. Who will get benefits of data sharing and how to divide costs caused by data sharing concept? The fair way to share benefits and fees for participant must be found before the concept can be used. The payment can be monthly based or included in procuration of car.

References 1. Staufenberg, J.: Norway to ‘completely ban petrol powered cars by 2025’. In: Independent, 4 June 2016 (2016) 2. Coren, M.: Nine countries say they’ll ban internal combustion engines. So far, it’s just words. In: Quartz, 7 August 2018 (2018) 3. EV-VOLUMES (2015). Global Plug-In Vehicle Sales 2015. http://www.ev-volumes.com/ news/global-plug-in-vehicle-sales/

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4. EV-VOLUMES (2016). Global Plug-In Vehicle Sales 2016. http://www.ev-volumes.com/ news/global-plug-in-sales-for-2016/ 5. Irle, R.: Global EV Sales for 2018 – Final Results (2018). http://www.ev-volumes.com/ 6. Knudesen, C., Doyle, A.: Norway’s electric cars zip to new record: almost a third of all sales. In: REUTERS, 2 January 2019 (2019) 7. Trafficom (2019). Ensirekisteröityjen ajoneuvojen käyttövoimatilastot. https://www.traficom. fi/fi/ensirekisteroityjen-ajoneuvojen-kayttovoimatilastot 8. Coren, M.: Researchers have no idea when electric cars are going to take over. In: Quartz, 18 May 2019 (2019) 9. Krotov, V.: The Internet of Things and new business opportunities. Bus. Horiz. 60(6), 831– 841 (2017) 10. Wollschläger, D.: Preconditions, requirements & prospects of the connected car. Auto Tech. Rev. 5(1), 30–35 (2016) 11. Osterwalder, A., Pigneur, Y.: Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, p. 288. Wiley, USA (2010) 12. Ju, J., Kim, M.S., Ahn, J.H.: Prototyping business models for IoT service. Procedia Comput. Sci. 91, 882–890 (2016) 13. Athanasopoulou, A., de Reuver, M., Nikou, S., Bouwman, H.: What technology enabled services impact business models in the automotive industry? An exploratory study. Futures 109(2019), 73–83 (2019) 14. Rouhiainen, N.: Tesla on kuin Terminator – nyt se tilaa jo varaosat itselleen. In: Kauppalehti, 7th May (2019) 15. Evarts, E.C.: Tesla eliminates annual service, keeps some periodic maintenance. In: Electric Cars, 25 March 2019 (2019). https://www.greencarreports.com/news/1122251_teslaeliminates-annual-service-keeps-some-periodic-maintenance 16. Lampton, C.: Will electric cars require more maintenance? HowStuffworks. https://auto. howstuffworks.com/will-electric-cars-require-more-maintenance.htm

Chapter 37

Detection and Classification of Helicopter Drive Shaft Faults Using Neuro-Fuzzy Based on Wavelet Power Spectrum Algorithm Mohamed A. Hassan , Michael R. Habib , and Abdel M. Bayoumi

Abstract Condition monitoring and quick fault detection and diagnostics of machinery problems can lead to significant reduction in operational and maintenance costs in addition to improving the safety level. In this paper, a new method is proposed for fault detection and diagnoses of the AH-64D helicopter tail rotor drive shaft problems. The proposed method depends on extracting minimum number of indicative features using search algorithm based on wavelet decomposition and power spectrum of vibration signals, then feeding them to a Neuro-fuzzy classification algorithm. The proposed method is verified by using experimental vibration data collected using an AH-64D helicopter test bed. The proposed algorithm shows superior performance in finding a set indicative features and classifying different shaft faults.

37.1

Introduction

Condition based maintenance (CBM) has proven number of benefits over the traditional time based maintenance (TBM) in the condition monitoring of the critical mechanical components in rotorcrafts [1, 2]. CBM has proven to be more reliable, accurate and efficient than TBM. CBM is more important for rotorcrafts as it maintains safety requirements and minimizes breakdown costs. Samuel in [3] discussed the cost of failure of rotorcrafts and summary of accident count and their reasons. CBM implementation follows three steps [1]: First step is data acquisition. Information about the mechanical system under study is collected using sensors attached to the system such as vibration signals [4] and acoustic signal [5]. Second M. A. Hassan (&)
M. R. Habib Electrical Engineering Department, Fayoum University, Fayoum, Egypt e-mail: [email protected] A. M. Bayoumi McNAIR Aerospace Center, University of South Carolina, Columbia, SC, USA © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_37

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step is signal processing to extract features correlated to the health of the system. Many signal processing techniques have been reported in the literature which can be for analyzing signals and extracting features such as time domain analysis [6], frequency domain analysis including [7], time-frequency analysis [8] and wavelet analysis [9]. Third and final step is the fault detection and diagnosis. This step is usually implemented using machine learning algorithms in order to map faults into features [10, 11]. Recent study [12] has been conducted to classify faults by using features directly from power spectrum and bispectrum. It used large number of features and more complex calculation to calculate all the frequency range of the vibration signal. This paper present an improvement to the previous work of [12] in many aspects. It increases the classification accuracy and reduces the number of features used. It also improved feature extraction and reduce computational complexity. In this paper, a proposed algorithm is used to distinguish common driving shaft problems in the Apache tail-rotor drive-train, namely; shaft imbalance, misalignment, and a combination of imbalance and misalignment from a healthy baseline case. The proposed algorithm uses discrete wavelet transform (DWT) in conjunction with power spectrum and search algorithm to extract the minimum number of features that can be used to increase the speed of the classification process as the aim of any diagnostic technique is to detect and locate the smallest possible fault as early as possible. Using real-world vibration data, Neuro-fuzzy algorithm has proven its ability to classify multiclass problems using minimum number of indicative features. This paper is organized as follows: Sect. 37.2, briefly discusses the mechanical experiment setup and the proposed algorithm. Mathematical foundation of wavelet analysis, power spectrum and Neuro-fuzzy are discussed in Sect. 37.3. Section 37.4 discusses the application of the proposed algorithm steps starting with collecting samples, how features are extracted, the Neuro-fuzzy network that has been built and the results. Section 37.5 is the conclusion.

37.2

Experiment Setup and Proposed Algorithm

37.2.1 Mechanical Experiment Setup The analysis method proposed in the paper is applied to vibration signals collected at the McNair aerospace research center at the University of South Carolina (USC) which is working in close collaborating with the South Carolina Army National Guard (SCARNG) in implementing CBM on AH-64D (Apache helicopter) [13, 14]. The McNair research center has a complete AH-64D tail-rotor drive-train test-bed. The test bed emulates a complete tail section of the Apache, as shown in Fig. 37.1(a).

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Vibration data are gathered using two accelerometers placed on the forward hanger bearing (will be shortened to FHB for simplicity) and the aft hanger bearing (AHB) of the Apache experimental test bed shown in Fig. 37.1a [6]. The two accelerometers gather vibration data simultaneously during a 30 min experiment every 2 min as shown in Fig. 37.1b. The experiment is conducted to gather 16 different data set by repeating the experiment four times using four different hanger bearings at the AHB position. For each hanger bearing at this position, four fault scenarios are tested as follows: 1—unbalanced and aligned shafts case, 2—balanced and misaligned shafts case, 3—unbalanced and misaligned shafts case, and 4—baseline aligned and balanced shafts case. Each vibration segment has 65536 data points sampled at 48 kHz and collected at normal operational speed of the helicopter which is 4863 rpm (81.05 Hz). The prime mover for the drive train is an 800hp AC induction motor controlled by variable frequency drive. The torque loads that would be applied by the tail rotor blade are emulated using another motor. Applied torque to the input of the drive shafts equals 32.35 ft.lb, while the applied torque at the output of the tail rotor gearbox equals 111 ft.lb.

Fig. 37.1 a Experimental helicopter tail rotor drive system with labeled drive shafts and indication for sensor places; b sample acquisition of vibration signals

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Fig. 37.2 Flow chart for the proposed algorithm

37.2.2 Proposed Algorithm The proposed algorithm used to classify the state of the tail rotor drive shafts into one of the four categories (healthy case—unbalanced case- misaligned case—unbalanced and misaligned case). Classification is based on vibration data collected from AHB and FHB. It consists of six steps as depicted in Fig. 37.2. First, the vibration samples are collected from AHB and FHB then discrete wavelet transform DWT is used to denoise and decompose signal. Four-level decomposition process is applied which results in four details signal; D1 (details of level 1) to D4 (details of level 4), and the approximation signal of A4. The term “details” indicates the high frequency content of the signal and “approximation” is the low frequency content of the signal. In this study, approximation signal A4 is used because it contains the frequency range that includes the fundamental frequency of the rotating shaft and its harmonics. The third step is to calculate the auto-power spectrum for AHB and FHB approximation signals and the cross-power spectrum between them. Then, spectral peak values at the fundamental frequency and its harmonics are used to select the minimum number of distinguishing features (3 features in this case, as will be shown in Sect. 4.3). The fifth step is to train the Neuro-fuzzy network with some samples, and the last step is to test the performance of Neuro-fuzzy by calculating the classification accuracy.

37.3

Theoretical and Mathematical Foundations

This section discusses some mathematical foundations of signal processing algorithms that are used in this study.

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37.3.1 Discrete Wavelet Transform (DWT) Wavelets is a powerful signal processing tool for decomposing signals into parts which contain small frequency ranges from the frequency range of the original signal [15]. Wavelet analysis has advantage over short time frequency transform that it provides good resolution in the frequency domain at the low frequencies and a good resolution in the time domain at high frequencies [16, 17]. Wavelet transform has two types: continuous and discrete. Continuous wavelet transform (CWT) of a signal xðtÞ is defined as the convolution of xðtÞ with scaled and dilated versions of a mother wavelet function wðtÞ.   1 1 Z  tb Wx ða; bÞ ¼ pffiffiffi xðtÞ:w dt a a 1

ð37:1Þ

where a is dilation factor, b is the translation factor, wab ðtÞ is called the kernel function, which is translated and dilated from mother wavelet as described in (37.2).   1 tb wab ðtÞ ¼ pffiffiffi w a a

ð37:2Þ

where wab is called the daughter wavelet. DWT follows the same idea of CWT but the scaling parameter a and shift parameter b are not continuous but discrete as described in (37.3). DWT produces a family hierarchically organized decomposition due to its dyadic nature as indicated in (37.4). a ¼ 2 j & b ¼ ka  j  j wj;k ð xÞ ¼ 2 2 w 2 2 x  k

ð37:3Þ ð37:4Þ

In this study Daubechies wavelet db10 [16] is used throughout the rest of this paper to decompose vibration signals from AHB, FHB because it has shown the best results in denoising these vibration data compared to other wavelets. DWT is used to decompose signal into multilevel. DWT employs two sets of functions. The first is scaling function which is associated with low pass filter, and the second is wavelet functions which is associated with high pass filters. The signal is decomposed into two frequency bands obtained by successive application of high pass then low pass filtering at the time domain. For each decomposition level j, the signal is decomposed into approximation Aj and details Dj . The term “details” indicates the high frequency content of the signal and “approximation” is the low frequency content of the signal. At each level j, the approximation can be decomposed into approximation and details of level j + 1. Fast wavelet transform (FWT) is an algorithm for signal decomposition and reconstruction that was proposed by Mallat in 1988. The algorithm for FWT is a

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two channel subband coder using conjugate quadrature filters or quadrature mirror filters (QMF) [18, 19]. The decomposition algorithm takes the original signal xðtÞ of length n, and then there are at most log2 n decomposition steps using DWT. Each step produces two sets of coefficients; the details coefficients cDn and the approximation coefficients cAn; where n is the step number. The details coefficients at any level is obtained by convolving the approximations of the previous level (the original signal is considered the approximation of level 0) with high pass filter G followed by dyadic decimation, while to obtain the approximations coefficients previous level approximation convolved with low pass filter H followed by dyadic decimation as shown in Fig. 37.3. The output of the filtering operation and dyadic decimation can be mathematically expressed as in (37.5), (37.6). The filters G and H are quadrature mirror filters, while filters used in reconstruction are conjugate replicas of those filters used in decomposition [20, 21]. yhigh ½k ¼ ylow ½k  ¼

X

x½n:G½2k  n

ð37:5Þ

x½n:H ½2k  n

ð37:6Þ

n

X n

yhigh ½k and ylow ½k are the high pass filter output and the low pass filter output, respectively. Fig. 37.3 Three levels FWT decomposition steps

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37.3.2 Auto and Cross Power Spectrum Spectral information contained in the vibration signals can be extracted using different signal processing techniques. One of the most commonly used technique is the auto-correlation function Rxx ðsÞ which is defined for a stationary signal xðtÞ as follows: Rxx ðsÞ ¼ xðtÞ  xðtÞ ¼

1 Z

x ðtÞxðt þ sÞdt

ð37:7Þ

1

Cross-correlation function can also be used to measure the statistical dependence between two signals xðtÞ and yðtÞ, as shown in Eq. (37.8). Ryx ðsÞ ¼ xðtÞ  yðtÞ ¼

1 Z

x ðtÞyðt þ sÞdt

ð37:8Þ

1

From the experimental point of view, the auto- and cross-correlation can be statistically estimated using finite number of realizations as shown in Eqs. (37.9) and (37.10). Rxx ðsÞ ¼ Efx ðtÞxðt þ sÞg

ð37:9Þ

Rxy ðsÞ ¼ Efx ðtÞyðt þ sÞg

ð37:10Þ

E{.} denotes the expected-function operator. Based on Wiener-Khinchin theorem [22], the auto-power spectrum Pxx ð f Þ and auto-correlation Rxx ðsÞ represent Fourier transform pair while the cross-power spectrum Cyx ð f Þ and the cross-correlation Rxy ðsÞ represent another Fourier transform pair. Pxx ð f Þ and Cyx ð f Þ can be estimated using (37.11) and (37.12) respectively. n o Pxx ð f Þ ¼ E fX  ð f ÞX ð f Þg ¼ E X ð f Þ2 

ð37:11Þ

Cyx ð f Þ ¼ E fX  ð f ÞY ð f Þg

ð37:12Þ

where X ð f Þ is the Fourier transform of xðtÞ.

37.3.3 Search Algorithm The performance of the classification process can be improved by choosing the minimum number of indicative features using search algorithm. The main objective

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using minimum number of features is achieving more accurate classification while maintaining reduced operation complexity. The proposed search algorithm in this paper follows the following procedure: 1- Group samples into classes, four classes is used in this paper (healthy class—misalignment class—imbalance class—imbalance and misalignment class) 2- Calculate all possible features using dedicated signal processing techniques, then consider the minimum and the maximum values of each feature inside each class 3- A feature is promoted for further processing if it is tagged as distinguishing between two or more classes. This tag is achieved if the minimum value of this feature in one class is higher than its maximum value in other class. 4- Features are finally sorted from highest to lowest indicative. Highest indicative feature is the one that distinguishes between more classes

37.3.4 Neuro-Fuzzy Algorithm Neuro-fuzzy is an artificial intelligence technique based on fuzzy logic and neural network. Neuro-fuzzy is characterized by a fuzzy system where fuzzy sets and fuzzy rules are adjusted using neural network tuning techniques (back propagation algorithm) in an iterative way with input output training samples. Neuro-fuzzy works in two phases; the learning phase and the execution phase. In the learning phase, it behaves like neural network to learn its parameter and in the execution phase it behaves like a fuzzy logic system [23, 24]. The neural networks try to shape the biological functions of the human brain. It consists of number of layers where each layer consists of number of neurons. Neuron is the basic element in neural network. The first layer in neural network is the input layer and the last layer is the output layer and all layers in between is called hidden layers. Parameters are used to control the mapping from one layer to the next. Neural network has the ability to change the values of its parameters using training data and algorithms such as back propagation algorithm [25]. In this paper, hybrid Neuro-fuzzy system is used which can be defined as fuzzy system that uses a learning algorithm based on gradients or inspired by the neural networks theory (Fig. 37.4).

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Fig. 37.4 ANFIS network layers

Hybrid Neuro-fuzzy can be constructed using several architectures. Architecture used in this paper is adaptive network based Fuzzy inference system (ANFIS) [26]. ANFIS is used to implement Sugeno fuzzy interference system using five hidden layers network as shown in Fig. 37.5.

37.4

Algorithm Application and Performance Evaluation

In this section the proposed algorithm steps are implemented. Starting with how vibration samples are generated, signal decomposition using wavelet, calculating auto and cross power spectrum, selecting distinguishing features, creating ANFIS network and training it with part of samples.

37.4.1 Acquisition of Samples To test the proposed algorithm, 128 samples are used. Four different hanger bearings are used as dicried in Sect. 2.1. Vibration signals are collected under four different shaft categories (Imbalanced shafts case—Misaligned shafts caseImbalanced-and-Misaligned shafts case, and Healthy case). This results in 16 files to be collected where each file contains two segments vibration, one is AHB vibration and the other is FHB vibration. Each vibration data file contains 65536 data points which is then divided into 8 sections such that a total number of 32 samples is produced for each studies case.

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37.4.2 Wavelet Decomposition Each sample, which contains AHB and FHB vibration signals, is decomposed using Daubechies wavelet db10 into 4 levels, as discussed in Sect. 3.1. Wavelet is used to denoise the signal and to separate it into parts of different frequency ranges. Our interest here is focused on the approximation signal of AHB and FHB because its frequency range contains the fundamental frequency of the rotating shaft and its harmonics while the details range contains the high frequencies which represent noise and don’t contain any indicative features.

37.4.3 Power Spectrum and Features Extraction After decomposing signal into four levels of details and approximation of level 4, auto and cross power spectra are calculated for the AHB and FHB approximations using (37.9) (37.10). Then the spectral components of the fundamental frequency and its first eight harmonics are picked from AHB auto power spectrum, FHB auto power spectrum and cross power spectrum between AHB and FHB. Then all these 24 spectral components were fed to search algorithm which reorder them from the highest to the lowest indicative. The proposed algorithm depends on using the minimum number of indicative features. Three features only are used to classify the signal into one of the four classes. The first feature is the spectral component of the fundamental frequency at the auto power spectrum of the AHB. This feature has always two different set of values, one for classes 1, 3 and the other for classes 2, 4. So, using this feature, the signal can be classified into two groups: class 1 and 3 in the first group and class 2 and 4 in the second group. The second feature is the spectral component of the 8th harmonic of auto power spectrum at the FHB. This component can be used to distinguish between class 1 signals and class 3 signals in the first group. So, using this feature together with the first feature, class 1 and class 3 signals can be classified as shown in Fig. 37.5(a). The third feature is the spectral component of the 8th harmonic of cross power spectrum between AHB and FHB. This feature can distinguish between class 2 samples and class 4 samples in the second group as shown in Fig. 37.5(b). Neuro-fuzzy classifier uses this feature together with feature 1 to classify samples into class 2 or class 4. To demonstrate the role of wavelet decomposition and denoising on the proposed algorithm, the same features are calculated from the power spectrum directly without using wavelet denoising and the same results are plotted as shown in Fig. 37.6. In this case, all classes overlap making it impossible to separate them.

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Fig. 37.5 a Scatter plot for feature 1 and feature 2 b Scatter plot for feature 1 and feature 3

37.4.4 Results of Training ANFIS Network Figure 37.7 depicts the ANFIS Neuro-fuzzy network created after training the initial network with 50% of the samples. In this network there are three input nodes each one represents one feature from the three features discussed in Sect. 4.3. The first hidden layer is used to assign input values for their member functions (MFs). Each of the three inputs has two MFs. The second hidden layer is the rule layer which consists of four neurons. The function of this layer is to determine rules that control transferring inputs to outputs. Every neuron represents one rule. AND operation applies between all input conditions in rules layer. Four rules are used to determine the result based on the relation between MFs, and every rule has two inputs. The third hidden layer is used to normalize the output value of the rule layer. The fourth hidden layer is used to calculate the global output from the normalized value of the four used rules. The output layer generates the classification result of the input sample.

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Fig. 37.6 a Scatter plot for feature 1 and feature 2 without using wavelet b Scatter plot for feature 1 and feature 3 without using wavelet

Fig. 37.7 ANFIS network used to classify data

After using 50% of the samples to train the Neuro-fuzzy algorithm, and 10% of the samples for validation, the remaining samples have been used to test the performance. The accuracy of the proposed algorithm using Neuro-fuzzy based on wavelet power spectrum and the selective search algorithm is found to be 99.22%. This result indicates how the proposed algorithm could improve the performance

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compared to results in [12] which use large number of features directly extracted from power and bispectrum. The proposed algorithm has less computational complexity compared to previously used algorithms.

37.5

Conclusion

In this paper, the fault diagnosis of rotating drive shaft problems has been improved by using three indicative features to train a pattern recognition algorithm. These features are extracted from auto and cross power spectral components of the vibration signals at AHB and FHB and cross power spectrum between them. In order to improve the feature extraction accuracy, wavelet (db10) is used to decompose signal into four levels and takes approximation of level 4 to compute power spectrum for it and get indicative features. The proposed algorithm has proven its ability to distinguish four different health conditions for rotating drive shafts (imbalance, misalignment, combined shaft imbalance and misalignment, and healthy case). The proposed method has shown high performance in all of these criteria. Neuro-fuzzy has shown the highest classification performance with 99.22% accuracy.

References 1. Jardine, A., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Sig. Process. 20(7), 1483–1510 (2006) 2. Hashemian, H.M., Bean, W.C.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Measure. 60(10), 3480–3492 (2011) 3. Samuel, P., Pines, D.: A review of vibration-based techniques for helicopter transmission diagnostics. J. Sound Vibr. 282(1–2), 475–508 (2005) 4. Zhang, Y., Lei, P., Wang, H., Hong, W.: High-frequency vibration compensation of helicopter-borne THz-SAR [Correspondence]. IEEE Trans. Aerosp. Electron. Syst. 52(3), 1460–1466 (2016) 5. Orman, M., Rzeszucinski, P., Tkaczyk, A., Krishnamoorthi, K., Pinto, C.T., Sulowicz, M.: Bearing fault detection with the use of acoustic signals recorded by a hand-held mobile phone. In: International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), Bangalore, pp. 252–256 (2015) 6. Lebold, M., McClintic, K., Campbell, R., Byington, C., Maynard, K.: Review of vibration analysis methods for gearbox diagnostics and prognostics. In: Proceedings of Society for Machinery Failure Prevention Technology, 54th Meeting, Virginia Beach, VA, pp. 623–634, May 2000 7. Jang, B., Shin, C., Powers, E.J., Grady, W.M.: Machine fault detection using bicoherence spectra. In: 21st IEEE Instrumentation and Measurement Technology Conference, vol. 3, pp. 1661–1666, May 2004 8. Zhang, R., Li, G., Zhang, Y.D.: Micro-doppler interference removal via histogram analysis in time-frequency domain. IEEE Trans. Aerosp. Electron. Syst. 52(2), 755–768 (2016)

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9. Wu, J.-D., Hsu, C.-C., Wu, G.-Z.: Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference. Expert Syst. Appl. 36(3), 6244–6255 (2009) 10. Theodoridis, S., Koutroumbas, K.: Pattern Recogn. Academic, New York (1998) 11. Grabill, P., Seale, J., Wroblewski, D., Brotherton, T.: iTEDS: the intelligent turbine engine diagnostic system. In: Proceedings of 48 International Instrumentation Symposium, May 2002 12. Habib, M., Hassan, M., Abul Seoud, R., Bayoumi, A.: Mechanical fault detection and classification using pattern recognition based on bispectrum algorithm. In: Lecture Notes in Networks and Systems, vol. 4, pp. 147–165 (2016) 13. Hassan, M., Tarbutton, J., Bayoumi, A., Shin, Y.: Condition monitoring of helicopter drive shafts using quadratic-nonlinearity metric based on cross-bispectrum. IEEE Trans. Aerosp. Electron. Syst. 50(4), 2819–2829 (2014) 14. Hassan, M., Bayoumi, A., Shin, Y.: Quadratic-nonlinearity index based on bicoherence and its application in condition monitoring of drive-train components. IEEE Trans Instrum. Measure. 63(3), 719–728 (2014) 15. Newland, D.: An Introduction to Random Vibrations, Spectral & Wavelet Analysis, 1st edn. Dover Publications, Mineola (2005) 16. Shukla, K., Tiwari, A.: Efficient Algorithms for Discrete Wavelet Transform, 1st edn. (2013) 17. Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Sig. Process. 96, 1–15 (2014) 18. Akansu, A., Haddad, R.: Multiresolution Signal Decomposition, 1st edn. Academic Press, San Diego (2001) 19. Strang, G., Nguyen, T.: Wavelets and Filter Banks. Wellesley-Cambridge, Cambridge (1996) 20. Goumas, S., Zervakis, M., Stavrakakis, G.: Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction. IEEE Trans Instrum. Measure. 51(3), 497–508 (2002) 21. Yen, G., Lin, K.-C.: Wavelet packet feature extraction for vibration monitoring. In: Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328), June 2000 22. Proakis, J.G., Manolakis, D.G.: Power spectrum estimation. In: Digital Signal Proccessing: Principles, Algorithms, and Applications, 4th edn. pp. 960–1040. Prentice-Hall, Englewood Cliffs (2007) 23. Czogala, E., Lęski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Physica-Verlag, Heidelberg (2000) 24. Harris, J.: Fuzzy Logic Applications in Engineering Science, 1st edn. Springer, Dordrecht (2006) 25. Murty, M., Devi, V.: Pattern Recognition, 1st edn. Springer, London (2011) 26. Rutkowska, D.: Neuro-Fuzzy Architectures and Hybrid Learning, 1st edn. Physica-Verlag, Heidelberg (2002) 27. Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)

Chapter 38

Condition Monitoring of Gravel Roads–Current Methods and Future Directions Mirka Kans , Jaime Campos , and Lars Håkansson

Abstract High quality information is essential for the efficient decision making. Road condition is, together with traffic frequency, commonly used as input for classifying road status. Road condition is also a major input for the maintenance executor (vehicle operator) when deciding on maintenance actions, such as amount of gravel to replenish. With better condition related information, the maintenance execution as well as maintenance planning could be greatly improved. The current deficiencies in gravel road condition monitoring methods and technologies are described in this paper, and an approach for objective measurement of gravel road condition is proposed, including new measurement techniques and systems as well as information processing for decision making. This is realized by utilizing research findings from the area of signal processing, maintenance engineering, and information systems engineering in the specific context of gravel road monitoring, thus utilizing available technologies and methods as well as physics.

38.1

Introduction

Gravel roads comprise a substantial part of the Swedish road network. The state-owned gravel roads comprise 18 400 km, i.e. about 20% of the total road network. In addition to the state owned gravel roads, about 74 000 km of private gravel roads exist, and a large number of forest roads (about 210 000 km) [1]. These constitute the last branch of the road network and have an important business related as well as social function for sparsely populated areas: without them, it is difficult to conduct business and live in the countryside. It is therefore important M. Kans (&)
J. Campos
L. Håkansson Linnaeus University, 351 95 Växjö, Sweden e-mail: [email protected] J. Campos e-mail: [email protected] L. Håkansson e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_38

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that they be kept in good condition. Maintenance planning and execution are dependent on relevant and robust high quality data. For gravel road maintenance, the traffic frequency, environmental conditions as well as road condition are important factors to take into consideration [2–4].Typical parameters for describing road condition are rutting, longitudinal surface roughness and surface friction [2]. Neglecting the structural road condition will eventually result in permanent deformation and thus affect the functional condition of the road [5]. The structural condition is measured with parameters reflecting the bearing capacity, for instance as gravel loss of the wearing surface [6]. The digital development today allows the use of sensors and real-time data management for the collection of condition data, but there are several context dependent challenges that have to be addressed for gravel road maintenance, such as: 1) What type of data provides the basis for relevant information and how can we collect this data objectively and reliably? This problem requires, in addition to understanding of how roads condition can be estimated, knowledge of the types of sensors that are applicable, and their applicability in tough environments. 2) What kind of information is needed in order to make good decisions about gravel road maintenance? This requires understanding of specific conditions when planning gravel road, and maintenance strategies that can be used to create an optimal maintenance plan. There is also a need for an understanding of how condition data and other information can be used to optimize maintenance, as well as understanding how environmental-related effects can be measured. 3) How to design a system that supports efficient gravel road maintenance? This requires understanding of technology solutions that support data collection, signal processing, information management and decision making and how these can interact in a system solution. In the project Sustainable maintenance of gravel roads local actors within the gravel road ecosystem collaborate to face the challenges, and develop new knowledge and innovations for gravel road maintenance, using a participatory design approach for the development process. In this paper, current methods for condition monitoring and assessment of gravel roads are reviewed and a novel approach for gravel road condition monitoring is proposed, i.e. manly focusing on question number one as stated above.

38.2

Current Methods for Gravel Road Classification and Condition Monitoring

A gravel road mainly constitutes of a sub base layer and a surface layer. The sub base is the load bearing layer, while the surface layer is the wearing course of the road, see Fig. 38.1. The road is crowned in order to create a cross fall for drainage purposes: the water falls towards the sides and into the ditches. The edge is somewhat higher than the surface but lower than the crown.

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Fig. 38.1 Cross section of a gravel road

Road classification is done using parameters reflecting in fist hand the functional condition of the road. In Sweden, a four-level classification model is used, comprising four road condition parameters [7]: • • • •

Cross fall and road edges Irregularities Loose aggregate Dust

Cross fall and road edges are assessed using objective measures, and threshold limits for each of the four condition classes have been defined. The other parameters are visually assessed, and no quantitative limits are therefore defined. If a road is deemed as having poor condition it has to be corrected within a certain period of time [2]. Other road classification systems use subjective assessment all together, such as the Finnish five-level system. The condition of the gravel roads is in the Finnish system described on the basis of the service level and the frost damage. The service level describes the gravel road surface condition, and is evaluated on a scale of 1 to 5 (best) for each sub factor: road surface smoothness, density and dustiness. For the prioritization of measures and requirements at the management level, gravel roads have been divided into three different classes on the basis of, among other things, traffic volume, road importance and land use. The higher the class the road belongs to, the more stringent the requirements are [8]. The Finnish system takes into account the traffic load when classifying the road network, unlike the Swedish classification system, where condition and time to maintenance action are used without regarding traffic volume [2]. The condition of gravel roads may be measured objectively with the aid of a number of methods [9–11]. Today, objective measurements for the cross fall and the road edge depth are made by an assessor with the aid of e.g. a cross fall meter and a yardstick. The cross fall meter may for instance be a 2 m long folding straightedge with a fixed digital meter measuring the cross fall directly in percent [9, 10]. A so-called Laser-RST (Road Surface Tester) may also be used to make objective road surface measurements on gravel roads. The Laser-RST measure road surface roughness using distance measuring laser sensors mounted along the front of a car, orthogonal to the driving direction, and in parallel to the road surface in combination with computer technology [11]. Measurements of longitudinal and

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transverse irregularities such as rut depth and IRI (International Roughness Index), cross fall, hilliness and horizontal curvature are carried out. In addition to this, the longitudinal profile of the road is recorded in both wheel ruts and a number of other measures of the surface roughness are calculated [11]. The scanning-laser technology LIDAR (Light Detection And Ranging) enables measurement of the road condition with increased resolution in the transverse and longitudinal directions, combined with better accuracy compared to point-laser systems [12]. Gravel road surface roughness may also be measured in a car, when driving, using a smartphone with built-in vibration sensor (accelerometer) and GPS positioning [11, 13]. However, the measured acceleration will be a result of road irregularities, the speed at which you travel, where and how the accelerometer is placed and the car’s dynamic properties (tires and suspension) [11]. To access gravel roads condition under the road surface ground penetration radar (GPR) may be utilized [14]. Information concerning road structure such as layer thicknesses and the total thickness of the road structure may be obtained [14]. A road roughness index commonly used for evaluating and managing road systems is the International Roughness Index (IRI) [15–18]. It is generally produced based the accumulated velocity response of a dynamic model of a quarter-car vehicle exerted by measured longitudinal road profiles filtered by a tire moving average filter [15]. This results in a roughness index with units of slope (in/mi, m/km, mm/m, etc.) [15]. Longitudinal road profiles are produced by measuring and recording the road surface height in the longitudinal directions of roads, a longitudinal road profile for the gravel road shown in Fig. 38.2 may e.g. be produced by measuring and recording the road surface height along the longitudinal red line. In Fig. 38.3 the quarter-car model, a two-degrees-of-freedom system, is illustrated.

Fig. 38.2 Sketch of a gravel road

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Fig. 38.3 The quarter-car model

Here ms is the sprung mass, a quarter of the vehicle body supported by one wheel, ks is the vehicle suspension spring constant, cs is the vehicle suspension damping, mu is the unsprung mass of the wheel, tire, half the axle, etc. and kt is the tire spring constant, tire stiffness [15] and their values may be found in [15, 16]. Furthermore, zs(t) is the vertical displacement of the sprung mass, zu(t) is the vertical displacement of the unsprung mass and zsurf(x,y) is the road surface height as a function of the x- and y-coordinates. Assuming that the tire diameter is infinitely small the equations of motion of the quarter-car model may be written in terms of vectors and matrices, according to:


ms 0 ¼

0 mu 

( d 2 zs ðtÞ ) dt2 d 2 zu ðtÞ dt2

0 kt zsurf ðt; yÞ






cs þ cs

cs cs

( dzs ðtÞ ) dt dzu ðtÞ dt


þ

ks ks

ks kt þ ks



zs ðt Þ zu ðt Þ



ð38:1Þ Here the fact that the quarter-car travel at a constant speed over the road surface at v = 22.222 m/s (80 km/h), is utilized as zsurf(x,y) = zsurf(vt,y) = zsurf(t,y). In compact vector and matrix form the equations of motion may be expressed as ½M f€zðtÞg þ ½C fz_ ðtÞg þ ½K fzðtÞg ¼ ff ðtÞg

ð38:2Þ

Here [M] is the 2  2 mass matrix, [C] is the 2  2 damping matrix, [K] is the 2  2 stiffness matrix, {z(t)} is the 2  1 displacement vector and {f(t)} is the 2  1 force vector. The equations of motion of the quarter-car model may be written in terms of state-space form. The state vector may be defined as

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 fyðtÞg ¼

zðt Þ z_ ðtÞ

 ð38:3Þ

This yields the equations
fy_ ðtÞg þ

½02x2 ½M 1 ½K 

   ½I 2x2 0 y ð t Þ ¼ f g ½M 1 ff ðtÞg ½M 1 ½C

ð38:4Þ

, 

f0g2x1 fy_ ðtÞg þ ½ AfyðtÞg ¼ ½B ff ð t Þ g

 ð38:5Þ

To calculate the responses’ of the quarter-car model in the discrete time domain because of the force exerted on the quarter-car by the longitudinal road profile, the solution to these equations is sampled [15, 18]. In the discrete time domain the responses’ of the quarter-car model may be produced as [15, 18] 

fy ð n þ 1Þ g ¼ e

ATs



fyðnÞg þ ½ A

1 



I e

ATs





f0g2x1 ½B ff ð nÞ g

 ð38:6Þ

Here Ts is the sampling time interval, n is the discrete time (t = n Ts) and it is assumed that the excitation force {f(n)} = {0 kt zsurf(n,y)}T, T is matrix transpose, is constant between the sampling instances and that road surface height zsurf(t,y) have no energy above half the sampling frequency Fs = 1/ Ts. Now the assumption that the tire diameter is infinitely small is removed by introducing a tire moving average filter, defined by 

P þ k1 zsurf ðn; yÞ ¼ 1k nj¼n zsurf ðn; yÞ k ¼ max½1; nintðLB =Xs Þ

ð38:7Þ

where zsurf ðn; yÞ is the average road profile height, nint is the nearest integer, Xs (Xs = vTs) is the interval in mm that the road profile height has been sampled with and LB is the moving average base length in mm [15]. Hence, the discrete time domain the responses’ of the quarter-car model is now given by:        f0g fyðn þ 1Þg ¼ eATs fyðnÞg þ ½ A1 I  eATs ½B  2x1 f ð nÞ

ð38:8Þ

 T Here f ðnÞ ¼ f 0 ktzsurf ðn; yÞ g is the excitation force of the quarter-car model. The International Roughness Index (IRI) for a road profile having the length L = NXs may now be produced as [15]

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L

1 Zv 1 ZL jz_ s ðtÞ  z_ u ðtÞjdt ¼ jz_ s ð xÞ  z_ u ð xÞjdx L0 vL 0 1 XN1  jz_ ðnÞ  z_ u ðnÞj½mm=m n¼0 s vN

IRI ¼

ð38:9Þ

Here N is the number of samples the longitudinal road profile time series contain. Basically, the IRI is affected by road surface roughness’s having wave lengths in the interval 1.33 to 30 m [18] and thus the quarter-car model realize a band-pass filter [15, 18].

38.3

A Novel Approach for Gravel Road Condition Monitoring

From Sect. 38.2 it follows that condition assessment of gravel roads is mainly done using subjective measures, or with manual means. Objective and automatic condition monitoring technologies based on distance measuring laser sensors have been developed and are currently used on paved roads but may also be used on gravel roads [11]. However, it is associated with several shortcomings. First, the monitoring is mainly done using special measurement vehicles, secondly, measurements of gravel roads condition must be carried out more frequent since the condition of gravel roads tend to change substantially faster as compared to paved roads [11], thirdly, such measurements are expensive [13] and fourthly, the risk of damaging the measuring equipment plus the difficult measuring conditions with dust and water on the surface that may cause erroneous readings by the laser [19].

38.3.1 New Objective Measurement Approach We suggest a new approach for objective measurement of gravel road condition which is less sensitive for the rough condition on gravel roads, the driving speed and the dynamic properties of the vehicle as well as enables measurements of longitudinal and transverse irregularities. The new approach is based on a three-meter aluminum beam with radar distance measuring sensors equidistantly mounted along the beam in the longitudinal direction, two accelerometers mounted at each end of the beam to measure the vertical vibration and a gyro Inclinometer to measure the inclination of the beam. The new approach for objective measurement of gravel road condition is illustrated in Fig. 38.4.

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Fig. 38.4 New approach for objective measurement of gravel road condition

The surface roughness is measured in parallel by several radar distance measuring sensors and this information is converted to estimates of the longitudinal road profiles with the aid of the information provided by the two accelerometers that measure the vertical beam vibration. Based on the information provided by the gyro Inclinometer the cross fall and road edge depth may be estimated. This approach also comes with a challenge since radar distance measuring sensors samples the height of the longitudinal road profile with a constant time interval, the sampling time interval, without any anti-aliasing filtering. This require that the frequency content of the longitudinal road profile does not exceed half the sampling frequency at the speed the vehicle is driven at, or in terms of the wavelength of the road regularities, they must be greater than twice the distance between the samples of the height of the longitudinal road profile. If a radar sensor is mounted at approx. 0.2 m from the road surface, the maximal theoretical sampling frequency of the road surface is 850 Hz and the maximum measurement bandwidth without aliasing distortion will be 425 Hz. For road roughness index, such as the IRI, it is required that the longitudinal road profile data used to calculate it with are without or have negligible aliasing distortion. For this reason, the feasibility of using radar distance measuring sensors to measure longitudinal road profile with respect to aliasing distortion will be investigated. If aliasing distortion is an issue, a mechanical anti-aliasing filter solution may be considered.

38.3.2 Common Database The collected data will serve as essential input for a new Information and Communication System (ICT) for gravel road maintenance. In the development of the ICT system, the intention is to follow standards such as OSA-CBM and MIMOSA CRIS [20], for purposes of integration, modularity and interoperability because of the layered architecture and utilization of the Unified Modeling

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Language (UML) approach. OSA-CBM consists of seven layers: 1) Data Acquisition, 2) Data Manipulation, 3) State Detection (Condition Monitoring), 4) Health Assessment, 5) Prognostics, 6) Advisory Generation (Decision Support), and 7) Human Interface. Layers 1–3 concern the capture and processing of condition monitoring data, while layers 4–6 concern the utilization of condition monitoring data for decision-making purposes, such as health assessment, projection on the future health of the machine or its remaining useful life (RUL), and recommended actions. With Internet and LAN (local area network), distributed software architectures can be used for CBM. Such architectures are cost-effective and highly supported by the above-mentioned standards. Regarding MIMOSA CRIS, the objective was to encourage exchange of information among the plants on machine maintenance, to come up with publications and develop open international agreements (standards) on machine maintenance information systems MIMOSA developed a Common Relational Information Schema (CRIS), a relational database model for different data types that needs to be processed in CBM application, see example in Fig. 38.5. The system interfaces have been defined according to the database schema based on CRIS. The interfaces’ definitions developed by MIMOSA are an open data exchange convention to use for data sharing in today’s CBM systems. The current prototype system is aimed to follow the relevant OSA-CBM layers as well as database classes in its application. The appropriate layers are the Data acquisition, the Data manipulation, the Condition Monitoring, as well as the Health Assessment layer. Further developments would include additional data capture in order to support Advisory Generation.

Fig. 38.5 Measurement event database classes (MIMOSA CRIS)

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Todays’ methods for measuring and quantifying the condition of gravel roads are rough both in terms of method and technology. Subjective measures are today mainly utilized for the quantification of different road conditions, and the current suggested smartphone based automated methods do not capture the actual surface topology. The acquired information concerning the surface topology is thus greatly reduced. Furthermore, the repeatability of these methods is problematic; basically it requires that repeated measurements are carried out with the same equipment, speed and along the same track on the gravel road. Measurements of the condition of gravel roads will generally concern dynamic quantities, such as acceleration, distance and inclination. To acquire information concerning the actual surface topology of gravel roads the distance between the road surface and a reference level may be measured at a sufficient number of equidistant positions along the transversal direction over the road and at a suitable speed in the longitudinal direction of the road. Here, the acquired resolution of the gravel road topography in both the transversal and longitudinal directions will depend on the spatial and temporal sampling of the distance between the reference level and the road surface. Thus, depending on the requirements on the resolution of the measured surface topology the number of distance measuring sensors along the transversal direction and the sampling frequency and/or the speed of the vehicle carrying the measurement system are selected. Our approach suggests the use of an aluminum beam equipped with accelerometers and a gyro Inclinometer to measure the vertical vibration and the inclination of the beam. A set of radar sensors are mounted on the beam for measuring the road surface roughness. The condition monitoring system is robust enough to be attached to any heavy vehicle, for instance a grader. Data regarding the road condition (especially the functional condition) is required as a basis for creating a relevant database for gravel road maintenance. The novel approach promotes objective and robust data capture that results in high quality and rich information regarding road surface conditions. The data is processed and stored in a common database based on the OSA-CBM and MIMOSA standards, which allows for optimized maintenance planning and decision-making. The database should further be extended with data regarding other functional road condition data, such as dustiness and loose aggregate, but also other types of data required for reaching efficiency in the maintenance. Acknowledgements The research has been conducted as part of the project named Sustainable maintenance of gravel roads funded by the Kamprad Family Foundation. The project develops new methods and technologies for gravel road maintenance.

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References 1. Swedish Transport Agency (2019). Sveriges vägnät. https://www.trafikverket.se/resa-ochtrafik/vag/Sveriges-vagnat/. Accessed 02 May 2019 2. Alzubaidi, H.: Operations and maintenance of gravel roads. A Literature study. VTI meddelande 852a. Swedish National Road and Transport Research Institute, Linköping (1999) 3. Forsyth, A.R., Bubb, K.A., Cox, M.E.: Runoff, sediment loss and water quality from forest roads in a southeast Queensland coastal plain Pinus plantation. Forest Ecol. Manage. 221, 194–206 (2006) 4. Kuttah, D.: The performance of a trial gravel road under accelerated pavement testing transportation. Geotechnics 9, 161–174 (2016) 5. Saarenketo, T.: Monitoring Low Volume Roads. Roadex III report (2006). www.roadex.com 6. Alferor, R.M., McNiel, S.: Method for determining optimal blading frequency of unpaved roads. Transp. Res. Rec. 1252, 21–32 (2017) 7. Swedish Transport Agency: Bedömning av grusväglag. TDOK 2014:0135 Version 1.0 (2014) 8. Vägförvaltningen: Riktlinjer för drift och underhåll av grusvägar Verksamhets- och plandokument. Vägförvaltningen, Helsingfors (2008) 9. Alzubaidi, H.: Metodbeskrivning 106:2005 Bedömning av grusväglag, Vägverket publikation 2005:60, Vägverket, Borlänge (2005) 10. Lundberg, K., Lundberg, T. Sjögren, L.: Objektiv mätmetod för tillståndsbedömning av grusväglag, VTI rapport 863, Swedish National Road and Transport Research Institute, Linköping (2015) 11. Lundberg, T., Andrén, P., Wahlman, T., Eriksson, O., Sjögren, L., Ekdahl, P.: Ny teknik för vägytemätning Tvärprofil och spårdjup, VTI rapport 8961, Swedish National Road and Transport Research Institute, Linköping (2018) 12. Bäckström, A.: Smartphones för att mäta kvalitet på grusvägar (2017). http://vpp.sbuf.se/ Public/Documents/ProjectDocuments/884f10d1-2e09-4120-b78e-66e9e5b1608c/FinalReport/ SBUF%2013265%20Slutrapport%20Smartphones%20f%C3%B6r%20att%20m%C3%A4ta %20kvalitet%20p%C3%A5%20grusv%C3%A4gar.pdf 13. Christoffersson, P., Johansson, S.: Rehabilitation of the Timberland Forest Road. - Condition Survey, Design Proposals, Construction and Quality Control, The ROADEX “Implementing Accessibility” Project (2012). https://www.roadex.org/wp-content/uploads/2014/01/ Rehabilitation-of-the-Timmerleden-Forest-Road-design-proposals-2011.pdf 14. Sayers, M.W., Karamihas, S.M.: Little Book of Profiling. University of Michigan Transportation Research Institute (1998). http://www.umtri.umich.edu/content/LittleBook 98R.pdf 15. Lundberg, T., Sjögren, L., Andrén P.: Swedish road condition indicators; past, present and future. Part 3: Future –year 2010 and onwards, VTI rapport 719, (2015). http://vti.diva-portal. org/smash/get/diva2:848534/FULLTEXT01.pdf 16. Loprencipe, G.: Zoccali, P: Ride quality due to road surface irregularities: comparison of different methods applied on a set of real road profiles. Coatings 7(5), 59 (2017) 17. Karamihas, S.M., Gillespie, T.D., Perera, R.W., Kohn, S.D.: Guidelines for longitudinal pavement profile measurement. National Cooperative Highway Research Program Report 434, Transportation Research Board (1999). http://onlinepubs.trb.org/Onlinepubs/nchrp/ nchrp_rpt_434.pdf 18. Astrom, J.K., Wittenmark, B.: Computer-Controlled Systems: Theory and Design, 3rd edn. Prentice Hall, Upper Saddle River (1996) 19. Sjögren, L.: RST-mätningar på grusväg, uppföljning av vägytans kvalitet, VTI notat Nr 9, Swedish National Road and Transport Research Institute, Linköping (1998) 20. MIMOSA, MIMOSA OSA-CBM. http://www.mimosa.org/mimosa-osa-cbm/. Accessed 03 May 2019

Chapter 39

A Kind of Faults Knowledge Discovery Pattern by Means of Rough Set Theory Rongzhen Zhao, Yaochun Wu, and Tianjing He

Abstract The knowledge acquisition in expert system used to faults identification of mechanical device is very difficult. The puzzle has become the critical obstacle in the developing progress of machinery information technology. So based on the classification concepts of Rough Set Theory (RST) and the thinking of big data, the way to develop the technology along the data driven way was explored in this paper. By the concepts of classification contained in RST, the data operation scheme on knowledge conversions was present. It shows that there are two kinds of relations in the process of knowledge conversions, i.e., the knowledge equivalent relation and the knowledge inclusion relation. They indicate that to realize the knowledge acquisition is a systematized engineering project according to big data idea and by data-driven methods. Yet the primary task is to build a suitable data structure model and to use it accumulate the original knowledge of faults diagnosis with the specialized data mode. After obtaining the huge amounts of data contained the original decision knowledge, the other jobs includes that looking for all kinds of algorithms reduces the data size and achieves eventually the knowledge discovery. To solve well the faults knowledge acquisition in by the data driven of big data technology, the conclusion is that to establish a kind of valuable data structure model store the original faults decision knowledge from factory site is a most critical procedure. To carry out the data classification and clustering analysis using some intelligent tools like RST, it is a basic requirement to build a special data structure model for specific domain objects.

R. Zhao (&)
Y. Wu
T. He School of Mechanical and Electronical Engineering, Lanzhou University of Technology, Lanzhou 730050, China e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_39

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The faults diagnosis technology of mechanical device is one of the important elements for developing the intelligent machine [1–6]. For the information technology in industry, to have the reliable knowledge rules of decision-making faults is the decisive factor of identifying the failure of mechanical device. Unfortunately, the knowledge acquisition has been the bottleneck in the field of mechanical fault diagnosis [7]. One of the reasons for this disadvantage is because human experts are very scarce, and the expert’s knowledge cannot be inherited by other engineers. Secondly, because the faults knowledge rules from various experts lack of unified constraints standard, the defect makes the knowledge representation of experts have the considerable uncertainty. So to explore the new implementation way of knowledge acquisition has become very pressing needs to develop scientifically the machinery faults intelligent diagnosis technology [7, 8]. For cracking the puzzle, the big data [9–11] was triggered accompanied the data mining and the knowledge discovery in recent decade seems to guide the development of it. However, the application of big data technology must solve well the data classification to the obtaining huge amounts of data resources. To these key procedures of data classification, RST created by Poland’s famous mathematician Pawlak [12, 13] might have the potential enormous power [14, 15]. And the Granular Computing [16, 17] derived from RST may be able to establish a kind of inevitable connection between data classification and knowledge operation by data driven way [18, 19]. This is because the Granular Computing focuses on the data representation of a granule, and the transformation between the different granular layers, as well as on the interdependence on one knowledge space [20, 21]. The target of Granular Computing is looking forward to get the precise knowledge representation [22]. A series of [7–24] have shown out the following perspective. It is that some necessary relationships should exist objectively between RST and the Granular Computing as well as the big data. But how to find the relationship and how to realize the machine intelligence decision through the data-driven pathway, this is considered widely as a challenging research task [9–11]. Therefore, the research along this direction has a profound significance now. We had found some correlations within RST, the Granular Computing and the big data technology [7, 8]. So in this paper, to solve the puzzle of fault knowledge acquisition according to these three mathematical principles by data-driven approach will be explored. The other sections were organized as follows. In Sect. 39.2, both the general knowledge rules in expert’s system and the some conceptions contained in the three mathematical principles were described briefly. Then, a conversion model in the knowledge base with symbolic data was designed out. After the discussion in Sect. 39.3, a kind of framework implemented the faults knowledge discovery by data-driven way in Sect. 39.4 were put forward. At the end of this paper, we shown the obtained conclusions and gave a few of suggestions for further study.

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The Some Principles

39.2.1 The Knowledge Rules The expert systems are to make use of knowledge rules implement the reasoning and decision-making. In mechanical faults diagnosis domain, they have been trying to realize the automatic faults identification of mechanical device in plant by machine intelligence in recent decades. Now yearning goal is to let a class of machine intelligence achieve the high level of excellent human experts. However, a lot of knowledge rules embedded in an expert system is very uncertain and unreliable because of the extremely difficulty of knowledge acquisition from the domain experts [7]. The recent research in artificial intelligence is trying to improve the defect. In expert system, the expert’s knowledge is often described in (1), i.e. IF Conditions THEN Decision

ð39:1Þ

where “Conditions” denote the phenomenon in operational process of a kind of special device, and “Decision” must be the decisions result made by experts or by artificial intelligence according to the phenomenon. Obviously in here, the former should be as simple as possible because the mode is the same as the human expert’s knowledge structure. In fact, using the artificial intelligence to implement the faults diagnosis, the most difficult thing is how to find the best “Conditions” for the experts in plant site. Apparently, the simpler it is, the easier it is for experts to explain the malfunction. So, to look for the simple description with respect to fault’s status is a central theme. Unfortunately, this is also very hard in the development of artificial intelligence technology of mechanical device [1–4].

39.2.2 The Concepts of Data Classification in RST RST created by Poland mathematician Pawlak in 1982 is a kind of intelligent data analysis tool [14]. It is known as to specialize in uncertainty reasoning and decision making now [12, 13]. The some conceptions included in the theory can be showed in Fig. 39.1. The symbols are defined as follows in Fig. 39.1. U denotes a universe of discourse and U/R represents a sort of grid result obtained to partition U with a special ruler R; X is a set of objects waiting for to make out the making-decision, it is that each object in X should belong to which small grid. For the objects located in the envelope area with two plotline between the upper approximate R*(X) and the lower approximate R*(X), the reasoning and decision may be inaccurate. And from another view points, because the former is an approximate correct decision-making result subset, R*(X) = POS(X) denotes a correct decision-making result set. On the contrary, NEG(X) is a negative area. In here, if U is a knowledge base, the obtained

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A1

R

U R X R*(X) =POS(X) R*(X) NEG(X)

A2 Fig. 39.1 The basic concepts contained in RST

grid should be equivalent to a kind of knowledge granule, in which each sub-box in the grid represents a knowledge particle that is a knowledge rule. For the goal of knowledge discovery, we only focus on the two concepts of U and the grids obtained by U/R in this paper. At this point, a sample designed by us can be found in [8]. The classification result and the knowledge rules obtained according to the indiscernibility relation in RST are as follows in Table 39.1 and its follow-up content, respectively. According to Table 39.1, a series of knowledge rules we can get are as follows: Rule1. Rule2. Rule3. Rule4. Rule5.

IF IF IF IF IF

(a1, (a1, (a1, (a1, (a1,

2) 3) 1) 2) 1)

and and and and and

Table 39.1 R knowledge particles obtained to use d partition U

(a2, (a2, (a2, (a2, (a2,

1) 2) 1) 2) 3)

and and and and and

U/R

X1 X2 X3 X4 X5

(a3, (a3, (a3, (a3, (a3,

3) 1) 4) 1) 2)

THEN THEN THEN THEN THEN

(d, (d, (d, (d, (d,

1) 2) 3) 1) 1)

R knowledge particles

Attribute set A Condition attribute set C a2 a3 a1

Decision attribute D d

{x1, x3, x4, x9} {x2, x7, x10} {x5, x6, x8} {x11} {x12}

2 3 1 2 1

1 2 3 1 1

1 2 1 2 3

3 1 4 1 2

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Apparently, the five rules were a kind of scientific expression of five knowledge particles corresponding with (1). They are completely consistent with the concept of classification included in RST [12, 13].

39.2.3 The Granular Computing The Granular Computing is an advanced stage of RST and its principle originated in the grids of RST in Fig. 39.1 on some extent [16]. Now it is known as a kind of methodology that is able to imitate human thinking mode [18]. The principle considers human knowledge into a three-dimensional space. It assumed that human knowledge was described in granular and the size of granular can be enlarged or decomposed into smaller particles [17]. Hence using a knowledge space to describe the structure of human knowledge should be feasible, but it results in a series of new problems. They include that how to build a knowledge particles and how to design the structure describing human knowledge in a knowledge space, as well as what are the relations in different levels of a knowledge space and how to describe the relevant concepts with mathematical formulas and so on [16, 17]. At present, the consensus is that the knowledge can be amplified and decomposed and the rest all are very challenging tasks. The Granular Computing is a new concept and computing paradigm in the current intelligent information processing field [9, 19]. It is built based on the multi-levels structure of thinking, a complex problem solving and information processing model. On the above basis of, now the some concepts with respect to granular computing have been related to the methodology of human cognitive level.

39.2.4 The Big Data Technology The technology from data mining and knowledge discovery in database is a new area that attracts the most attention of researchers [9–11]. It began to emerge in facing the disaster of huge amounts data and the low level of artificial intelligence in various applications for a long time, as well as the urgent need for automatic demands in engineering applications. In order to achieve the purpose of using machine intelligence to implement the automatic decision-making, to realize the artificial intelligence from huge amounts data resource has been considered to be the fourth paradigm of scientific discovery [9]. The significance contained in the paradigm can be shown in Fig. 39.2. We can draw the conclusion form the figure. It is that there are objectively some transitive relations between original data and knowledge as well as wisdom. But the data is basic factors and wisdom is the desired goal, and to establish the suitable relations to implement some transformation between the data and the knowledge as well as the wisdom is the important procedures. In this process, the most important thing is how to establish the correct relationships with math mode.

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The Amplified Knowledge Granularity

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Wisdom

Meta Knowledge

Belief

Knowledge

Information

Data The Attributes Noise

Fig. 39.2 A kind of transformational relation between data and knowledge

We found in previous studies that there are the natural relations between RST and the Granular Computing, but the Granular Computing still waits to be defined clearly [7, 8]. The effects hided in Fig. 39.2 seem to can unify the three together. In the figure, a transformation relationship from the data contained noise to knowledge and wisdom are shown out implicitly. So, to fusion the three into one and solve the puzzle of knowledge acquisitions will be discussed below.

39.3

The Model Designed for Faults Knowledge Discovery

According to the conceptions of RST included in Fig. 39.1 and the transformation reflected out in Fig. 39.2, a knowledge space established through the combination of both was shown as in Fig. 39.3. The special space consists of more than 4 layers at least and its A1RA2 is self same with Fig. 39.1. The layer G located above A1RA2 is the result obtained to use the knowledge G divide the same U. Distinctly, the granule’s number in G is less and each piece is relatively larger than in R. To expand the mapping relation to other layers such as G and E, a significative conclusion can be extracted from the relation between G and E. It is that in a knowledge space, a kind of knowledge inclusion relation between two adjacent layers should exist objectively. It can be inferred that there are a number of tiny knowledge points in the layer W located under R, which must be a kind of data resource contained the most primitive of knowledge, such as the original faults knowledge from plants site. If you infer from this point, the nature of faults knowledge discovery in machinery engineering is that the small original knowledge points are gathered into the knowledge particles.

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Fig. 39.3 The correlations between the data and knowledge

The small knowledge points is amplified into knowledge granulars. The knowledge granular with numerous small knowledge points

The amplified knowledge granularity One knowledge granular is divided into small knowledge points.

Fig. 39.4 The physical meanings of knowledge operation

The knowledge inclusion relation between two adjacent layers also can be extended to the whole knowledge space. We can find that the so-called knowledge discovery is that a number of small knowledge points were transform into the larger knowledge granular. This is consistent with the anticipated target of big data technology. So, a kind of mode on knowledge operation can show as in Fig. 39.4. The tiny knowledge points can be gathered to become the large knowledge granular. But the general pattern results in a series of the challenging tasks in research. Of course, they include the theory and method of data mining and knowledge discovery, and the most fundamental task should be to save the original knowledge with a kind of valuable data structure model into huge amounts of data resources.

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The Engineering Application of Big Data Technology

Big data is known as with 4 V characteristics. They are the Volume, Variety, Velocity and Veracity from IBM. In here, the last emphasizes specially the veracity of big data resource. Apparently, this characteristic is accordant with the conclusion from Sects. 39.2 and 39.3. So by means of combination RST with big data technology, a kind of new way to discover the faults diagnosis knowledge of mechanical device and develop the intelligent machinery is planed out by us. On the premise to set a specified mechanical device types as U, other research tasks should include as follows these: • Step 1: A valuable data structure model should be established forehand to a specific U. • Step 2: Use the model to accumulate the valuable mechanical conditions characteristics and expert’s decisions into big data resource with data mining value. • Step 3: Explore the algorithm on reducing data dimensions and solving data classification for data cleaning. This process will be for narrowing the data scale and makes the faults knowledge discovery become easy. • Step 4: To use the knowledge discovery principle of RST suggested by us achieves the special applications of faults knowledge discovery. • Step 5: Attributes optimization to U is a core task in processing the systematized project. Obviously, the above procedures are a systems engineering project for specific engineering applications. However, to build a specific data structure model is a basic task, and reducing data size is the core task in the middle parts and realizing knowledge acquisition through classification operation is the anxious goal of knowledge discovery research.

39.5

Conclusion

In this paper, the inner relations between RST and Granular Computing as well as the big data technology are discussed. Through the analysis from RST to Granular Computing, a knowledge space was established. And we found out a kind of knowledge inclusion relations implied within the space. The relation shows out that the nature of knowledge discovery is to combine tiny knowledge points into knowledge granular by means of data merging operations. On the contrary, a large knowledge particle can be decomposed into small knowledge particles. This nature triggers out new needs which must build a valuable data structure model to save the original faults knowledge of machinery device. Apparently, the model can just be established after to define out a finite universal U for each class of mechanical device is a prerequisite. Using the model to obtain vast amounts of data resources

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by online accumulating is basic work for faults knowledge discovery. Then to reduce the data size with the aid of data classification or clustering, which reduces the dimensionality of vast amounts data resources, is key and core objective. To realize the application of knowledge discovery in industry is an anxious goal. These are challenging tasks in the research field of mechanical fault diagnosis. Acknowledgements This work is supported by the National Natural Science Foundation of China (Grant No.51675253), the National Key Research and Development Program of China (Grant No.2016YFF0203303) and the Hongliu First-class Disciplines Development Program of Lanzhou University of Technology.

References 1. Saimurugan, M., Ramachandran, K.I., Sugumaran, V., et al.: Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Syst. Appl. 38(4), 3819–3826 (2011) 2. Kim, Y.M.: Data–driven modeling, control, and fault detection of wind turbine systems. Int. J. Syst. Control Inf. Process. 1(3), 298–318 (2014) 3. Bittencourt, A.C., Saarinen, K., Sander-Tavallaey, S., et al.: A data-driven approach to diagnostics of repetitive processes in the distribution domain – applications to gearbox diagnostics in industrial robots and rotating machines. Mechatronics 24(8), 1032–1041 (2014) 4. Donat, W., Choi, K., An, W., et al.: Data visualization, data reduction and classifier fusion for intelligent fault diagnosis in gas turbine engines. Trans. ASME J. Eng. Gas Turb. Power 130 (4), 041602, 1–8 (2008) 5. Di Maio, F., Zio, E.: Failure prognostics by a data-driven similarity-based approach. Int. J. Reliab. Qual. Saf. Eng. 20(1), 1350001, 1–17 (2013) 6. Song, B.L., Lee, J.: Framework of designing an adaptive and multi-regime prognostics and health management for wind turbine reliability and efficiency improvement. Int. J. Adv. Comput. Sci. Appl. 4(2), 142–149 (2013) 7. Rongzhen, Z.: Investigation of Elementary Problems on Diagnosis Knowledge Discovery of a Rotor-Bearings System with Double Spans Based on Theory of Rough Sets. Xi’an, Xi’an Jiaotong University (2006) 8. Rongzhen, Z., Chao, L., Linfeng, D.: Engineering application way of faults knowledge discovery based on rough set theory. In: 9th International Conference on Damage Assessment of Structures (DAMAS2011), Journal of Physics: Conference Series, vol. 305 (2011). http:// doi.org/10.1088/1742–6596/305/1/012018 9. Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1(1), 51–59 (2013) 10. Fan, J., Han, F., Liu, H.: Big data: challenges and applications. Natl. Sci. Rev. 1, 293–314 (2014) 11. Jagadish, H., Gehrke, J., Labrinidis, A., et al.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014) 12. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982) 13. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177, 3–27 (2007) 14. Pawlak, Z.: Rough sets and intelligent data analysis. Inf. Sci. 147, 1–12 (2002) 15. Wang, G.-Y., Yao, Y., Yu, H.: A survey on rough set theory and applications. Chin. J. Comput. 32(7), 1229–1246 (2009)

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16. Liang, J., Qian, Y., Li, D., et al.: Theory and method of granular computing for big data mining. SCIENTIA SINICA Inf. 45, 1355–1369 (2015). http://doi.org/10.1360/n112015– 00092 17. Qing, L.I.U., Tao-Rong, Q.I.U., Lan, L.I.U.: The research of granular computing based on nonstandard analysis. Chin. J. Comput. 38(8), 1618–1627 (2015). (In Chinese) 18. Miao, D., Zhang, Q., Qian, Y., et al.: From human intelligence to machine implementation model - theories and applications based on granular computing. CAAI Trans. Intell. Syst. 12 (1), 1–15 (2016) 19. Ji, X.U., Guo-Yin, W.A.N.G., Hong, Y.U.: Review of big data processing based on granular computing. Chin. J. Comput. 38(8), 1229–1246 (2015). (in Chinese) 20. Eissa, M.M., Elmogy, M., Hashemc, M.: Rough-granular computing knowledge discovery models for medical classification. Egypt. Inf. J. 17(3), 265–272 (2016) 21. Chiaselotti, G., Ciucci, D., Gentile, T.: Simple graphs in granular computing. Inf. Sci. 340– 341, 279–304 (2016) 22. Al-Hmouz, R., Pedrycz, W., Balamash, A.: Description and prediction of time series: a general framework of granular computing. Expert Syst. Appl. 42(10), 4830–4839 (2015) 23. Salehi, S., Selamat, A., Fujita, H.: Systematic mapping study on granular computing. Knowl. Based Syst. 80(May), 78–97 (2015) 24. Gacek, A.: Signal processing and time series description: a perspective of computational intelligence and granular computing. Appl. Soft Comput. 27(February), 590–601 (2015)

Chapter 40

Exploring the Impacts of Using Mobile Collaborative Augmented Reality on the Field Service Business Model of Capital Goods Manufacturing Companies Stefan Ohlig , Dirk Stegelmeyer , Rakesh Mishra , and Maike Müller

Abstract Most manufacturing companies in capital goods industries sell their products on a worldwide scale and must cope with several challenges to provide field services to distant customers. Mobile collaborative augmented reality (MCAR) provides a new way of servicing distant customers by enabling real-time collaboration between on-site technicians and remote experts. Because companies adopting MCAR to facilitate remote services are not yet operational, they are challenged to exploit arising opportunities to create, capture, and monetize customer value. However, research in this area mainly focuses on application development or usability studies. This paper contributes to this field of research by exploring the impacts of using MCAR technology on the field service business model of capital goods manufacturing companies. The two considered use cases are supporting field service personnel or directly supporting the customers’ personnel in carrying out troubleshooting and repairs. Focus group discussions with 19 industry experts have been conducted. The Business Model Canvas was utilized to structure the discussions. Multiple impacts of using MCAR on the field service business model have been identified. For example, the results show that using MCAR technology for remote services can increase uptimes and decrease service deployment costs. OEMs using MCAR can reach and serve new customer segments and might increase the customers’ dependency. Key resources are remote experts and MCAR hard- and software.

S. Ohlig (&)
R. Mishra
M. Müller School of Computing and Engineering, University of Huddersfield, Huddersfield, UK e-mail: [email protected] S. Ohlig
D. Stegelmeyer
M. Müller Institut für Interdisziplinäre Technik, Frankfurt University of Applied Sciences, Frankfurt am Main, Germany © Springer Nature Switzerland AG 2020 A. Ball et al. (eds.), Advances in Asset Management and Condition Monitoring, Smart Innovation, Systems and Technologies 166, https://doi.org/10.1007/978-3-030-57745-2_40

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Introduction

Manufacturing companies in capital goods industries are increasingly transforming their businesses from producing and selling capital goods toward providing services to maintain their installed base [1–3]. The installed bases consist of all capital goods sold to the customers. Because most original equipment manufacturers (OEMs) sell their products on a worldwide scale, they must cope with several challenges to provide field services in distant markets [4, 5]. In general, downtime due to machine breakdown is expensive for the customer, so field service delivery requires a quick response and troubleshooting. Furthermore, the customer is often unwilling to pay for additional expenses due to the long distance, such as travel costs [6]. Hence, a local service delivery infrastructure by means of local service partners capable of responding quickly and without greater travel effort is demanded [5]. However, maintenance processes are often complex and knowledge intensive [7], and highly knowledge-intensive services are usually still performed by the OEMs’ own service technicians. There is a growing interest in using mobile collaborative augmented reality (MCAR) technology to facilitate remote services in a manner that addresses the aforementioned challenges within field service delivery. MCAR systems enable real-time collaboration between remote experts and on-site technicians in two different locations. This kind of knowledge transfer can help field service technicians make repairs more quickly and accurately, as documented by empirical field research [8]. Furthermore, service partners can be enabled to perform more knowledge-intensive services, which reduces time-consuming and expensive travel efforts for the OEM. Although such MCAR systems are commercially available, their operational use is quite rare in practice [9–11]. Most research related to this area lacks an industrial context [12] and is mainly focused on application development [e.g., 13–15] or on usability studies [e.g., 8, 16, 17]. Companies adopting MCAR to facilitate remote services now face challenges to exploit arising opportunities based on this technology to create, capture, and monetize customer value. The choice of a suitable business model influences the way in which such technology is monetized [18]. Therefore, this paper explores the impacts of using MCAR technology on the field service business model of manufacturing companies. In the next chapter, basic information on MCAR is provided. Section 40.3 describes focus group discussion as the research method applied and the process of the data collection. The results of the focus group discussions and derived impacts are outlined in Sect. 40.4. Finally, a brief conclusion and outlook are provided.

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Mobile Collaborative Augmented Reality

Mobile collaborative augmented reality (MCAR) systems enable real-time collaboration between remote experts and on-site technicians in two physically different locations via audio–video streams. MCAR systems can be used for AR-based remote guidance to support on-site technicians during complex technical work [19], such as diagnosis, repair, or maintenance tasks. The MCAR system consists of two components: the MCAR hardware (i.e., the AR device used by the on-site technician) and the MCAR software (i.e., the software enabling the real-time audio–video stream, including AR features, between the on-site technician and the remote expert). The AR device used by the on-site technicians can be either an AR-enabled smartphone or tablet or a head-mounted display (HMD), also referred to as smart glasses. HMDs include the Microsoft HoloLens, the RealWear HMT-1, or the Vuzix M300. The advantage of HMDs over smartphones or tablets is that they can be used hands free. The MCAR software, which is independent of the MCAR hardware, primarily enables the real-time audio–video stream with integrated AR features, such as pointing into the video stream or sharing of 2D annotations or 3D objects. Such software is offered, for example, by PTC, Fieldbit, and Adtance. Although the operational use of MCAR in field service delivery is quite rare, research has identified different use cases [9, 20]. The most common use cases are supporting one’s own field service personnel (84%) or directly supporting the customers’ personnel in carrying out troubleshooting and repairs (42%) [9].

40.3

Research Method

In order to explore the impacts of using MCAR technology for remote services on the field service business model of capital goods manufacturing companies, we conducted focus group discussions. This research method is suitable for exploratory research in which the subject is little known and issues are still unclear [21]. The focus group discussions took place in January of 2019 with a total of 19 industry experts from 12 internationally operating German capital goods manufacturing companies (see Table 40.1), referred to from this point on as original equipment manufacturers (OEMs). Due to our focus on the service business and in order to ensure the validity of the results, participating industry experts were required to hold positions of strategic responsibility in their company’s service department. Because most companies are not yet operational with MCAR systems, only those that had already tested or were currently testing MCAR systems were selected. The 19 participants were divided into four individual focus groups. Groups 1 and 2 discussed the case in which the OEM’s own or a service partner’s field service

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technician is using the AR device to be remotely guided by an OEM’s expert (case A: FIELD SERVICE). Groups 3 and 4 discussed the case in which the customer is using the AR device to be remotely guided by an OEM’s expert (case B: CUSTOMER SUPPORT). To facilitate structured discussions within the focus groups on the impacts on the field service business model, we used the Business Model Canvas (BMC) by Osterwalder and Pigneur [22]. The BMC serves as a visual representation of a firm’s business model and consists of the following nine elements: customer segments (i.e., the different groups of customers a company aims to reach and serve), value proposition (i.e., the products and services that create value for a specific customer segment), sales channels (i.e., the ways in which a company reaches its customer segments), customer relationships (i.e., the types of relationships a company establishes with specific customer segments), revenue streams (i.e., the cash a company generates from each customer segment), key resources (i.e., the most important assets required to deliver value), key activities (i.e., the most important activities required to deliver value), key partnerships (i.e., the network of suppliers and partners that make the business model work), and cost structure (i.e., the costs incurred to deliver the value proposition) [22]. Half of the participants stated that they already knew the BMC. However, in order to ensure a common understanding among all participants, the BMC was thoroughly explained to all participants for 60 min using an illustrative industry example. The participants of each focus group were encouraged to discuss all nine elements of the BMC within their discussion. Initiating questions related to each business model element were used as starting points for the discussions (included in Tables 40.2, 40.3, 40.4, 40.5, 40.6, 40.7, 40.8, 40.9 and 40.10). Before the participants started the focus group discussions, each participant had the opportunity to generate and note their own individual thoughts on the nine elements for 15 min. Afterwards, the four focus group discussions took place simultaneously and without a moderator; only a time observer was present. Each focus group discussion lasted for one hour and was audio recorded. The audio files were transcribed verbatim. Written informed consent was obtained from legally authorized representatives before the study. The resulting transcripts were analyzed based on the qualitative content analysis of Mayring [23]. Using a deductive coding approach, the participants’ statements were coded according to each of the nine elements of the BMC.

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Table 40.1 Focus group participants #

Industry

OEM information (2018)

Position of participant within the company

Focus group

Discussed use case

1

Bagging systems