The intelligent diagnosis and maintenance of the machine mainly includes condition monitoring, fault diagnosis, performance degradation assessment and remaining useful life prediction, which plays an important role in protecting people’s lives and property. In actual engineering scenarios, machine users always hope to use an automatic method to shorten the maintenance cycle and improve the accuracy of fault diagnosis and prognosis. In the past decade, Artificial Intelligence applications have flourished in many different fields, which also provide powerful tools for intelligent diagnosis and maintenance.
This book highlights the latest advances and trends in new generation artificial intelligence-driven techniques, including knowledge-driven deep learning, transfer learning, adversarial learning, complex network, graph neural network and multi-source information fusion, for diagnosis and maintenance of rotating machinery. Its primary focus is on the utilization of advanced artificial intelligence techniques to monitor, diagnose, and perform predictive maintenance of critical structures and machines, such as aero-engine, gas turbines, wind turbines, and machine tools.
The main markets of this book include academic and industrial fields, such as academic institutions, libraries of university, industrial research center. This book is essential reading for faculty members of university, graduate students, and industry professionals in the fields of diagnosis and maintenance.
قائمة المحتويات
Introduction.- Overview of Intelligent Fault Diagnosis and Maintenance for Rotating Machinery.- Deep Learning and Sparse Representation Coupled Intelligent Diagnosis and Maintenance.- Sparse Model-Driven Deep Learning for Weak Fault Diagnosis of Rolling Bearings.- Memory Residual Regression Autoencoder for Bearing Fault Detection.- Transfer Learning-based Intelligent Diagnosis and Maintenance.- Fault Diagnosis of Polytropic Conditions Based on Transfer Learning.- Performance Degradation Assessment Based on Transfer learning for Bearing.- Remaining Useful Life Prediction on .- Transfer Learning for Bearing.- Adversarial Learning-based Intelligent Diagnosis and Maintenance.- Deep Sequence Multi-distribution Adversarial Model for Abnormal Condition Detection in Industry.- Multi-Scale Lightweight Fault Diagnosis Model Based on Adversarial Learning.- Performance Degradation Assessment Based on Adversarial Learning for Bearing.- Graph-structured Information-based Intelligent Diagnosisand Maintenance.- Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis.- Community Clustering Algorithms and Its Application in Machine Fault Diagnosis.- Remaining Life Assessment of Rolling Bearing Based on Graph Neural Network.- Multi-source Information Fusion-based Intelligent Diagnosis and Maintenance.- Intelligent Fault Diagnosis Method Based on Multi-source Data and Multi-Feature Fusion.- D-S Evidence Theory and Its Application for Fault Diagnosis of Machinery.- Conclusion, Challenges, and Future Work.- Conclusion, Challenges, and Future Work.
عن المؤلف
Guangrui Wen received his B.S., M.S., and Ph.D. degrees from the School of Mechanical Engineering, Xi’an Jiaotong University (XJTU), China, in 1998, 2001, and 2006, respectively. From 2008 to 2010, he worked as a Postdoctoral Research Fellow at Xi’an Shaangu Power Co., Ltd., Xi’an. He was a visiting scholar of the University of Liverpool from 2017 to 2018. He has over 20 years of teaching and research experiences at XJTU.
Dr. Guangrui Wen is currently a Full Professor of the School of Mechanical Engineering and the Dean of the School of International Education in XJTU, China. He is also the Vice Dean of the Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, and the Vice Dean of National & Local Joint Engineering Research Center for Equipment Operation Safety Assurance and Intelligent Monitoring, China. Dr. Wen is a member of IEEE, the Chinese Mechanical Engineering Society, the Chinese Society for Vibration Engineering (CSVE), and the Executive Director and Deputy Secretary-General of the Dynamic Test Professional Committee of CSVE.
Dr. Wen has authored two books and over 130 peer-reviewed journal articles and holds more than 20 patents. Some of his work was published in top journals such as Information Fusion, Mechanical Systems and Signal Processing, IEEE Transactions on Industrial Informatics and IEEE Transactions on Industrial Electronics. His research interests include artificial intelligence, mechanical system fault diagnosis and prognosis, mechanical equipment life cycle health monitoring and intelligent maintenance.
Dr. Wen won National Science and Technology Innovation Leading Talents of China in 2022, Young and Middle-aged Scientific and Technological Innovation Leading Talents of Shaanxi Province in 2021, the Third Provincial Science and Technology Award in 2019, the First Provincial Science and Technology Award and the Science & Technology Award for Shaanxi Youth Award in 2015, New Century Excellent Talents in University Award from the Ministry of Education, China, and the Science & Technology Award Achievement Award for Youths from CIME in 2013 and the Second Provincial Science and Technology Award in 2012. Dr. Wen is in charge of a National Science and Technology Major Project of China as the chief scientist from 2020 to 2024.
Zihao Lei received the B.Sc. degree in mechanical engineering from Southwest Jiaotong University, Chengdu, China, in 2018, and the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 2024. From 2022 to 2023, he was a visiting scholar in electrical engineering with the School of Engineering, the University of British Columbia, Canada. He worked at the Intelligent Sensing, Diagnostics, and Prognostics Research Lab of UBC as a research assistant from 2022 to 2023.
His current research focuses on new-generation artificial intelligence-driven diagnosis and maintenance techniques, such as deep learning, transfer learning, adversarial learning, graph neural networks, and information fusion.
He has authored/co-authored more than twenty papers in top journals, including Information Fusion, IEEE Transactions on Industrial Electronics, Mechanical Systems and Signal Processing, and Expert Systems with Applications.He reviews many manuscripts for over ten SCI journals such as Mechanical Systems and Signal Processing, IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Cybernetics, Expert Systems with Applications, ISA Transactions, IEEE Access, and Measurement Science and Technology. He has also participated in some research projects, including the National Key Research and Development Program, the National Science and Technology Major Project, and the National Science Foundation of China, and the Innovation for Defence Excellence and Security (IDEa S) program of Canada.
Xuefeng Chen is a Full Professor and Dean of the School of Mechanical Engineering at XJTU, China, where he received his Ph.D. degree in Mechanical Engineering in 2004. He is the executive director of the Fault Diagnosis Branch in China Mechanical Engineering Society, a member of ASME and IEEE, and the chair of IEEE the Xi’an and Chengdu Joint Section Instrumentation and Measurement Society Chapter.
His fields of interest include fault diagnosis, sparse representation, deep learning, composite structure, aero-engine and wind power equipment. He has authored over 100 SCI publications, 10 of which are Highly Cited Papers in fault diagnosis. He has also published two monographs and two postgraduate textbooks. He won the National Excellent Doctoral Thesis Award in 2007, the First Technological Invention Award of Ministry of Education in 2008, the Second National Technological Invention Award in 2009, the First Provincial Teaching Achievement Award in 2013, and the First Technological Invention Award of Ministry of Education in 2015. He received the National Science Fund for Distinguished Young Scholars in 2012 and the Science & Technology Award for Chinese Youth in 2013. He was in charge of a National Key 973 Research Program of China as the chief scientist in 2015.
Xin Huang received his B.S. and M.S. degrees in Mechanical Engineering from Xinjiang University, Urumqi, China, in 2013 and 2016, and his Ph.D. degree in Mechanical Engineering at the School of Mechanical Engineering at XJTU, Xi’an, China, in 2022. Currently, he works at the SINOPEC Research Institute of Safety Engineering Co., Ltd, Qingdao, China.
His research interests include mechanical signal processing, mechanical fault diagnosis, and prognosis.
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