Intelligent Fault Diagnosis and Health Assessment for Complex Electro-Mechanical Systems

Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.

Presents advanced machine learning paradigms for complex electro-mechanical system fault diagnosis and health assessment Covers a wide range of research directions in intelligent fault diagnosis and health assessment Includes abundant case studies for a better understanding of the deployment of the methods

Auteur

Weihua Li, Senior Member, IEEE, received the Ph.D. degree in mechanical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2003. He is currently a Dean and Professor with the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China. Prof. Li is now serving as the co-chair of Technical Committee (TC-3) on Condition Monitoring & Fault Diagnosis Instrument, IEEE Instrumentation and Measurement Society (IEEE IM Society). He is the director of the Industrial Intelligence Technology Innovation Center of Pazhou Lab, Guangzhou, and the director of the Joint Laboratory of Intelligent Manufacturing between the SCUT and CIMC. He also serves as a member of the Editorial Board of IEEE Trans. on Instrumentation & Measurement (TIM) IEEE Sensors Journal Journal of Dynamics, Monitoring and Diagnostics (JDMD) IET Collaborative Intelligent Manufacturing Chinese Journal of Mechanical Engineering (CJME) and Journal of Vibration Engineering (JVE). His research interests include Industrial intelligence, Industrial Big Data, Digital Twins, Intelligent Maintenance & Health Management and Environment Perception & Path Planning for Intelligent Connected Vehicles. He is the PI (principal investigator) of nearly 20 projects which are funded by National Natural Science Foundation of China, National Key Research and Development Program of China, Key Research and Development Program of Guangdong Province, University-Industry Cooperation, etc. Prof. Li has published over 110 papers in related journals, including IEEE Trans. on Industrial Informatics Instrumentation & Measurement Sensor Journal IEEE/ASME Mechatronics, Renewable Energy, Mechanical System & Signal Processing, Journal of Mechanical Engineering, etc. In addition, he has published 5 books and issued more than 20 Chinese invention patents.

Xiaoli Zhang received the Ph.D. degree in mechanical engineering from the Xi'an Jiaotong University, Xi'an, China, in 2011. She is currently the associate professor with the School of Construction Machinery, Chang'an University, Xi'an, China. Her research interests include machinery intelligent maintenance and condition monitoring, reliability analysis. Dr. Zhang is the member of Fault Diagnosis Committee of Chinese Society of Vibration Engineering. She is the PI of nine projects which are funded by National Natural Science Foundation of China, National Science and Technology Support Program, Young Talents Promotion Program of Shaanxi University Science and Technology Association, etc. Dr. Zhang has published over 30 papers, issued more than 10 Chinese patents, and published 1 book.

Ruqiang Yan received the Ph.D. degree in mechanical engineering from the University of Massachusetts Amherst, Amherst, USA, in 2007. He was a guest researcher at the National Institute of Standards and Technology (NIST) in 2006-2008 and a professor at the Southeast University, China, in 2009-2017. Dr.Yan joined Xi'an Jiaotong University in 2018, and he is an ASME fellow and received Technical Award of the IEEE Instrumentation and Measurement Society in 2019. His research interests include instrumentation design, data analytics, and energy-efficient sensing for condition monitoring and health diagnosis of large-scale, complex, dynamical systems. Prof. Yan is currently an AdCom member of the IEEE Instrumentation and Measurement Society (IMS) and serves as the vice-president (VP) for Membership. He was also the VP for Technical and Standards Activities of the IMS in 2016-2019. Prof. Yan is the region 10 liaison of the IMS and formed the first local IMS chapter (Nanjing/Shanghai/Wuhan Jt. Sections IMS Chapter) in China, to promote instrumentation and measurement related activities. He is a chair of the Technical Committee (TC-7) on Signals and Systems in Measurement and worked as a working group co-chair to develop an IEEE P1451.001 Standard for Signal Treatment Applied to Smart Transducers. Prof. Yan has been serving as the Editor-in-Chief for the IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT since January 2022; His honours and awards include the IEEE Instrumentation and Measurement Society Technical Award in 2019 and the Distinguished Service Award in 2022, and multiple best paper awards. Prof. Yan has published over 180 papers, issued more than 20 Chinese invention patents, and published 3 books.

Contenu
Chapter 1 Introduction.- Chapter 2 Supervised SVM based intelligent fault diagnosis methods.- Chapter 3 Semi-supervised Learning Based Intelligent Fault Diagnosis Methods.- Chapter 4 Manifold learning based intelligent fault diagnosis and prognostics.- Chapter 5 Deep learning based machinery fault diagnosis.- Chapter 6 Phase space reconstruction based on machinery system degradation tracking and fault prognostics.- Chapter 7 Complex electro-mechanical system operational reliability assessment and health maintenance.