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Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery

  • ID: 3769896
  • Book
  • October 2016
  • Region: Global
  • 376 Pages
  • Elsevier Science and Technology
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Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The main contents include multi-domain signal processing and feature extraction, intelligent diagnosis models, clustering algorithms, hybrid intelligent diagnosis strategies, and RUL prediction approaches, etc.

This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to predict the RUL of rotating machinery. Besides experimental demonstrations, many application cases are presented and illustrated to test the methods mentioned in the book.

This valuable reference provides an essential guide on machinery fault diagnosis that helps readers understand basic concepts and fundamental theories. Academic researchers with mechanical engineering or computer science backgrounds, and engineers or practitioners who are in charge of machine safety, operation, and maintenance will find this book very useful.

  • Provides a detailed background and roadmap of intelligent diagnosis and RUL prediction of rotating machinery, involving fault mechanisms, vibration characteristics, health indicators, and diagnosis and prognostics
  • Presents basic theories, advanced methods, and the latest contributions in the field of intelligent fault diagnosis and RUL prediction
  • Includes numerous application cases, and the methods, algorithms, and models introduced in the book are demonstrated by industrial experiences

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1. Introduction and background 2. Signal Processing and feature extraction 3. Individual intelligent techniques based fault diagnosis 4. Clustering algorithms based fault diagnosis 5. Multidimensional hybrid intelligent diagnosis 6. RUL prediction

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Lei, Yaguo
Yaguo Lei is a Full Professor in the School of Mechanical Engineering at Xi'an Jiaotong University (XJTU), China, which he joined as an associate Professor in 2010. Prior to that, he worked at the University of Alberta, Canada, as a postdoctoral research fellow. He also worked at the University of Duisburg-Essen, Germany, as an Alexander von Humboldt fellow in 2012. He was promoted to Full Professor in 2013. He received the BS degree and the PhD degree both in Mechanical Engineering from XJTU, in 2002 and 2007, respectively. He is an associate editor or a member of the editorial boards of more than ten journals, including Mechanical Systems and Signal Processing, Measurement Science & Technology, and Neural Computing & Applications. He is also a Fellow of the Institution of Engineering and Technology (IET), a senior member of IEEE and a member of ASME, respectively. He has pioneered many signal processing techniques, intelligent fault diagnosis methods, and remaining useful life prediction models for machines. He has published one monograph and more than 100 peer-reviewed papers on signal processing, fault diagnosis and remaining useful life prediction.
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