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Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
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Provides a systematic description and introduction to intelligent fault diagnosis and RUL prediction of rotating machinery

Table of Contents

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

About the Author

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.

Reviews

'Your book on "Intelligent Fault Diagnosis (& Prognosis) for Rotating Machinery" really made a difference to me.' CEO of Nanoprecise Company, Canada.

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