Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery - 1st Edition - ISBN: 9780128115343, 9780128115350

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery

1st Edition

Authors: Yaguo Lei
eBook ISBN: 9780128115350
Paperback ISBN: 9780128115343
Imprint: Butterworth-Heinemann
Published Date: 28th October 2016
Page Count: 376
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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.

Key Features

  • 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


Academic researchers at universities and other institutions, with mechanical engineering or computer science background, working in the field of intelligent fault diagnosis and RUL prediction. Company engineers or practitioners in charge of safe operation and maintenance of machinery

Table of Contents

  • About the Author
  • Preface
  • Chapter 1: Introduction and background
    • Abstract
    • 1.1. Introduction
    • 1.2. Overview of PHM
    • 1.3. Preface to Book Chapters
  • Chapter 2: Signal processing and feature extraction
    • Abstract
    • 2.1. Introduction
    • 2.2. Signal Preprocessing
    • 2.3. Signal Processing in the Time Domain
    • 2.4. Signal Processing in the Frequency Domain
    • 2.5. Signal Processing in the Time-Frequency Domain
    • 2.6. Conclusions
  • Chapter 3: Individual intelligent method-based fault diagnosis
    • Abstract
    • 3.1. Introduction to Intelligent Diagnosis Methods
    • 3.2. Artificial Neural Networks
    • 3.3. Statistical Learning Theory
    • 3.4. Deep Learning
    • 3.5. Conclusions
  • Chapter 4: Clustering algorithm–based fault diagnosis
    • Abstract
    • 4.1. Introduction to Clustering Algorithm
    • 4.2. Weighted K Nearest Neighbor-Based Fault Diagnosis
    • 4.3. Weighted Fuzzy c-Means–Based Fault Diagnosis
    • 4.4. Hybrid Clustering Algorithm–Based Fault Diagnosis
    • 4.5. Conclusions
  • Chapter 5: Hybrid intelligent fault diagnosis methods
    • Abstract
    • 5.1. Introduction
    • 5.2. Multiple WKNN Combination-Based Fault Diagnosis
    • 5.3. Multiple ANFIS Hybrid Intelligent Fault Diagnosis
    • 5.4. A Multidimensional Hybrid Intelligent Method
    • 5.5. Conclusions
  • Chapter 6: Remaining useful life prediction
    • Abstract
    • 6.1. Background
    • 6.2. Data-driven Prediction Methods
    • 6.3. Model-Based Prediction Methods
    • 6.4. Conclusions
  • Glossary
  • Index


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About the Author

Yaguo Lei

Prof. Yaguo Lei received the B.S. degree and the Ph.D. degree both in mechanical engineering from Xi’an Jiaotong University, P. R. China, in 2002 and 2007, respectively. He is currently a professor in mechanical engineering of Xi’an Jiaotong University, P. R. China. He worked at the University of Duisburg-Essen, Germany as an Alexander von Humboldt fellow and at the University of Alberta, Canada as a postdoctoral research fellow. His research interests include condition monitoring and fault diagnosis of rotating machinery, mechanical signal processing techniques, and intelligent fault diagnostics and remaining useful life (RUL) prediction. He is a recipient of several honors including Alexander von Humboldt Fellow of Germany, Excellent Young Scientists of National Natural Science Foundation of China, New Century Excellent Talents of Ministry Of Education, and Young Researcher New Star Scientists of Chinese Academy of Sciences. He is members of ASME and IEEE, senior members of CMES and CAA, respectively, and the editorial board members of Neural Computing & Applications, Advances in Mechanical Engineering, International Journal of Mechanic Systems Engineering, Journal of Signal Processing Theory and Applications, International Journal of Applied Science and Engineering Research, etc.

Affiliations and Expertise

Professor, School of Mechanical Engineering, Xi’an Jiaotong University, China

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