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Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
- 1st Edition - October 28, 2016
- Author: Yaguo Lei
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 1 5 3 4 - 3
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 1 5 3 5 - 0
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL predictio… Read more
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Request a sales quoteIntelligent 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
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
- 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
- No. of pages: 376
- Language: English
- Edition: 1
- Published: October 28, 2016
- Imprint: Butterworth-Heinemann
- Paperback ISBN: 9780128115343
- eBook ISBN: 9780128115350
YL
Yaguo Lei
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.
Affiliations and expertise
Professor, School of Mechanical Engineering, Xi’an Jiaotong University, ChinaRead Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery on ScienceDirect