This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating machinery. Techniques and applications through experiments and case studies for intelligent precision compensation and fault diagnosis are offered along with the introduction of machine learning and deep learning methods.
Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems discusses how to formulate and solve precision compensation and fault diagnosis problems. The book includes experimental results on hardware equipment used as practical examples throughout the book. Machine learning and deep learning methods used in intelligent precision compensation and intelligent fault diagnosis are introduced. Applications to deal with relevant problems concerning CNC machining and rotating machinery in industrial engineering systems are provided in detail along with applications used in precision motion systems.
Methods, applications, and concepts offered in this book can help all professional engineers and students across many areas of engineering and operations management that are involved in any part of Industry 4.0 transformation.
This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating machinery. Techniques and applications through experiments and case studies for intelligent precision compensation and fault diagnosis are offered along with the introduction of machine learning and deep learning methods.
1. Background and Related Methods.
2. Fault Diagnosis Method Based on Recurrent Convolutional Neural Network.
3. Fault Diagnosis of Rotating Machinery Gear Based on Random Forest Algorithm.
4. Bearing Fault Diagnosis under Different Working Conditions Based on Generative Adversarial Networks.
5. Rotating Machinery Gearbox Fault Diagnosis Based on One-Dimensional Convolutional Neural Network and Random Forest.
6. Fault Diagnosis for Rotating Machinery Gearbox Based on Improved Random Forest Algorithm.
7. Imbalanced Data Fault Diagnosis Based on Hybrid Feature Dimensionality Reduction and Varied Density Based Safe-Level Synthetic Minority Oversampling Technique.
Rui Yang received the B.Eng. degree in Computer Engineering and the Ph.D. degree in Electrical and Computer Engineering from National University of Singapore in 2008 and 2013 respectively. He is currently an Assistant Professor in the School of Advanced Technology, Xian Jiaotong-Liverpool University, Suzhou, China, and an Honorary Lecturer in the Department of Computer Science, University of Liverpool, Liverpool, United Kingdom. His research interests include machine learning based data analysis and applications.
Maiying Zhong received her Ph.D. degree in control theory and control engineering from the Northestern University, China, in 1999. From 2000 to 2001, she was a visiting Scholar at the University of Applied Sciences Lausitz, Germany. From 2002 to July 2008, she was a professor of the School of Control Science and Engineering at Shandong University. From 2006 to 2007, she was a Postdoctoral Researcher Fellow with the Central Queensland University, Australia. From 2009 to 2016, she was a professor of the School of Instrument Science and Opto-Electronics Engineering, Beihang University. In March 2016, she joined Shandong University of Science and Technology, China, where she is currently a professor with the College of Electrical Engineering and Automation. Her research interests are model based fault diagnosis, fault tolerant systems and their applications.
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