Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems

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, Xi’an 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.

• 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.