Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring

Dr Dong Wang has over 15 years of research experience on machine condition monitoring and fault diagnosis. Dr. Wang’s research focuses on the theoretical foundations of fault feature extraction and their applications to machine condition monitoring, fault diagnosis and prognostics. Dr. Wang has published over 150 journal papers (the first author for 40+ papers) Bingchang Hou received his B.Eng. degree in Mechanical Engineering from Chongqing University, Chongqing, China, in 2020. Since Sep. 2020, he is pursuing his Ph.D. degree in Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. His research interests include machine condition monitoring and fault diagnosis, prognostics and health management, sparsity measures, signal processing, and machine learning

• 1. Introduction and background2. Basic signal processing transforms and analysis3. Newly advanced sparsity measures for fault signature quantification4. Classic and advanced sparsity measures-based signal processing technologies5. Sparsity measures data-driven framework based signal processing technologies6. Outlook References