Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring
Feb 2025 · Elsevier
Ebook
300
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About this ebook
Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring presents newly designed sparsity measures and their advanced signal processing technologies for machine condition monitoring and fault diagnosis. This book systematically covers new sparsity measures including a quasiarithmetic mean ratio framework for fault signatures quantification, a generalized Gini index, as well as classic sparsity measures based on signal processing technologies and a cycle-embedded sparsity measure based on new impulsive mode decomposition technology. This book additionally includes a sparsity measure data-driven framework–based optimized weights spectrum theory and its relevant advanced signal processing technologies. - Provides the background, roadmaps and detailed discussion of newly designed sparsity measures and their advanced signal processing technologies for machine condition monitoring and fault diagnosis - Covers new theories, advanced technologies, and the latest contributions in the field of machine condition monitoring and fault diagnosis - Particularly focuses on newly advanced sparsity measures for fault signature quantification, classic and advanced sparsity measures–based signal processing technologies and sparsity measures using data-driven framework–based signal processing technologies - Provides experimental and real-world practical validation cases, including newly advanced sparsity measures and their advanced signal processing technologies
About the author
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
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