Title: Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring
Publisher: Elsevier - Health Sciences Division
Publication Date: 2025
Language: English
ISBN 10: 0443334862
ISBN 13: 9780443334863
Binding: Soft cover
Condition: new

About this title

Synopsis

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 Authors

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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