Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring

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Dong Wang (Shanghai Jiao Tong University, China) Bingchang Hou, B.Eng (Shanghai Jiao Tong University, China)

Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring

Dong Wang (Shanghai Jiao Tong University, China), Bingchang Hou, B.Eng (Shanghai Jiao Tong University, China)

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English

Elsevier - Health Sciences Division
16 May 2025

Mechanical engineering; Communications engineering & telecommunications; Signal processing

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.

By: Dong Wang (Shanghai Jiao Tong University China), Bingchang Hou, B.Eng (Shanghai Jiao Tong University, China)
Imprint: Elsevier - Health Sciences Division
Country of Publication: United States
Dimensions: Height: 229mm, Width: 152mm,
Weight: 450g
ISBN: 9780443334863
ISBN 10: 0443334862
Pages: 184
Publication Date: 16 May 2025
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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