Sparsity Measures and Their Signal Processing Applications for Machine Condition Monitoring

Name: Sparsity Measures and Their Signal Processing Applications for Machine Condition Monitoring
Brand: Atlantic Books
SKU: 9780443334863
Price: 17047.0 INR
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by Dong Wang , Bingchang Hou

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ISBN13: 9780443334863
Binding: Paperback
Subject: N/A
Publisher: Elsevier
Publisher Imprint: Elsevier
Publication Date: May 22, 2025
Pages: 184
Original Price: USD 150.0
Language: English
Edition: N/A
Item Weight: 304 grams
BISAC Subject(s): Mechanical

About The Book

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.

About The Author

Hou, Bingchang: - 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

Wang, Dong: - 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)

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