Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring - Softcover

Wang

 
9780443334863: Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring
Softcover
ISBN 10: 0443334862 ISBN 13: 9780443334863
Publisher: Elsevier, 2025

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

"synopsis" may belong to another edition of this title.

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

From the Back Cover

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 this title" may belong to another edition of this title.

Publisher
Elsevier
Publication date
2025
Language
English
ISBN 10 
0443334862
ISBN 13 
9780443334863
Binding
Paperback
Number of pages
184