Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring

Erscheinungsjahr 2025
ISBN: 978-0-443-33486-3
Verlag: Elsevier Inc

Buch, Englisch, 186 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 258 g

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.

Autoren/Hrsg.

Wang, Dong

Hou, Bingchang

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung

Inhaltsverzeichnis

1. Introduction and background
2. Basic signal processing transforms and analysis
3. Newly advanced sparsity measures for fault signature quantification
4. Classic and advanced sparsity measures-based signal processing technologies
5. Sparsity measures data-driven framework based signal processing technologies
6. Outlook References

Über Autor(innen)

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)

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