Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring
- 1st Edition - February 1, 2025
- Imprint: Elsevier
- Authors: Dong Wang, Bingchang Hou
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 4 8 6 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 4 8 7 - 0
Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring presents newly designed sparsity measures and their advanced signal processing techno… Read more
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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
Academic researchers at universities and institutions involved in mechanical engineering/industrial engineering/signal processing who would like to know about trending and emerging theories and methodologies in the domain of machine condition monitoring, fault diagnosis and prognostics: especially fault feature extraction, sparsity measures and their advanced signal processing technologies
1. Introduction and background
1.1 Historical review
1.2 Machine fault characteristics and fault diagnosis
1.2.1 Typical modes of machine fault vibration signals
1.2.2 Bearing fault diagnosis
1.2.3 Gear fault diagnosis
1.3 Sparsity and Sparsity measures
1.4 Summaries References
2. Basic signal processing transforms and analysis
2.1 Introduction
2.2 Fourier transform
2.3 Time, frequency, and time-frequency analysis
2.4 Digital filtering
2.5 Hilbert transform and envelope analysis
2.6 Impulsiveness and cyclo-stationarity analysis
2.7 Summaries References
3. Newly advanced sparsity measures for fault signature quantification
3.1 Introduction
3.2 Classic sparsity measures and properties
3.3 Frameworks of sparsity measures
3.4 Newly designed sparsity measures and statistical indices
3.5 Cyclo-embedded sparsity measures
3.6 Other sparsity-informed measures
3.7 Difference between sparsity measures and complexity measures
3.8 Summaries References
4. Classic and advanced sparsity measures-based signal processing technologies
4.1 Introduction
4.2 Fast kurtogram and its variants
4.3 Blind deconvolution and its variants
4.4 Other signal decomposition/processing methods
4.4.1 Features of signal decomposition methods
4.4.2 EMD, LMD-based methods
4.4.3 WPT, EWT, VMD-based methods
4.4.4 Time-frequency based methods
4.4.5 Sparse decomposition and representation
4.5 Impulsive mode decomposition
4.6 Summaries References
5. Sparsity measures data-driven framework based signal processing technologies
5.1 Introduction
5.2 Sparsity measures data-driven framework
5.3 Optimized weights theory and its extensions
5.4 Optimized weights spectrum-based index
5.5 Difference mode decomposition
5.6 Others
5.7 Summaries
6. Outlook References
1.1 Historical review
1.2 Machine fault characteristics and fault diagnosis
1.2.1 Typical modes of machine fault vibration signals
1.2.2 Bearing fault diagnosis
1.2.3 Gear fault diagnosis
1.3 Sparsity and Sparsity measures
1.4 Summaries References
2. Basic signal processing transforms and analysis
2.1 Introduction
2.2 Fourier transform
2.3 Time, frequency, and time-frequency analysis
2.4 Digital filtering
2.5 Hilbert transform and envelope analysis
2.6 Impulsiveness and cyclo-stationarity analysis
2.7 Summaries References
3. Newly advanced sparsity measures for fault signature quantification
3.1 Introduction
3.2 Classic sparsity measures and properties
3.3 Frameworks of sparsity measures
3.4 Newly designed sparsity measures and statistical indices
3.5 Cyclo-embedded sparsity measures
3.6 Other sparsity-informed measures
3.7 Difference between sparsity measures and complexity measures
3.8 Summaries References
4. Classic and advanced sparsity measures-based signal processing technologies
4.1 Introduction
4.2 Fast kurtogram and its variants
4.3 Blind deconvolution and its variants
4.4 Other signal decomposition/processing methods
4.4.1 Features of signal decomposition methods
4.4.2 EMD, LMD-based methods
4.4.3 WPT, EWT, VMD-based methods
4.4.4 Time-frequency based methods
4.4.5 Sparse decomposition and representation
4.5 Impulsive mode decomposition
4.6 Summaries References
5. Sparsity measures data-driven framework based signal processing technologies
5.1 Introduction
5.2 Sparsity measures data-driven framework
5.3 Optimized weights theory and its extensions
5.4 Optimized weights spectrum-based index
5.5 Difference mode decomposition
5.6 Others
5.7 Summaries
6. Outlook References
- Edition: 1
- Published: February 1, 2025
- Imprint: Elsevier
- Language: English
DW
Dong Wang
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)
Affiliations and expertise
Shanghai Jiao Tong University, ChinaBH
Bingchang Hou
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
Affiliations and expertise
Shanghai Jiao Tong University, ChinaRead Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring on ScienceDirect