Machine Performance Degradation Assessment
Convex Optimization Models and Their Interpretable Data Fusion Applications
- 1st Edition - October 1, 2025
- Imprint: Elsevier
- Authors: Dong Wang, Tongtong Yan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 4 4 0 0 7 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 4 4 0 0 8 - 3
Machine Performance Degradation Assessment: Convex Optimization Models and Their Interpretable Data Fusion Applications is an essential resource for industry professionals and re… Read more
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Machine Performance Degradation Assessment: Convex Optimization Models and Their Interpretable Data Fusion Applications is an essential resource for industry professionals and researchers seeking to understand the latest trends in performance degradation assessment technologies. This comprehensive guide delves into the fundamental theories of convex optimization models while exploring cutting-edge research methods. Readers will gain valuable insights into interpretable data fusion models and their applications, providing practical and theoretical knowledge to advance their understanding of machine performance degradation. In addition to the core mathematical elements, the book includes advanced techniques for formulating degradation properties into convex optimization models for health index construction.
Real-world applications and examples demonstrate how these innovative methods can be applied in practice. By presenting novel concepts and analytical frameworks, this book offers fresh perspectives to help readers navigate the complexities of machine performance degradation assessment.
Real-world applications and examples demonstrate how these innovative methods can be applied in practice. By presenting novel concepts and analytical frameworks, this book offers fresh perspectives to help readers navigate the complexities of machine performance degradation assessment.
- Provides valuable insights on the evolving challenges in machinery performance monitoring and optimization
- Includes the background and roadmaps of machine performance degradation assessment technologies, which are described in-depth
- Presents real-world applications and examples of practical experience
Researchers, both academic and in other institutions, with a background in mechanical engineering or statistical mathematics, working in the field of machine performance degradation assessment
1 Machine performance degradation assessment
1.1 Introduction
1.2 Dataset collection
1.3 Signal processing technology
1.4 Feature extraction and selection
1.5 Data fusion technology
1.6 Health index construction
1.7 Conclusions
1.8 References
2 Fundamentals of convex optimization
2.1 Introduction
2.2 Convex set
2.3 Convex function
2.4 Convex optimization problems
2.5 Conclusions
2.6 References
3 Machine degradation processes related mathematical properties
3.1 Introduction
3.2 Different patterns of machine degradation processes
3.3 Desirable properties of health indices and their evaluation
3.4 Spectral amplitude fusion based generalized health indices
3.5 Conclusions
3.6 References
4 Generalized health index weight optimization models based on degradation properties and amplitude fusion in the frequency domain
4.1 Introduction
4.2 Convex degradation model of separation property
4.3 Convex degradation model of intra-class and inter-class properties
4.4 Convex degradation model of separation and monotonicity properties
4.5 Convex degradation model of separation, monotonicity and shapeness properties
4.6 Convex degradation model of separation and fitness properties
4.7 Convex degradation model of signal to noise ratio of a generalized health index
4.8 Conclusions
4.9 References
5 Generalized health index weight optimization models based on fault feature sparsity and amplitude fusion in the envelope spectral domain
5.1 Introduction
5.2 Sparsity property of fault transients
5.3 Sparse and flexible convex-hull representation based convex degradation model
5.4 Maximum entropy principle and weight sparsity regularization based convex degradation model
5.5 Monotonicity and weight sparsity regularization based convex degradation model
5.6 Sparsity preserving projection and baselined hyperdisk model based convex degradation model
5.7 Conclusions
5.8 References
6 Conclusions
6.1 References
1.1 Introduction
1.2 Dataset collection
1.3 Signal processing technology
1.4 Feature extraction and selection
1.5 Data fusion technology
1.6 Health index construction
1.7 Conclusions
1.8 References
2 Fundamentals of convex optimization
2.1 Introduction
2.2 Convex set
2.3 Convex function
2.4 Convex optimization problems
2.5 Conclusions
2.6 References
3 Machine degradation processes related mathematical properties
3.1 Introduction
3.2 Different patterns of machine degradation processes
3.3 Desirable properties of health indices and their evaluation
3.4 Spectral amplitude fusion based generalized health indices
3.5 Conclusions
3.6 References
4 Generalized health index weight optimization models based on degradation properties and amplitude fusion in the frequency domain
4.1 Introduction
4.2 Convex degradation model of separation property
4.3 Convex degradation model of intra-class and inter-class properties
4.4 Convex degradation model of separation and monotonicity properties
4.5 Convex degradation model of separation, monotonicity and shapeness properties
4.6 Convex degradation model of separation and fitness properties
4.7 Convex degradation model of signal to noise ratio of a generalized health index
4.8 Conclusions
4.9 References
5 Generalized health index weight optimization models based on fault feature sparsity and amplitude fusion in the envelope spectral domain
5.1 Introduction
5.2 Sparsity property of fault transients
5.3 Sparse and flexible convex-hull representation based convex degradation model
5.4 Maximum entropy principle and weight sparsity regularization based convex degradation model
5.5 Monotonicity and weight sparsity regularization based convex degradation model
5.6 Sparsity preserving projection and baselined hyperdisk model based convex degradation model
5.7 Conclusions
5.8 References
6 Conclusions
6.1 References
- Edition: 1
- Published: October 1, 2025
- Imprint: Elsevier
- Language: English
DW
Dong Wang
Dr. Dong Wang is based at the Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, China. Dr Wang has over 15 years' 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
Affiliations and expertise
Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, ChinaTY
Tongtong Yan
Tongtong Yan received her B.E. degree from Central South University in Changsha, China, in 2019. She is currently pursuing her Ph.D. in the Department of Industrial Engineering and Management and in the State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, China. Her research interests include interpretable convex optimization modeling, machine learning, statistical learning, machine condition monitoring, performance degradation assessment, and fault diagnosis
Affiliations and expertise
Department of Industrial Engineering and Management and in the State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, China