Machine Performance Degradation Assessment - Dong Wang

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Résumé :
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....

Biographie:
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...

Sommaire:
1. Machine performance degradation assessment
2. Fundamentals of convex optimization
3. Machine degradation processes related mathematical properties
4. Generalized health index weight optimization models based on degradation properties and amplitude fusion in the frequency domain
5. Generalized health index weight optimization models based on fault feature sparsity and amplitude fusion in the envelope spectral domain
6. Conclusions...