Machine Performance Degradation Assessment: Convex Optimization Models and Their Interpretable Data Fusion Applications - Softcover

Wang

Softcover

ISBN 10: 0443440077 ISBN 13: 9780443440076

Publisher: Elsevier, 2025

Synopsis

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.

• 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

About this title

About the Authors

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

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

From the Back Cover

Machine Performance Degradation Assessment: Convex Optimization Models and Their Interpretable Data Fusion Applications provides industry professionals and researchers in both academic and other organisations with the up to date information they need to understand the new trends and changes in interpretable performance degradation assessment. This book provides readers across the field of machine performance degradation assessment technologies with guidance on understanding the basic concepts and fundamental theories, and understand cutting-edge research and latest methods. With a primary focus on convex optimization models, their interpretable data fusion models and applications for assessing machine performance degradation, these topics, characterized by their novelty and practical utility, offer valuable insights for practitioners in the field. The basic mathematical elements of machine performance degradation assessment technologies are presented. Advanced methods showing how to formulate mathematical degradation properties into convex optimization models for health index construction are explained. In addition to demonstrating theoretical and experimental work, examples of real applications are provided to test methods introduced in the book. The book offers innovative perspectives on machine performance degradation assessment, examining novel concepts within the degradation process and associated mathematical properties, to provide readers with fresh insights and analytical frameworks, presented in a structurally robust manner

Edition details

• Publisher: Elsevier
• Publication date: 2025
• Language: English
• ISBN 10: 0443440077
• ISBN 13: 9780443440076
• Binding: Paperback
• Number of pages: 152