Residual Life Prediction and Optimal Maintenance Decision for a Piece of Equipment (eBook)

This book addresses remaining life prediction and predictive maintenance of equipment. It systematically summarizes the key research findings made by the author and his team and focuses on how to create equipment performance degradation and residual life prediction models based on the performance monitoring data produced by currently used and historical equipment. Some of the theoretical results covered here have been used to make remaining life predictions and maintenance-related decisions for aerospace products such as gyros and platforms. Given its scope, the book offers a valuable reference guide for those pursuing theoretical or applied research in the areas of fault diagnosis and fault-tolerant control, remaining life prediction, and maintenance decision-making.

Changhua Hu is a Cheung Kong professor at Hi-Tech Institute of Xi'an, Shaanxi, China. He was a visiting scholar at University of Duisburg (September 2008-December 2008). His current research has been supported by the National Science Foundation of China. He has published two books and over 100 articles. His research interests include fault diagnosis and prognosis, life prediction, and fault-tolerant control.

Hongdong Fan received his B.Eng. degree in mechanical and electrical engineering, M.Sc., and Ph.D. degrees in control engineering all from Xi'an Institute of Hi-Tech, Xi'an, China, in 2003, 2006, and 2012, respectively. He is currently a lecturer of Xi'an Institute of Hi-Tech. His current research interest is in the area of reliability analysis, fault prognosis, and predictive maintenance.

Zhaoqiang Wang received the M.S. and Ph.D. degrees from High-Tech Institute of Xi'an, Xi'an, Shaanxi, China, in 2011 and 2015, respectively. He is currently an assistant professor with the High-Tech Institute of Xi'an, Xi'an, Shaanxi, China. He has published one book and over 20 articles in several journals, including the IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Reliability, Mechanical Systems & Signal Processing, Reliability Engineering & System Safety, etc. He is also an active reviewer for a number of high-quality international journals. His research interests include machine learning, prognostics and health management, reliability modeling, maintenance scheduling, and inventory controlling. 

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This book addresses remaining life prediction and predictive maintenance of equipment. It systematically summarizes the key research findings made by the author and his team and focuses on how to create equipment performance degradation and residual life prediction models based on the performance monitoring data produced by currently used and historical equipment. Some of the theoretical results covered here have been used to make remaining life predictions and maintenance-related decisions for aerospace products such as gyros and platforms. Given its scope, the book offers a valuable reference guide for those pursuing theoretical or applied research in the areas of fault diagnosis and fault-tolerant control, remaining life prediction, and maintenance decision-making.