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

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 iscurrently 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.

• Introduction.- Degradation modelling and remaining useful life estimation based on a nonlinear diffusion process.- Degradation modelling and remaining useful life estimation based on a Wiener process with change points.- Residual life prediction based on an inverse-Gaussian process.- Degradation modelling and remaining useful life prediction with support vector machines.- Degradation modelling and remaining useful life estimation based on a relative vector machine fuzzy model.- Performance degradation modelling and reliability prediction based on evidential reasoning approach.- Residual life prediction based on a weight selected particle filter.- Optimal inspection policy based on predicted life information for a deteriorating equipment.- Cooperative predictive maintenance of repairable systems with dependent failure modes and resource constraint.