Predictive analytics using a nonhomogeneous semi-Markov model and inspection data

Abstract

Predicting the remaining useful life plays an important role in minimizing the overall maintenance cost of mechanical systems. Although most conventional reliability models deal with binary systems to perform such predictions, in most practical cases, mechanical systems experience multiple levels of degradation states before failure. When the degradation level associated with such a multistate deteriorating process is monitored only at fixed inspection points, extracted monitoring data are interval-censored. Interval censoring can influence both the parameter estimation (model training) and the calculation of principal reliability measures. This article studies the problem of parameter estimation and the development of principal prognostic-based reliability measures, including reliability function and mean residual life, for a multistate device under limited inspection capacity. The correctness of the introduced models is demonstrated through simulation-based numerical experiments. Finally, an example of the wear process of the shell of a bearing is used to demonstrate the application of the proposed models.

Keywords:

• Reliability prediction
• multistate degradation
• residual life
• parameter estimation
• interval censoring

Acknowledgements

The authors thank the anonymous reviewers and the Editor for their comments that helped in improving the quality of this article. This research is supported by the Natural Sciences and Engineering Research Council of Canada and the University of Electronic Science and Technology of China.

Additional information

Notes on contributors

Ramin Moghaddass

Ramin Moghaddass is currently a postdoctoral research fellow at the Massachusetts Institute of Technology. He received his Ph.D. from the University of Alberta, Canada, and his B.Sc. and M.Sc. from Sharif University of Technology, Iran. His research interests concern analytics and stochastic modeling and their applications in pattern recognition, prediction, and combining machine learning with decision making under uncertainty. He is particularly interested in applications in data analytics, health care, energy, reliability, and maintenance decision making.

Ming J Zuo

Ming J. Zuo received an M.Sc. in 1986 and a Ph.D. in 1989, both in Industrial Engineering, from Iowa State University, Ames, Iowa. He is currently a Professor in the Department of Mechanical Engineering at the University of Alberta. His research interests include system reliability analysis, maintenance planning and optimization, condition monitoring, and fault diagnosis. He is Associate Editor of IEEE Transactions on Reliability and Department Editor of IIE Transactions. He is Fellow of IIE, EIC, and ISEAM and a Senior Member of IEEE.

Yu Liu

Yu Liu is a Professor in the School of Mechanical, Electronic, and Industrial Engineering at the University of Electronic Science and Technology of China. He received his Ph.D. degree in Mechatronics Engineering from the University of Electronic Science and Technology of China. He was a Visiting Pre-doctoral Fellow in the Department of Mechanical Engineering at Northwestern University, Evanston, Illinois, between 2008 and 2010 and a Postdoctoral Research Fellow in the Department of Mechanical Engineering, at the University of Alberta, Edmonton, Canada, between 2012 and 2013. His research interests include system reliability modeling and analysis, maintenance decisions, prognostics and health management, and design under uncertainty.

Hong-zhong Huang

Hong-Zhong Huang is a Professor in the School of Mechanical, Electronic, and Industrial Engineering at the University of Electronic Science and Technology of China. He received a Ph.D. degree in Reliability Engineering from Shanghai Jiaotong University, China, and has published 200 journal papers and five books in the fields of reliability engineering, optimization design, fuzzy sets theory, and product development. His current research interests include system reliability analysis, warranty, maintenance planning and optimization, and computational intelligence in product design.