Optimal maintenance policy incorporating system level and unit level for mechanical systems

ABSTRACT

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach.

KEYWORDS:

• Mechanical system
• condition-based maintenance
• semi-Markov decision process
• system-level maintenance
• unit-level maintenance

Acknowledgments

The authors would like to express their gratitude to associate editor and respected referees for their useful comments and suggestions which contributed to significant improvement and enrichment of the original version of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research was supported by China Scholarship Council (CSC) [grant number 201506160096]; National Natural Science Foundation of China (NSFC) [grant number 51475189]; National Key Research and Development Program of China [grant number 2016YFE0121700]; Science and Technology Development Fund of Macao S.A.R (FDCT) (MoST-FDCT Joint Grant) [grant number 015/2015/AMJ].

Notes on contributors

Chaoqun Duan

Chaoqun Duan is currently working toward the Ph.D. degree at School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China. He is also a visiting Ph.D. student with Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada since the year of 2015. His research interests include fault diagnostics and prognostics, model predictive control and condition-based maintenance.

Chao Deng

Chao Deng received the Ph.D. degree in mechanical engineering from the School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China, in 1998. From 1998 to 2000, she was a Post-Doctoral Research Fellow with Tokyo Institute of Technology, Japan. In 2008, she was a visiting professor with University of Auckland, New Zealand. She is currently a Full Professor with School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China. Her research interests mainly include condition monitoring, reliability engineering, intelligent maintenance and model predictive control.

Bingran Wang

Bingran Wang is pursuing the B.A.Sc degree in aerospace engineering with Division of Engineering Science, University of Toronto, Toronto, Canada. Partway through his undergraduate degree, he took the practical experience year program, and is currently working as a full-time Aircraft Performance Engineer at Bombardier Aerospace Toronto site. His research interests include aircraft modelling, system identification and operation research.