Condition-based maintenance optimisation without a predetermined strategy structure for a two-component series system

Zhang, Zhisheng, Zhou, Yifan, Sun, Yong, & Ma, Lin (2012) Condition-based maintenance optimisation without a predetermined strategy structure for a two-component series system. Maintenance and Reliability, 14(2), pp. 120-129.

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Most existing research on maintenance optimisation for multi-component systems only considers the lifetime distribution of the components. When the condition-based maintenance (CBM) strategy is adopted for multi-component systems, the strategy structure becomes complex due to the large number of component states and their combinations. Consequently, some predetermined maintenance strategy structures are often assumed before the maintenance optimisation of a multi-component system in a CBM context. Developing these predetermined strategy structure needs expert experience and the optimality of these strategies is often not proofed. This paper proposed a maintenance optimisation method that does not require any predetermined strategy structure for a two-component series system. The proposed method is developed based on the semi-Markov decision process (SMDP). A simulation study shows that the proposed method can identify the optimal maintenance strategy adaptively for different maintenance costs and parameters of degradation processes. The optimal maintenance strategy structure is also investigated in the simulation study, which provides reference for further research in maintenance optimisation of multi-component systems.

Impact and interest:

12 citations in Scopus
8 citations in Web of Science®
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ID Code: 59401
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: semi-Markov decision process, condition-based maintenance, multi-component system
ISSN: 15072711
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 EIN
Deposited On: 26 Apr 2013 02:21
Last Modified: 12 Jun 2013 15:44

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