 Condition-based maintenance plan for multi-state systems using reinforcement learningShuyuan Gan, Nooshin Yousefi and David W. Coit International Journal of Reliability and Safety, 2024, vol. 18, issue 2, 144-162 Abstract: In this paper, a dynamic condition-based maintenance model is presented for a degrading system using a reinforcement learning approach. The system utilises a critical machine with multiple production states, which are estimated and obtained by some measurable indicators. Within the framework of the suggested maintenance policy, both the production state and machine virtual age are considered during the decision-making process for maintenance actions. The system is inspected at periodic times, and at each inspection, an action is selected from those that are available regarding spare parts and maintenance. Replacement and different levels of imperfect repairs are both considered. Additionally, the ordering of spare parts is also involved. The problem is formulated by using a discrete time Markov decision process and solved by the Q-learning algorithm. Numerical examples are provided to illustrate the proposed method. Also, the results indicate that the proposed method is time-saving and more cost-efficient than traditional methods. Keywords: dynamic maintenance; Markov decision process; Q -learning; spare parts; virtual age. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijrsaf:v:18:y:2024:i:2:p:144-162 Access Statistics for this article More articles in International Journal of Reliability and Safety from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.