An efficient procedure for optimal maintenance intervention in partially observable multi-component systems
Oktay KarabaÄŸ,
Bulut, Önder,
Toy, Ayhan Özgür and
Fadıloğlu, Mehmet Murat
Reliability Engineering and System Safety, 2024, vol. 244, issue C
Abstract:
With rapid advances in technology, many systems are becoming more complex, including ever-increasing numbers of components that are prone to failure. In most cases, it may not be feasible from a technical or economic standpoint to dedicate a sensor for each individual component to gauge its wear and tear. To make sure that these systems that may require large capitals are economically maintained, one should provide maintenance in a way that responds to captured sensor observations. This gives rise to condition-based maintenance in partially observable multi-component systems. In this study, we propose a novel methodology to manage maintenance interventions as well as spare part quantity decisions for such systems. Our methodology is based on reducing the state space of the multi-component system and optimizing the resulting reduced-state Markov decision process via a linear programming approach. This methodology is highly scalable and capable of solving large problems that cannot be approached with the previously existing solution procedures.
Keywords: Condition-based maintenance; Spare part quantity; Markov decision process; Linear programming; Stochastic degradation; Partially observable systems (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:244:y:2024:i:c:s0951832023008281
DOI: 10.1016/j.ress.2023.109914
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