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A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model

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  • Najafi, Seyedvahid
  • Lee, Chi-Guhn

Abstract

Condition-based maintenance (CBM) optimization may turn intractable when a complex system with multiple units becomes an asset of interest. This paper aims to find a CBM policy for a multi-unit series system subject to stochastic degradation, where a new inspection is scheduled based on age and condition monitoring data upon each inspection. The novelty of this study lies in proposing a modified deep reinforcement learning (DRL) algorithm for the semi-Markov decision processes (SMDP) to find an opportunistic CBM policy for a multi-unit system with economic dependency over an infinite horizon, where a range of repair actions are allowed under an aperiodic inspection scheme. We also suggested a novel environment simulator that considers the simultaneous impact of age and covariates using the proportional hazards (PH) model and the system's reliability characteristics. DRL acts as not only a learning algorithm obviating the full specification of the model but also an approximate scheme producing a solution in a limited computation. The proposed algorithm is applied to a multi-unit hydroelectric power system with the damage self-healing property to demonstrate the higher performance of the DRL algorithm in cost reduction than alternative policies and explain how enhancing system reliability reduces costs during the learning process.

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  • Najafi, Seyedvahid & Lee, Chi-Guhn, 2023. "A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000947
    DOI: 10.1016/j.ress.2023.109179
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    References listed on IDEAS

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