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Article Dans Une Revue Reliability Engineering and System Safety Année : 2021

Reinforcement learning for maintenance decision-making of multi-state component systems with imperfect maintenance

Van-Thai Nguyen
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Phuc Do Van
Alexandre Voisin
Benoît Iung

Résumé

In this paper we propose an artificial intelligence (AI) based framework for maintenance decision-making and optimization of multi-state component systems with imperfect maintenance. Our proposed framework consists of two main phases. The first aims at constructing artificial neural network (ANN) based predictors to forecast system’s reliability and maintenance cost. The second refers to the use of deep reinforcement learning (DRL) algorithms to optimize maintenance policy which can deal with large scale applications. Numerical results show that ANN is suitable to reliability, maintenance cost forecasting and DRL is a potentially powerful tool for maintenance decision- making and optimization.
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Dates et versions

hal-03784737 , version 1 (23-09-2022)

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Van-Thai Nguyen, Phuc Do Van, Alexandre Voisin, Benoît Iung. Reinforcement learning for maintenance decision-making of multi-state component systems with imperfect maintenance. Reliability Engineering and System Safety, 2021, 228, pp.108757. ⟨10.3850/978-981-18-2016-8_304-cd⟩. ⟨hal-03784737⟩
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