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