This paper proposes a maintenance decision-making framework
for multi-unit systems using Machine Learning (ML). Specically, we propose
to use Deep Reinforcement Learning (RL) for a dynamic maintenance model
of a multi-unit parallel system that is subject to stochastic degradation and
random failures. As each unit deteriorates independently in a three-state ho-
mogeneous Markov process, we consider each unit to be in one of three states:
healthy, unhealthy, or a failed state. We model the interaction among system
states based on the Birth/Birth-Death process. By combining individual com-
ponent states, we dene the overall system state. To minimize costs, we use
the Markov Decision Process (MDP) framework to solve the optimal main-
tenance policy. We apply the Double Deep Q Networks (DDQN) algorithm
to solve the problem, making the proposed RL solution more practical and
eective in terms of time and cost savings than traditional MDP approaches.
A numerical example is provided which demonstrates how the RL can be used
to nd the optimal maintenance policy for the system under study.