Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems

Abstract

Currently, in manufacturing, massive useful data about health condition and maintenance is often available thanks to Industry 4.0 technologies. However, how to take advantage of historical data to optimize maintenance policies for multi-component systems has still been a challenging problem. This is especially true when maintenance cost models at component level are not available and/or maintenance actions are imperfect. In order to cope with this issue, we propose in this paper an artificial-intelligence-based maintenance approach which first constructs a predictor based on artificial neural network (ANN) for estimating maintenance cost at system level and then employs a customized multi-agent deep reinforcement learning algorithm to optimize maintenance decisions that can be applied for large-scale systems. To evaluate the performance and scalability of the proposed maintenance approach, numerical studies are conducted on a small 4-component system with different configurations and a large system composed of 15 components considering both deterministic and random maintenance quality. The simulation results show that ANN-based predictor is efficient for maintenance cost forecasting and multi-agent deep reinforcement learning is a promising solution for maintenance decision-making and optimization.