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

doi:10.1016/j.ress.2022.108757

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

2022-12-01

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DOI：10.1016/j.ress.2022.108757

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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.

Keyword:

Deep reinforcement learning

Multi-agent systems

Maintenance decision-making

Multi-state component systems

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

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