Risk-Informed Operations and Maintenance Decision Making Using Deep Reinforcement Learning
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Abstract
The goal of this research is to improve nuclear operations and maintenance (O&M) decision-making by integrating component reliability, condition monitoring, and deep reinforcement learning to reduce overall life-cycle costs. By using deep reinforcement learning, we can train a neural network to identify the optimal maintenance decision given the current state of the plant. Preliminary studies have shown that an optimized condition-based decision-maker can reduce O&M costs by over 50%.
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Risk-informed, Deep Reinforcement Learning, Maintenance, Decision Making, Asset Management
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