Risk-Informed Operations and Maintenance Decision Making Using Deep Reinforcement Learning

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Published Oct 28, 2022
Ryan Spangler

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%.

How to Cite

Ryan Spangler. (2022). Risk-Informed Operations and Maintenance Decision Making Using Deep Reinforcement Learning. Annual Conference of the PHM Society, 14(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/3397
Abstract 34 |

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Keywords

Risk-informed, Deep Reinforcement Learning, Maintenance, Decision Making, Asset Management

Section
Doctoral Symposium Summaries