查看文章

A novel Reinforcement Learning (RL) driven maintenance strategy is proposed in this paper for solving the problem of aircraft long-term maintenance decision optimization. Specifically, it is targeted to process the information of aircraft future mission requirement, repair cost, spare components storage and aircraft Prognostics and Health Management (PHM) output, and provide real-time End-to-End sequential maintenance action decisions based on the coordination between short and long-term operation performance. The proposed RL-driven strategy is designed in the RL framework with Extreme Learning Machine based Q-learning algorithm, and an integrated aircraft maintenance simulation model is developed for training/testing RL-driven strategy. We test the proposed RL-driven strategy in several simulated dynamic aircraft maintenance scenarios together with 3 other commonly used maintenance strategies …