Deep reinforcement learning based preventive maintenance policy for serial production lines
作者：
Highlights：
• Apply a state-of-the-art deep reinforcement learning algorithm to the PM problem.
• Formulate the PM problem as an MDP with guidance of domain knowledge.
• Use a data-driven modeling method to build a fast simulator for learning.
• The DRL agent learns to perform group maintenance and opportunistic maintenance.
• Proposed method outperforms age-dependent policy and opportunistic policy.
摘要
•Apply a state-of-the-art deep reinforcement learning algorithm to the PM problem.•Formulate the PM problem as an MDP with guidance of domain knowledge.•Use a data-driven modeling method to build a fast simulator for learning.•The DRL agent learns to perform group maintenance and opportunistic maintenance.•Proposed method outperforms age-dependent policy and opportunistic policy.
论文关键词：Preventive maintenance,Production loss,Deep reinforcement learning,Serial production line,Group maintenance,Opportunistic maintenance
论文评审过程：Available online 30 June 2020, Version of Record 10 July 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113701
