程俭达, 刘炎, 李天匀, 等. 强化学习模式下舰船多状态退化系统的维修策略[J]. 中国舰船研究, 2021, 16(6): 45–51. doi: 10.19693/j.issn.1673-3185.02129
引用本文: 程俭达, 刘炎, 李天匀, 等. 强化学习模式下舰船多状态退化系统的维修策略[J]. 中国舰船研究, 2021, 16(6): 45–51. doi: 10.19693/j.issn.1673-3185.02129
shu
CHENG J D, LIU Y, LI T Y, et al. Maintenance strategy of ship multi-state deterioration system under reinforcement learning mode[J]. Chinese Journal of Ship Research, 2021, 16(6): 45–51. doi: 10.19693/j.issn.1673-3185.02129
Citation: CHENG J D, LIU Y, LI T Y, et al. Maintenance strategy of ship multi-state deterioration system under reinforcement learning mode[J]. Chinese Journal of Ship Research, 2021, 16(6): 45–51. doi: 10.19693/j.issn.1673-3185.02129
shu

强化学习模式下舰船多状态退化系统的维修策略

Maintenance strategy of ship multi-state deterioration system under reinforcement learning mode

    摘要
  • 摘要:
      目的  舰船的船体结构、武器装置及动力设备等系统在服役期间的性能退化将增加全寿命周期的运行风险，故须根据实船退化情况开展视情维修策略研究。
      方法  基于马尔科夫链建立多状态退化系统模型，利用强化学习方法训练产生维修策略的代理，在自适应学习过程中得到最优维修策略。
      结果  某船舶结构退化系统的验证结果表明，该方法可以在考虑系统实际退化状态下实现最优维修策略的快速响应，为决策者提供视情维修策略的智能化辅助决策工具。
      结论  舰船视情维修策略与强化学习相结合是一种提升舰船装备维修决策技术水平的可行方法。

     

    Abstract:
      Objectives  Naval ship systems such as the hull structure, weapons equipment and power equipment will deteriorate during their service life. Thus, a ship maintenance strategy based on the actual deterioration state is essential for ensuring the safety and availability of naval ships.
      Methods  In this paper, a multi-state deterioration system model is established on the basis of the Markov decision process. A reinforcement learning mode is then introduced to train the agent that generates the maintenance strategy, and the optimal condition-based maintenance strategy is obtained in the process of adaptive learning.
      Results  The proposed method is applied to a ship structural deterioration system for demonstration, and the results show that it can obtain the optimal maintenance policy for a multi-state deterioration system considering the actual conditions, thereby providing an intelligent supporting tool for decision-makers to formulate optimal ship maintenance strategies.
      Conclusions  This paper shows that the reinforcement learning method has great potential in comprehensively improving ship maintenance support.

     

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