基于贝叶斯网络强化学习的复杂装备维修排故策略生成

刘宝鼎; 于劲松; 韩丹阳; 唐荻音; 李鑫

doi:10.13700/j.bh.1001-5965.2022.0449

基于贝叶斯网络强化学习的复杂装备维修排故策略生成

doi: 10.13700/j.bh.1001-5965.2022.0449

1.
北京航空航天大学自动化科学与电气工程学院，北京 100191
2.
北京航空航天大学仪器科学与光电工程学院，北京 100191
3.
中国运载火箭技术研究院，北京 100076

基金项目: 国家重点研发计划（2018YFB1403300）；国家自然科学基金（51875018，71701008）

详细信息

通讯作者:
E-mail：yujs@buaa.edu.cn

中图分类号: TP206+.3
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出版历程
- 收稿日期: 2022-05-31
- 录用日期: 2022-07-22
- 网络出版日期: 2022-08-01
- 整期出版日期: 2024-04-29

Complex equipment troubleshooting strategy generation based on Bayesian networks and reinforcement learning

1.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2.
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
3.
China Academy of Launch Vehicle Technology, Beijing 100076, China

Funds: National Key R&D Program of China (2018YFB1403300); National Natural Science Foundation of China (51875018, 71701008)

More Information

Corresponding author: E-mail：yujs@buaa.edu.cn

摘要

摘要:
为解决传统启发式维修排故决策方法决策时间长、生成策略总成本高的问题，提出一种基于贝叶斯网络（BN）结合强化学习（RL）进行复杂装备维修排故策略生成方法。为更好地利用复杂装备模型知识，使用BN进行维修排故知识表述，并且为更加贴近复杂装备实际情况，依据故障模式、影响和危害性分析（FMECA）的故障概率，经合理转化后作为BN的先验概率；为使用RL的决策过程生成维修排故策略，提出一种维修排故决策问题转化为RL问题的方法；为更好地求解转化得到的强化学习问题，引入观测-修复动作对（O-A）以减小问题规模，并设置动作掩码处理动态动作空间。仿真结果表明：在统一的性能指标下，所提BN-RL方法较传统方法获得更高的指标值，证明该方法的有效性和优越性。
- 强化学习 /
- 贝叶斯网络 /
- 维修排故策略生成 /
- 复杂装备 /
- 动态动作空间
Abstract:
To shorten the time spent and reduce the troubleshooting cost of traditional heuristic methods, a method of generating a troubleshooting strategy based on reinforcement learning (RL) and Bayesian networks (BN) is proposed for complex equipment. BN is used for the expression of knowledge to make better use of model knowledge of complex equipment. To get closer to the real scenario, the fault probability in the failure mode, effect, and critical analysis (FMECA) of complex equipment is converted and used as a prior probability in BN. A paradigm of converting troubleshooting problems into RL problems is proposed to generate a troubleshooting strategy by using the decision process of RL. The observation-action pair (O-A) is introduced to reduce the scale of the RL problem and the action masking is set to deal with dynamic action space. Simulation findings demonstrate the superiority of the proposed BN-RL method by demonstrating its remarkable performances compared to standard heuristic methods based on the proposed metrics.
- reinforcement learning /
- Bayesian networks /
- troubleshooting strategy generation /
- complex equipment /
- dynamic action space

HTML全文

图 1 基于BN的维修排故决策问题模型

Figure 1. Maintenance troubleshooting problem model based on BN

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图 2 本文BN-RL方法原理框架

Figure 2. Principal framework of proposed BN-RL method

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图 3 “汽车不能启动”问题BN模型

Figure 3. BN model of “Car can’t start” problem

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图 4 各方法维修排故成本

Figure 4. Troubleshooting cost of each method

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图 5 各方法性能指标

Figure 5. Performance of each method

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图 6 各方法引入FMECA前后维修排故成本

Figure 6. Troubleshooting cost of each method before and after introducing FMECA

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图 7 加入O-A动作前后问题规模

Figure 7. Problem scale before and after introducing O-A action

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图 8 设置动作掩码前后本文BN-RL方法性能指标

Figure 8. Performance of proposed BN-RL method before and after introducing action masking

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表 1 各方法耗时和维修排故成本

Table 1. Time consumed and troubleshooting costs of each method

方法	$C$	$t$
全知决策方法	25.46	1
随机决策方法	92.83	1.67
静态决策方法	134.31	2.11
决策理论方法	45.61	37 990.20
向前一步间接观测方法	38.23	383 244.21
本文BN-RL方法	30.66	11.10

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