基于离散时间贝叶斯网络的动态故障树分析方法

周忠宝; 周经伦; 孙权; 金光; 董豆豆

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基于离散时间贝叶斯网络的动态故障树分析方法

专题研究 | 更新时间：2025-12-08

- 基于离散时间贝叶斯网络的动态故障树分析方法
- Dynamic Fault Tree Analysis Method Based on Discrete-Time Bayesian Networks
- 西安交通大学学报 2007年41卷第6期页码：732-736
- 作者机构：
  
  国防科技大学信息系统与管理学院,长沙,410073
- 作者简介：
- 基金信息：
  
  : 2006-09-07. 作者简介: 周忠宝(1979～),男,博士生;周经伦(联系人),男,教授,博士生导师. 基金项目: “十五”国防预研基金资助项目(41319020103)
- DOI：
  中图分类号： TB114.3
- 网络首发：2007-06-10，
  
  纸质出版：2007
- 稿件说明：
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周忠宝, 周经伦, 孙权, 等. 基于离散时间贝叶斯网络的动态故障树分析方法[J]. 西安交通大学学报, 2007,41(6):732-736.

周忠宝, 周经伦, 孙权, et al. Dynamic Fault Tree Analysis Method Based on Discrete-Time Bayesian Networks[J]. 2007, 41(6): 732-736.
周忠宝, 周经伦, 孙权, 等. 基于离散时间贝叶斯网络的动态故障树分析方法[J]. 西安交通大学学报, 2007,41(6):732-736. DOI：

周忠宝, 周经伦, 孙权, et al. Dynamic Fault Tree Analysis Method Based on Discrete-Time Bayesian Networks[J]. 2007, 41(6): 732-736. DOI：

摘要

提出了一种基于离散时间贝叶斯网络的动态故障树分析方法.首先给出优先与门、顺序相关门、备件门、功能相关门等动态逻辑门向离散时间贝叶斯网络的转化方法

在得到动态故障树对应的离散时间贝叶斯网络之后

再利用贝叶斯网络推理算法计算、诊断和预计顶事件概率、重要度等常规分析结果.对数字飞控计算机系统进行的分析表明

该方法能够保证较高的求解精度

其相对误差均保持在0.4%以内

而且易于扩展到多态和非确定性逻辑关系的情形.

Abstract

A new dynamic fault tree analysis method based on discrete-time Bayesian networks is proposed. The conversion of the dynamic logic gates

such as priority and gate

sequence enforcing gate

spare gate and functional dependency gate to discrete-time Bayesian networks is studied. The top-event probability

importance measures and other diagnosis and prediction results can be obtained subsequently using the classical algorithms based on Bayesian networks. The comparison with Markov chain method is carried out by means of the digital aviation control computer system

and the results show that the proposed method can guarantee high accuracy

and the relative errors are no more than 0.4%. In addition

the proposed method can be easily extended to multi-state systems with non-determinate logic relations.

关键词

Keywords

references

Dugan J B, Bavuso S J, Boyd M A. Dynamic fault-tree models for fault-tolerant computer systems [J]. IEEE Transactions on Reliability, 1992, 41(3):363-377.

Amari S, Dill G, Howald E. A new approach to solve dynamic fault trees [C]∥Proceedings of Annual Conference on Reliability and Maintainability. Piscataway, USA: IEEE, 2003:374-379.

Bobbio A, Portinale L, Minichino M, et al. Improving the analysis of dependable systems by mapping fault trees into Bayesian networks [J]. Reliability Engineering and System Safety, 2001, 71(3):249-260.

Langseth H. Bayesian networks with applications in reliability analysis [D]. Trondheim, Norway: Department of Mathematical Sciences, Norwegian University of Science and Technology, 2002.

周忠宝, 董豆豆, 周经伦. 贝叶斯网络在可靠性分析中的应用 [J]. 系统工程理论与实践, 2006, 26(6): 95-100.

Zhou Zhongbao, Dong Doudou, Zhou Jinglun. Application of Bayesian networks in reliability analysis [J]. System Engineering Theory Practice, 2006, 26(6): 95-100.

Montani S, Portinale L, Bobbio A. Dynamic Bayesian networks for modeling advanced fault tree features in dependability analysis [C]∥European Safety and Reliability Conference. Leiden, Netherlands: Balkema Publishers, 2005:1414-1422.

Boudali H, Dugan J B. A discrete-time Bayesian network reliability modeling and analysis framework [J]. Reliability Engineering and System Safety, 2005, 87(3):337-349.

程明华, 姚一平. 动态故障树分析方法在软、硬件容错计算机系统中的应用 [J]. 航空学报, 2000, 21(1): 34-37.

Cheng Minghua, Yao Yiping. Application of dynamic fault tree analysis to software and hardware fault-tolerant control computer systems [J]. Acta Aeronautica et Astronautica Sinica, 2000, 21(1): 34-37.

Murphy K P. The Bayes net toolbox for Matlab [J]. Computing Science and Statistics, 2001, 33:331-351.

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