吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于故障树和Bayes网络组合的装备故障诊断

刘淑芬1,2, 杨双双2, 王辉2   

  1. 1. 吉林大学 计算机科学与技术学院, 长春 130012;2. 河南理工大学 计算机科学与技术学院, 河南 焦作 454000
  • 收稿日期:2013-09-02 出版日期:2014-09-26 发布日期:2014-09-26
  • 通讯作者: 王辉 E-mail:wanghui_jsj@hpu.edu.cn

Fault Diagnosis of Equipment Based on Fault Tree Combined with Bayesian Network

LIU Shufen1,2, YANG Shuangshuang2, WANG Hui2   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China; 2. College ofComputer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, Henan Province, China
  • Received:2013-09-02 Online:2014-09-26 Published:2014-09-26
  • Contact: WANG Hui E-mail:wanghui_jsj@hpu.edu.cn

摘要:

针对故障树和Bayes网络在故障诊断中的局限性, 提出一种使用故障树和Bayes网络组合的方式建立诊断故障Bayes网络, 并基于诊断故障Bayes网络运用联合树推理进行故障诊断的方法. 该方法解决了在复杂系统故障诊断过程中独立运用故障树和Bayes网络出现故障推理能力弱和建模难等问题. 实验结果表明, 使用该方法对某型舰船上的甲板灯光照明系统进行故障诊断, 得出了各个故障征兆节点或故障原因节点的概率分布, 从而可快速准确地定位甲板灯光照明系统故障.

关键词: 故障树, Bayes网络, 联合树, 故障诊断

Abstract:

A new equipment fault diagnosis method was proposed. In this method, we established the fault diagnosis Bayesian network through the combination of the fault tree with Bayesian network, and used joint tree to reason out the fault diagnosis based on the diagnosis fault Bayesian network. The method can solve the problems that modelling hardly and weakly reasoning ability caused by respectively using fault tree and Bayesian network. Fault diagnosis to a deck lighting system of a ship was carried out, and then the probability distributions of fault nodes were calculated, as a result, the fault of deck lighting system can be located quickly and accurately.

Key words: fault tree, Bayesian network, junction tree, fault diagnosis

中图分类号: 

  • TP391