• CN:11-2187/TH
  • ISSN:0577-6686

›› 2014, Vol. 50 ›› Issue (2): 193-201.

• 论文 • 上一篇    下一篇

基于T-S故障树和贝叶斯网络的模糊可靠性评估方法

姚成玉;陈东宁;王斌   

  1. 燕山大学河北省工业计算机控制工程重点实验;燕山大学河北省重型机械流体动力传输与控制重点实验室;先进锻压成形技术与科学教育部重点实验室(燕山大学)
  • 发布日期:2014-01-20

Fuzzy Reliability Assessment Method Based on T-S Fault Tree and Bayesian Network

YAO Chengyu; CHEN Dongning; WANG Bin   

  1. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China
  • Published:2014-01-20

摘要: 针对贝叶斯网络方法存在的贝叶斯网络模型和节点条件概率表难以构造、根节点故障率和故障概率数据难以精确获取等不足,以及T-S故障树分析方法存在的计算复杂、不能进行反向推理等不足,提出基于T-S故障树和贝叶斯网络的模糊可靠性评估方法:利用T-S故障树构造贝叶斯网络模型、T-S门规则构造节点条件概率表;用模糊数描述节点的多种故障状态,模糊子集描述节点各故障状态下的故障率、故障概率;结合贝叶斯网络推理给出在仅知根节点故障状态条件下,叶节点各故障状态的发生概率、根节点状态重要度;以及已知根节点各故障状态的故障率、故障概率模糊子集条件下,叶节点各故障状态的故障率、故障概率模糊子集,以及根节点模糊重要度、后验概率。通过与文献[5]的T-S故障树分析方法、文献[10]的贝叶斯网络方法对比,验证所提方法的可行性。对巷道运输车液压系统进行模糊可靠性评估,计算根节点状态重要度等可靠性指标,为提高系统可靠性和进行故障诊断提供依据。

关键词: 可靠性评估;贝叶斯网络;T-S故障树;模糊子集;重要度;液压系统

Abstract: In order to solve the disadvantages of difficulty to construct Bayesian network model and nodes’ conditional probability tables and to obtain the root nodes’ fault rates and fault probabilities accurately in Bayesian network method, and the shortage of complicated calculation and no reverse inference in T-S fault tree analysis method, a fuzzy reliability assessment method based on T-S fault tree and Bayesian network is proposed:the Bayesian network model and conditional probability tables are constructed by T-S fault tree and T-S gate rules respectively; the multi fault states of nodes are described by fuzzy numbers, the nodes multi fault states’ fault rates and fault probabilities are described by fuzzy subsets; the probability of leaf node’s fault states and root nodes’ state importance are proposed at the condition of just knowing root nodes’ fault states, and the fault rates and fault probabilities fuzzy subsets of leaf node’s fault states, and root nodes’ fuzzy importance and posterior probabilities at the condition of knowing the fault rates and fault probabilities fuzzy subsets of root nodes’ fault states are proposed by the Bayesian network inference. It is proved that the proposed methods are feasible by comparing with the T-S fault tree method in reference [5] and Bayesian network method in reference [10]. At last, fuzzy reliability assessment of coalmine roadway transporter’s hydraulic system is completed by the proposed method, the reliability indexes such as the root nodes’ state importance is calculated out, which provides basis for improving system reliability and completing fault diagnosis.

Key words: reliability assessment;Bayesian network;T-S fault tree;fuzzy subset;importance;hydraulic system

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