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基于FTA−BN的混合动力汽车故障诊断策略

Fault Diagnosis Strategy of Hybrid Electric Vehicle Based on FTA−BN

  • 摘要: 为实现对混合动力汽车(HEV)底盘系统多部件进行故障融合诊断,提出一种基于故障树分析和贝叶斯网络(FTA−BN)相结合的故障诊断策略。结合VB语言和SQL Server开源数据库平台设计HEV底盘故障诊断系统,基于构建的HEV底盘系统故障树模型,通过故障树与贝叶斯网络的映射关系将故障事件转换为网络节点,引入专家系统;依据混合模糊推理结合有界深度优先搜索方法,通过对故障征兆的定性和定量分析,基于FTA−BN计算故障事件的可信度,选择可信度高的原因作为诊断结论,且进行实验验证。结果表明:采用提出的FTA−BN故障诊断策略可对HEV底盘系统多部件进行故障融合诊断,故障诊断的准确率为0.850,高于人工经验判断的准确率(0.675)。

     

    Abstract: To realize fault fusion diagnosis for multiple components of hybrid electric vehicle (HEV) chassis system, a fault diagnosis strategy based on fault tree analysis and Bayesian network (FTA−BN) was proposed. The fault diagnosis system of HEV chassis was designed based on VB language and SQL Server open source database platform. Based on the constructed fault tree model of HEV chassis system, the fault events were converted into network nodes through the mapping relationship between fault tree and Bayesian network, and the fault events were introduced into the expert system. Based on the mixed fuzzy reasoning and bounded depth first search method, through the qualitative and quantitative analysis of fault symptoms, the reliability of fault events was calculated based on FTA−BN, and the causes with high reliability were selected as the diagnostic conclusions, and the experimental verification was carried out. The results show that the proposed FTA−BN fault diagnosis strategy can be used for the fusion diagnosis of multiple components of HEV chassis system, and the accuracy of fault diagnosis is 0.850, which is higher than that of manual experience judgment (0.675).

     

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