电控汽油喷射系统的贝叶斯故障诊断
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摘要
通过介绍发动机故障自诊断系统的诊断原理及适用范围,指出了发动机自诊断系统和传统的故障诊断方法都已经不能满足电控汽油喷射系统故障诊断的要求。在此基础上介绍了贝叶斯网络的发展及应用,阐述了贝叶斯网络的拓扑结构和数学模型及贝叶斯网络的特点。通过把故障树与贝叶斯网络进行对比,说明了故障树到贝叶斯网络转化的可行性,并给出了转化算法。
通过分析得出了电控汽油喷射系统发生故障时发动机出现的四种常见故障:发动机不能起动、发动机动力不足、发动机怠速不良、发动机加速不良,并给出了这四种常见故障的故障树图。然后根据故障树到贝叶斯网络的转化算法,将故障树逐一进行了贝叶斯网络转化。
根据实验室现有的试验设备和条件在哈飞赛马汽车上对电控发动机典型故障进行故障模拟试验。通过实验发现发动机的一些不明显故障是目前的发动机诊断仪不能诊断的,同时借助故障模拟试验数据确定了发动机怠速不良时电控汽油喷射系统贝叶斯网络故障诊断中各节点的先验概率值,然后通过贝叶斯网络找出了发动机怠速不良时电控汽油喷射系统各部件的故障发生概率。最后通过实验验证了用贝叶斯网络方法对电控汽油喷射系统进行故障诊断方便、快捷、直观且准确度高。
This paper describes the diagnosed principle and application of the engine fault self-diagnosis system, and introduces that the engine self-diagnosis system and the traditional fault diagnosis methods have been unable to meet the requirements of the electronically controlled gasoline injection system fault diagnosis. Based on this, This paper describes Bayesian networks development and application, and expatiates on the topology, the mathematical models and the characteristics of Bayesian networks. Through the fault tree and Bayesian networks compared, this paper shows the feasibility of the fault tree translation into Bayesian networks, and gives the translation algorithm.
When the electronically controlled gasoline injection system fault, the paper analyzes the four common engine fault: engine not starting, engine lacking power, engine idling bad, and engine accelerating bad, then gives their fault trees. According to the translation algorithm of the fault tree translation into Bayesian networks, the fault trees are translated into Bayesian networks one by one.
According to current equipment in laboratory, the fault simulation tests of typical faults have be tested on the electronically controlled engine on Hafei Saima. The experiments find that a number of engine faults are not obvious and these faults can not be diagnosed by the existing instruments of the engine diagnosis. According to the fault simulation tests, the prior probabilities of the nodes what is needed by Bayesian networks fault diagnosis are obtained. Then the paper gives the fault probability of the electronically controlled fuel injection system components when engine idling bad. Finally, fault diagnosis on Hafei Saima proves that Bayesian networks fault diagnosis of the electronically controlled gasoline injection system is convenient, fast, intuitive and high-accuracy.
通过分析得出了电控汽油喷射系统发生故障时发动机出现的四种常见故障:发动机不能起动、发动机动力不足、发动机怠速不良、发动机加速不良,并给出了这四种常见故障的故障树图。然后根据故障树到贝叶斯网络的转化算法,将故障树逐一进行了贝叶斯网络转化。
根据实验室现有的试验设备和条件在哈飞赛马汽车上对电控发动机典型故障进行故障模拟试验。通过实验发现发动机的一些不明显故障是目前的发动机诊断仪不能诊断的,同时借助故障模拟试验数据确定了发动机怠速不良时电控汽油喷射系统贝叶斯网络故障诊断中各节点的先验概率值,然后通过贝叶斯网络找出了发动机怠速不良时电控汽油喷射系统各部件的故障发生概率。最后通过实验验证了用贝叶斯网络方法对电控汽油喷射系统进行故障诊断方便、快捷、直观且准确度高。
This paper describes the diagnosed principle and application of the engine fault self-diagnosis system, and introduces that the engine self-diagnosis system and the traditional fault diagnosis methods have been unable to meet the requirements of the electronically controlled gasoline injection system fault diagnosis. Based on this, This paper describes Bayesian networks development and application, and expatiates on the topology, the mathematical models and the characteristics of Bayesian networks. Through the fault tree and Bayesian networks compared, this paper shows the feasibility of the fault tree translation into Bayesian networks, and gives the translation algorithm.
When the electronically controlled gasoline injection system fault, the paper analyzes the four common engine fault: engine not starting, engine lacking power, engine idling bad, and engine accelerating bad, then gives their fault trees. According to the translation algorithm of the fault tree translation into Bayesian networks, the fault trees are translated into Bayesian networks one by one.
According to current equipment in laboratory, the fault simulation tests of typical faults have be tested on the electronically controlled engine on Hafei Saima. The experiments find that a number of engine faults are not obvious and these faults can not be diagnosed by the existing instruments of the engine diagnosis. According to the fault simulation tests, the prior probabilities of the nodes what is needed by Bayesian networks fault diagnosis are obtained. Then the paper gives the fault probability of the electronically controlled fuel injection system components when engine idling bad. Finally, fault diagnosis on Hafei Saima proves that Bayesian networks fault diagnosis of the electronically controlled gasoline injection system is convenient, fast, intuitive and high-accuracy.
引文
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[5]郑凌云.汽车电控发动机系统故障诊断的特点及诊断方法[J].新疆农机化 2005(1):57-59.
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[10]孙洲阳,陈景锋,黄加亮.故障树分析法在柴油机故障诊断中的应用[J].中国修船,2000(6):24-26.
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[14]王立欣,王明彦,齐明.电机故障的逻辑诊断方法[J].中国电机工程学报,2003(3):112-115.
[15]李浩,杨今云.故障树分析法在数控加工中心故障诊断中的应用[J].机床电器,2006(3):18-20.
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[18]邵延峰,薛红军.故障树分析法在系统故障诊断中的应用[J].中国制造业信息化,2007(1):72-74.
[19]纪常伟.基于故障树的汽车故障诊断系统开发[J].车辆与动力技术,2003(1):52-57.
[20]叶伯生,黄增双,李斌.故障树分析法在数控机床故障诊断系统中的应用[J].机械设计与制造,2006(8):135-137.
[21]Luis Alberto M.Riascos,Marcelo G.Simoes,Paulo E.Miyagi.On-line fault diagnostic system for proton exchange membrane fuel cells[J].Journal of Power Sources,2007,175(9):419-429.
[22]Alyson G.Wilson,Aparna V.Huzurbazar.Bayesian networks for multilevel system reliability[J].Reliability Engineering & System Safety,2007,92(10):1413-1420.
[23]Luis Alberto M.Riascos,Marcelo G.Simoes and Paulo E.Miyagi.A Bayesian network fault diagnostic system for proton exchange membrane fuel cells[J].Journal of Power Sources,2007,165(2):267-278.
[24]S.Dey,J.A.Stori.A Bayesian network approach to root cause diagnosis of process variations[J].International Journal of Machine Tools and Manufacture,2005,1(1):75-91.
[25]P.Trucco,E.Cagno,F.Ruggeri,et al.A Bayesian Belief Network modelling of organisational factors in risk analysis:A case study in maritime transportation[J].Reliability Engineering & System Safety,2007,93(4):845-856.
[26]王理冬,汪光阳,程泽凯,等.贝叶斯网络的发展与展望[J].安徽工业大学学报,2006(4):195-198.
[27]S.Montani,L.Portinale,A.Bobbio,et al.RADYBAN:A tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks[J].Reliabiiity Engineering & System Safety,2007,93:922-932.
[28]Ferat Sahin,M.Cetin Yavuz,Ziya Arnavut,et al.Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization[J].Parallel Computing,2007,33(3):124-143.
[29]A.Bobbio,L.Portinale,M.Minichino,et al.Improving the analysis of dependable systems by mapping fault trees into Bayesian networks[J].Reliability Engineering & System Safety,2001,71(3):249-260.
[30]李相刚.贝叶斯网络在汽车故障诊断中的应用[J].公路与汽运,2006(4):21-23.
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