【作者】 王旭景；

【导师】 韦志康； 黄大明；

【作者基本信息】 广西大学 ， 农业机械化工程， 2008， 硕士

【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。更多还原

【Abstract】 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.更多还原