基于排放和模糊神经网络模型的柴油机故障诊断方法  

Study on Fault Diagnosis of Diesel Engine Based on Exhaust Emission and Fuzzy Network

作　　者：李国璋[1] 罗亮[2] 滕飞[1] 高阳[1] 

机构地区：[1]军械工程学院火炮工程系,河北石家庄050003 [2]武汉军械士官学校自行火炮系,湖北武汉430075

出　　处：《军械工程学院学报》2011年第5期48-52,共5页Journal of Ordnance Engineering College

摘　　要：根据排放检测数据规律，定义并提取特征参数，建立了规则与模糊神经网络有机结合的柴油机故障诊断模型及其对应的特征知识库，确立了模型的“可塑性”学习路线，并以单缸失火故障为例，进行了模型诊断实例研究。结果表明：运用该方法进行柴油机的故障诊断，结果准确，识别速度快，诊断效率高。In this paper, a new method of fault diagnosis for diesel engine based on the exhaust emission is proposed. The parameters are defined from the emission measured data by the analysis of the emission. And the diagnosis model which is made up of the rule and the fuzzy network is built. Then the characteristic database and the plastidty principle are confirmed. Through the experimentation of fault diagnosis, we can conclude that this method of fault diagnosis is accurately, efficient and valuable.

关 键 词：柴油机 排放 模糊神经网络 故障诊断 

分 类 号：TK42[动力工程及工程热物理—动力机械及工程] TP206[自动化与计算机技术—检测技术与自动化装置]