内燃机失火故障的神经网络诊断研究  

A Research on I.C.Engine Misfire Fault Neural Network Diagnosis

作　　者：宫唤春[1] 邢佳蕊 吴义虎[1] 

机构地区：[1]长沙理工大学能源与动力工程学院,长沙410076 [2]天津市市政工程学校,天津300171

出　　处：《北京联合大学学报》2008年第2期33-36,40,共5页Journal of Beijing Union University

基　　金：湖南省自然科学基金杰出青年项目(01jzy2102)

摘　　要：提出了一种利用排气中HC、CO2、O2浓度和内燃机工况参数信息的内燃机失火故障诊断方法,并提出了描述内燃机失火程度的模糊评价指标,进行了内燃机有失火故障和无故障排气成分检测对比实验。利用实验数据和内燃机工况参数,通过Elman神经网络建立了失火程度评价指标与排气中HC、CO2、O2浓度以及内燃机工况参数之间关系的诊断模型,应用MATLAB软件对该模型进行学习训练,将训练好的神经网络模型应用于内燃机失火故障的诊断。结果表明,此模型能够正确诊断内燃机失火故障。An information fusion method for diagnosis of misfire fault in internal combustion engine based on exhaust density of HC,CO2,O2 and the engine＇s operation parameters are presented in this paper,and a fuzzy figure describing the misfire degree is also introduced.The engine＇s operation parameters,exhaust emission with misfire fault and without fault are tested.A diagnosis model which describes the relationship between the misfire degree and the internal combustion engine＇s exhaust emission and operation work parameters is established based on GRNN neural network,and the model is trained by test data and MATLAB software.The model has been used to diagnose internal combustion engine misfire fault. The result illustrates that this diagnosis model is suitable.

关 键 词：内燃机 失火 神经网络 故障诊断 

分 类 号：TK401[动力工程及工程热物理—动力机械及工程]