基于粗糙集与BP神经网络的发动机故障诊断模型  

Study on Fault Diagnosis Model of Misfire in Engines Based on Rough Set Theory and Neural Network Technology

作　　者：李增芳[1] 何勇[1] 

机构地区：[1]浙江大学生物系统工程与食品科学学院

出　　处：《农业机械学报》2005年第8期118-121,共4页Transactions of the Chinese Society for Agricultural Machinery

基　　金：高等学校优秀青年教师教学科研奖励计划资助项目(项目编号:02411);浙江省自然科学基金资助项目(项目编号:301270);浙江省自然科学基金人才基金资助项目(项目编号:RC02067);浙江省教育厅资助项目(项目编号:JK2002316)

摘　　要：结合粗糙集理论和神经网络在信息处理方面的优势,建立了一个基于粗糙集理论和BP神经网络相结合的发动机失火故障诊断模型。通过对EQ6102型发动机的实际试验表明,模型简化了网络训练样本,优化了神经网络结构,提高了系统运行效率。Low burning quality of engine mixture gases will cause engine power descending, fuel consumption increasing and pollution of exhaust emission aggravation. Analyzing the relationship between change of exhaust emission＇s content and the burning quality, a new fault diagnosis model of misfire in engines based on rough sets theory and neural network technology was presented. The model reduced the sample size, optimized the neural network, decreased the computation and increased the diagnosis correctness. It has been testified by the experimentation of EQ6102 engine and results showed that it could optimize neural network technology and increase the running efficiency of the system.

关 键 词：发动机 故障诊断 粗糙集 神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TK418[自动化与计算机技术—控制科学与工程]