基于尾气成分与灰色关联度的内燃机故障诊断  

Fault Diagnosis of Internal Combustion Engine Based on Exhaust Composition and Grey Relation Analysis

作　　者：谢继鹏[1,2] Xie Jipeng(Nanjing University of Science and Technology Zijin College,Nanjing City,Jiangsu Province 210023,China;School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing City,Jiangsu Province 210094,China)

机构地区：[1]南京理工大学紫金学院智能制造学院,江苏省南京市210023 [2]南京理工大学机械工程学院,江苏省南京市210094

出　　处：《农业装备与车辆工程》2022年第5期46-49,共4页Agricultural Equipment & Vehicle Engineering

基　　金：江苏省研究生科研与实践创新计划项目(SJKY19_0274);南京理工大学紫金学院教育教学改革与研究课题项目(20200101005)。

摘　　要：故障智能分类与决策是内燃机故障诊断的重要手段。内燃机燃烧排放的尾气成分综合反映出内燃机运行状态的变化,对尾气成分分析是一种有效的故障诊断方法,提出基于动态权重的灰色关联度算法实现故障关联度区间及故障分类。实例计算结果表明,灰关联度对内燃机故障具有良好的区分度。发动机故障诊断时,只需计算出采集尾气成分的小样本数据的关联度,并与所述发动机故障的关联区间相比较即诊断出发动机故障类型。所提出的内燃机故障分类与诊断方法可在专用故障诊断仪中对内燃机故障进行自诊断。Intelligent fault classification and decision making is an important method for internal combustion engine fault diagnosis.The composition of exhaust gas from combustion of engine can reflect the state of engine. The analysis of exhaust gas composition is an effective method for fault diagnosis. A grey relational degree algorithm based on dynamic weight is proposed to realize fault relational degree interval and fault classification. The calculation results of an example show that the grey relational degree can distinguish the engine faults. The correlation degree of small sample data collected from exhaust composition is only needed to be calculated and compared with the correlation interval of the engine fault to diagnose the type of engine fault. The proposed fault classification and diagnosis method of internal combustion engine can be used for self-diagnosis of engine fault in a special fault diagnosis instrument.

关 键 词：内燃机 故障分类 尾气成分 灰色关联度 主成分分析 动态权重 

分 类 号：U472.9[机械工程—车辆工程] TK418[交通运输工程—载运工具运用工程]