基于MSPCA的汽油机失火故障诊断  

Research on Misfire Fault Diagnosis of Gasoline Engine based on MSPCA

作　　者：宫唤春[1] Gong Huanchun(Yanching Institute of Technology, Beijing 065201, China)

机构地区：[1]燕京理工学院

出　　处：《湖南科技大学学报（自然科学版）》2018年第3期69-73,共5页Journal of Hunan University of Science And Technology：Natural Science Edition

基　　金：河北省高等学校科学技术研究项目资助(Z2015092)

摘　　要：失火会导致汽油机性能下降,排放恶化.失火故障影响因素众多难以准确建立数学模型.本文利用多尺度主成分分析法提取气缸振动信号检测失火故障,对HQ492汽油机进行失火故障实验测试,通过小波变化和主成分分析对提取信号进行重构作为诊断失火故障的特征信号.试验结果表明：利用多尺度主成分分析可以快速准确诊断失火故障.Fire will lead to decline in gasoline engine performance,emissions deterioration. It is difficult to establish accurate mathematical model for the factors that affect the misfire. By using multi-scale principal component analysis（ MSPCA） method to extract the vibration signal of cylinder misfire detection,HQ492 gasoline engine misfire experiment to be test,the characteristics of signal extraction of signal reconstruction were analysed as the diagnosis of misfire fault by wavelet transform and principal component. The experimental results show that the fault diagnosis is quickly and accurately detected by multi-scale principal component analysis.

关 键 词：汽油机 失火故障 多尺度主成分分析 

分 类 号：TK417[动力工程及工程热物理—动力机械及工程]