基于多特征提取和KECA柴油机关键部件故障识别

Fault identification of key components of diesel engine based on multi feature extraction and KECA

针对柴油机系统故障特征信息微弱、识别率低的问题,提出一种基于多特征提取和核熵成分分析(KECA)的柴油机关键部件故障识别方法。首先对采集的信号经集合经验模态分解重构降噪后提取方差、峭度、方根幅值、峰值因子和排列熵作为特征参量,选择KECA对其高维降成低维特征,最后由支持向量机对新的低维特征进行故障识别,并对比用其他降维方法的分类结果。结果显示,此分类结果显著比其他方法好,识别准确率为96.67%,说明所提方法可对柴油机关键部件进行故障识别,且拥有良好的应用前景。

Aiming at the problems of weak fault feature information and low recognition rate of diesel engine system,a fault recognition method of key components of diesel engine based on multi feature extraction and kernel entropy component analysis(KECA)is proposed.Firstly,the collected signal is reconstructed and denoised by ensemble empirical mode decomposition,and then the variance,kurtosis,square root amplitude,peak factor and arrangement entropy are extracted as the characteristic parameters,which are reduced by KECA.Finally,support vector machine is used for fault identification and classification,and the classification results of other dimensionality reduction methods are compared.The results show that the classification results of this paper are obviously better than the other two,and the correct rate of fault identification is 96.67%,which shows that this method can effectively diagnose the fault of key components of diesel engine system,and has a good application prospect.