基于EMD熵特征融合的滚动轴承故障诊断方法

Method of roller bearing fault diagnosis based on feature fusion of EMD entropy

研究了滚动轴承故障诊断单一故障信号的局限性和故障特征的非线性,从信息融合的理论出发,利用非线性动力学参数熵作为特征,提出了基于经验模态分解(EMD)熵特征融合的方法来解决滚动轴承故障诊断问题.首先将原始信号进行EMD,利用EMD的自适应多分辨率的特点计算EMD得到的固有模态函数(IMF)信号的多种熵值,然后采用核主元分析(KPCA)对提取的状态特征进行信息融合,从而得到互补的特征,最后将提取的融合特征通过支持向量机(SVM)进行故障诊断.滚动轴承故障诊断实验表明:该方法结合了EMD、信息熵理论和KPCA强大的非线性处理能力的特点,可以进行滚动轴承故障诊断.

The limitation of single fault signal for roller bearing fault diagnosis and non- linear relation of fault features were studied. Starting from theory of information fusion, the method based on feature fusion of empirical mode decomposition （EMD） entropy was pro- posed using nonlinear dynamics parameters of entropy as features to deal with roller bearing fault diagnosis problem. Firstly, EMD was conducted for original signal, and on the basis of the property of adaptive multi-resolution for the EMD, different entropies of the intrinsic mode function （IMF） signal reconstructed by using the EMD were calculated. Secondly, the information fusion of the state features was further implemented by using the kernel principal component analysis （KPCA） to extract the complementary feature. Finally, the support vec- tor machine （SVM） was employed to diagnose the fault by using the extracted fusion fea- tures. The experiment of rolling bearing fault diagnosis shows that the proposed method combines EMD, information entropy theory and strong nonlinear processing of KPAC, so it can be used for roller bearing fault diagnosis.