EWT多尺度排列熵与GG聚类的轴承故障辨识方法

Method Integrate EWT Multi-scale Permutation Entropy with GG Clustering for Bearing Fault Diagnosis

针对滚动轴承故障信号具有非线性、非平稳性特点导致的故障类别难以辨识问题,提出一种基于经验小波变换、多尺度排列熵、GG(Gath-Geva,简称GG)聚类算法相结合的故障诊断方法。首先,采用经验小波变换对滚动轴承的原始信号进行分解、得到若干个固有模态分量,初步提取滚动轴承的状态特征值;其次,通过相关性分析选择最优模态分量,并在多个尺度下计算其排列熵值;最后,运用主成分分析对高维熵值特征向量进行可视化降维、并输入到GG聚类算法中,实现对滚动轴承的故障辨识。与其他模式组合方法进行比较的结果表明,本研究提出的故障辨识方法具有聚类结果的类内紧致性更好的优点。

In the light of the identification of the faults type of rolling bearing,which is hard due to the non-linear and non-stationary characteristics of the fault signals,a method of fault identification is proposed.It consists of the experience wavelet transform(EWT),multi-scale permutation entropy(MPE)and GG(Gath-Geva)clustering algorithm.First of all,the original signals of rolling bearing are decomposed into many intrinsic mode components based on the EWT decomposition.Then,the state features of the rolling bearing are preliminary extracted;the optimal modal component is selected with correlation analysis,and the permutation entropy is calculated in multiple scales.Finally,the principal component analysis(PCA)is used to reduce the dimension of the entropy feature vector for visualization,and low features subset is introduced into the GG clustering algorithm to realize the fault diagnosis of the rolling bearing.Comparisons with other mode combination method show that the proposed fault diagnosis method has certainly advantages,which better fault recognition effect.