关键词: 滚动轴承, 故障诊断, 模糊熵, 总体平均经验模式分解, Gath-Geva聚类

Abstract: Aiming at the high dimension of entropy eigenvectors in rolling bearing vibration signals,a combination method based on Ensemble Empirical Mode Decomposition (EEMD),Fuzzy Entropy (FE),Principal Component Analysis (PCA) and Gath-Geva (GG) clustering algorithm was proposed for rolling bearing fault recognition.The original signals were decomposed into a series Intrinsic Mode Functions (IMFs) by using Empirical Mode Decomposition (EMD) and EEMD methods respectively,then the Sample Entropy (SE) and the FE methods were used to calculate the corresponding entropy values.The dimension reduction and visualization of SE and FE eigenvectors which regarded as the input of Fuzzy C-Mean (FCM),Gustafson-Kessel (GK) and GG models for fulfill the rolling bearing fault recognition were achieved by PCA model.The PC and CE two indicators were used to verify and compare the effect of twelve methods which were made up by EMD/EEMD,SE/FE and FCM/GK/GG respectively.The experiment result showed that the proposed method could reduce the dimension of ebtropyeigenvectors,and the effect of fault recognition clustering was better than the other eleven models.

Key words: rolling bearing, fault diagnosis, fuzzy entropy, ensemble empirical mode decomposition, Gath-Geva clustering