A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

• Main Authors: Jingbao Hou, Yunxin Wu, Hai Gong, A. S. Ahmad, Lei Liu
• Format: Article
• Language: English
• Published: MDPI AG 2020-01-01
• Series: Applied Sciences
• Subjects: intelligent fault diagnosis; rolling element bearing; ensemble empirical mode decomposition; permutation entropy; linear discriminant analysis; clustering
• Online Access: https://www.mdpi.com/2076-3417/10/1/386

For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath−Geva (GG) clustering algorithm is proposed. Firstly, we decompose the vibration signal using EEMD, and several inherent modal components are obtained. Then, the permutation entropy values of each modal component are calculated to get the entropy feature vector, and the entropy feature vector is reduced by the LDA method to be used as the input of the clustering algorithm. The data experiments show that the proposed fault diagnosis method can obtain satisfactory clustering indicators. It implies that compared with other mode combination methods, the fault identification method proposed in this study has the advantage of better intra-class compactness of clustering results.