Journal of Shanghai Jiaotong University ›› 2018, Vol. 52 ›› Issue (5): 538-544.doi: 10.16183/j.cnki.jsjtu.2018.05.006

Previous Articles     Next Articles

Data-Driven Performance Degradation Condition Monitoring for Rolling Bearings

WU Jun a,b,LI Guoqiang a,WU Chaoyong a,CHENG Yiwei c,DENG Chao c   

  1. a. School of Naval Architecture and Ocean Engineering; b. Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration; c. National Engineering Research Center of Digital Manufacturing Equipment, Huazhong University of Science and Technology, Wuhan 430074, China
  • Online:2018-05-28 Published:2018-05-28

Abstract: A degradation condition monitoring method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and principal component analysis (PCA) for rolling bearings is proposed, which considers the problems of weak fault signals and diverse fault modes. In this paper, intrinsic energy features and failure frequency features are respectively extracted from the preprocessed original vibration signals of rolling bearings using CEEMDAN algorithm and Hilbert-Huang transform. Then, a health index of the rolling bearings is obtained through the feature fusion using spearman rank correlation coefficient and PCA. Finally, the degradation condition of the rolling bearings is identified by the analysis of the monotonicity, robustness and correlation of the health index. The conclusion is drawn by a case study that the proposed method can be applied to accurately and timely identifying the degradation condition of the rolling bearings.

Key words: rolling bearing, empirical mode decomposition, principal component analysis (PCA), condition monitoring

CLC Number: