A Bearing Fault Diagnosis Strategy Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Correlation Coefficient Method

Abstract

In response to the difficulties in extracting fault features from bearing vibration signals and the serious mode mixing and endpoint effects in traditional empirical mode decomposition (EMD) methods, a bearing fault diagnosis strategy combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDANAN) and correlation coefficient method is proposed. This method fully combines the advantages of CEEMDANAN algorithm and correlation coefficient method in signal random detection. Firstly, CEEMDANAN decomposition is performed on the bearing vibration signals to obtain a series of intrinsic mode function (IMF) components. Select IMF components with high correlation coefficients for Hilbert envelope spectrum analysis to achieve bearing fault diagnosis. The experimental results show that the proposed method can effectively achieve fault diagnosis of bearings.