Performance Degradation Assessment and Fault Diagnosis of Bearing Based on EMD and PCA-SOM

Bearings are used in a wide variety of rotating machineries, and bearing vibration signals are non-stationary signals. According to the non-stationary characteristics of bearing vibration signals, a bearing performance degradation assessment and fault diagnosis method based on empirical mode decomposition (EMD) and PCA-SOM is proposed in this paper. Firstly, vibration signals are decomposed into a finite number of intrinsic mode functions (IMFs), and EMD energy feature vector, which is composed of all the IMF energy, is obtained; then, principal component analysis (PCA) is introduced in to reduce the dimension of feature vectors; finally, the reduced feature vectors are chosen as input vectors of SOM neural network for performance degradation and fault diagnosis. The analysis results from bearings with different fault diameters and fault patterns show that the proposed method is able to assess the degradation of bearing suitably, and achieve fault recognition rate of over 95% for various bearing fault patterns.