Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform

In this paper, fault diagnosis methodology is proposed for rolling element bearings, which utilizes autocorrelation of raw vibration signals to reduce the dimension of vibration signals with minimal loss of significant frequency content. It is observed that dimension of vibration signal is reduced to 10% with negligible loss of information. After reducing the dimension of vibration signals, coefficients of continuous wavelet transform are calculated using six different base wavelets. The base wavelet that maximizes the energy to Shannon entropy ratio is selected to extract statistical features from wavelet coefficients. Finally, a comparative study is carried out with the calculated statistical features as input to two supervised soft computing techniques like Artificial Neural Network and Support Vector Machine (SVM) for faults classification. The proposed method is applied to the rolling element bearings fault diagnosis and complex Gaussian wavelet is selected based on maximum energy to Shannon entropy ratio. The test results show that the SVM identifies the fault categories of rolling element bearing more accurately and has a better diagnosis performance. It is also observed that classification accuracy is improved with autocorrelation.