Rolling Element Bearing Condition Monitoring Based on Vibration Analysis Using Statistical Parameters of Discrete Wavelet Coefficients and Neural Networks

There are several techniques that can be used to determine the condition of a rolling element bearing. In this paper, vibration analysis is used to conduct fault diagnosis of a bearing. Vibration signal noise was eliminated using hard thresholding wavelet analysis. The best mother wavelet for the denoising process was selected using the minimum Shannon entropy criterion. Statistical parameters and other signal properties such as energy and entropy are powerful tools for analyzing vibration signals. These features were calculated in the time and wavelet domains and applied to Artificial Neural Networks (ANNs) as the feature vector to classify the condition of a bearing into one healthy and three faulty conditions. The ANN parameters were separately optimized using three optimization algorithms. The comparison of the results shows that if the ANN parameters are properly optimized, the statistical parameters in the time-frequency domain can optimize accuracy.