Neural Network and L-kurtosis for Diagnosing Rolling Element Bearing Faults

Finding a precise method for improved fault detection and classification when dealing with non-stationary vibration signals is the main goal of this paper. For the detection and classification of induction motor failures, a wavelet packet decomposition (WPD) associated to an artificial neural network (ANN) technique is considered. The effectiveness of this approach depends on the characteristics that have been carefully chosen and prepared to enable the classifier support the healthy conditions of the monitored system with the aid of the measured signal. Different testing data sets of healthy and defective bearings under various rotating speeds are studied to train the ANN classifier in order to demonstrate the effectiveness of the proposed method. The results showed the high performance of this procedure as an efficient method for bearing fault diagnosis.