Bearing fault diagnostic using machine learning algorithms

This study aims to enhance the condition monitoring of external ball bearings using the raw data provided by Paderborn University which provided sufficient data for motor current signal MCS. Three classes of bearings have been used: healthy bearings, bearings with an inner race defect, and bearings with outer race defect. Online data at different operating conditions, bearings, and faults extent of artificial and real damages have been chosen to provide the generalization and robustness of the model. After proper preprocessing to the raw data of vibration and MCS, time, frequency, and time-frequency domain features have been extracted. Then, optimal features have been selected using genetic algorithm. Artificial neural network with optimized structure using genetic algorithm has been implemented. A comparison between the performance of vibration and motor current signal has been presented. Moreover, our results are compared to previous work by using the same raw data. Results showed the potential of motor current signal in bearing fault diagnosis with high classification accuracy. Moreover, the results showed the possibility to provide a promised diagnostic model that can diagnose bearings of real faults with different fault severities using MCS.