Intelligent fault diagnosis of rolling bearing and gear system under fluctuating load conditions using image processing technique

Health monitoring of a rotating machine is mainly done by investigation of the vibration patterns generated by the machine. Leveraging the fact that faults occurring in different parts of a machine generate unique fault signatures, a fault diagnosis methodology is proposed that can identify nine different healthy and faulty categories under varying load and noisy conditions. Neural network is employed for classification of faults in various categories. The robustness of features such as semivariance, kurtosis and Shannon entropy make them strong candidates to train the artificial neural network. The matching of vibration textural patterns with wave atom basis functions ensures removal of noise. As a result, the enhanced features used to train the neural network have led to high accuracy in classification. The algorithm is tested at various load conditions for both bearing and gear fault experimental data sets acquired by machinery fault simulator in laboratory. Simulation results show high degree of accuracy for both bearing and gear fault diagnosis under no load to heavy load noisy conditions.