An intelligent fault diagnosis approach for planetary gearboxes based on deep belief networks and uniformed features

A planetary gearbox is a crucial but failure-prone component in rotating machinery, therefore an intelligent and integrated approach based on impulsive signals, deep belief networks (DBNs) and feature uniformation is proposed in this paper to achieve real-time and accurate fault diagnosis. Since the gear faults usually generate the repetitive impulses, an integrated approach using the optimized Morlet wavelet transform, kurtosis index and soft-thresholding is applied to extract impulse components from original signals. Then time-domain features and frequency-domain features are calculated by both original signals and impulsive signals, and probability density functions are applied to study the sensitivities of the features to the faults. The extracted features are fed into DBNs to identify the fault types, and the results show that the DBN-based fault diagnosis method is feasible and the impulsive signals play a positive role to improve the accuracies. Finally, by the mean value of various signals under multiple load conditions, uniformed time-domain features are constructed to reduce the interference of loads, and the experimental results validate that feature uniformation can improve the accuracies and robustness of intelligent fault diagnosis approach.