Improved Deep Transfer Auto-Encoder for Fault Diagnosis of Gearbox Under Variable Working Conditions With Small Training Samples

It is considerable to solve practical fault diagnosis task of gearbox under variable working conditions by introducing sufficient auxiliary data. For this purpose, a new approach called improved deep transfer auto-encoder is proposed for intelligent diagnosis of gearbox faults under variable working conditions with small training samples. First, multi-wavelet is employed as activation function for effectively learning useful features hidden in the non-stationary vibration data. Second, correntropy is used to modify the cost function to enhance the reconstruction quality. Third, pre-train an improved deep auto-encoder using sufficient auxiliary data in the source domain, and transfer its parameters to the target model. Finally, the improved deep transfer should be fine-tuned by small training samples in the target domain to adapt to the characteristics of the rest testing data. The proposed approach is used to analyze two sets of experimental vibration data collected from gearbox under variable working conditions. The results show that the proposed approach can accurately diagnose different faults of gearbox even the working conditions have significant changes, which is superior to the existing methods.