A Fault Diagnosis Approach of Gear System based on Deep Learning Theory - Wenbo Xu; Yafeng Ren; Bing Han (Jixie chuandong)
The FFT-DBN model based on fast Fourier transform and deep belief network， WT-CNN model based on wavelet transform and deep convolutional neural network and HHT-CNN model based on Hilbert Huang transform and deep convolutional neural network are established respectively. Through the integration of the three depth learning models， the comprehensive evaluation model of gear system fault diagnosis based on depth learning is further constructed. By setting up the vibration test bench of the power closed gear system， the test gear pairs with different failure modes are processed and their vibration acceleration signals are extracted as samples， the fault identification effect of the comprehensive evaluation model based on the depth learning is compared with other models， and the results show that the comprehensive evaluation model based on the depth learning can effectively identify a variety of gear faults. Comparing with other models， the fault recognition accuracy of the comprehensive evaluation model based on deep learning is higher.