Deep residual hedging network and its application in fault diagnosis of rolling bearings

A new depth residual hedging network model is proposed, in which, with the help of Inception stacking idea, a stacked convolution hedging structural block was proposed to accelerate the convergence speed of the network, and a new identity mapping block is designed to realize the residual connection between the input layer and the middle layer. Moreover, the follow-up Squash function is introduced in the full connection layer to prevent the divergence of loss gradient. The depth residual offset network is applied to the fault diagnosis of the rolling bearing. In the preprocessing, the frequency spectrum of the vibration acceleration signal of the rolling bearing is directly taken as the input of the network, thus, simplifying the preprocessing of the data. Finally, two sets of actual rolling bearing failure data are used for methods validation, and to be compared with 18 Layer Residual Networks Deep Residual Networks (Resnet18), Convolutional Neural Networks (CNN) and other verification methods. The results show that of the depth of residual hedge network test accuracy of the model proposed is estimated at 2% more than other models, and training time can be shortened to one third, which fully suggests that the method has strong robustness and fast convergence rate. © 2022 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.