LAUSR

The existing deep learning based bearing fault diagnosis models often adopts the default numeric value or manual adjustment for the learning rate of the optimizer, which leads to the low adaptive performance of the diagnosis model and can not adapt to the changeable working conditions of bearing fault diagnosis; Moreover, the trained model may not be used in other scenarios due to the difference of data distribution, and the learning rate of the optimizer has to be manually adjusted again. An adaptive resource allocation depth neural network (PDC-LR-HCNN) is proposed, which is composed of improved extended convolution, Lookahead-Radam (LR) optimizer and hyperband regulator. Firstly, LR optimizer is used to update parameters instead of Adam optimizer; Secondly, the superposition structure of expanded convolution is improved, and the convolution layers with expansion rates of 1, 3 and 5 are used for superposition, and an expanded convolution module based on the characteristics of human receptive field is proposed; Finally, the hyperband regulator is used for adaptive resource allocation to speed up the random search and dynamically find the optimal learning rate of the optimizer, which solves the disadvantage that manually adjusting the learning rate of the optimizer is time-consuming, laborious and not optimal. Through the experimental data sets of CWRU and XJTU-SY bearings, the proposed PDC-LR-HCNN neural network model is verified. The accuracy can reach more than 90%, and the classification accuracy convergence can be achieved within 10 iterations, which verifies the effectiveness of the model.