Deep Residual Network Combined with Transfer Learning Based Fault Diagnosis for Rolling Bearing

Abstract

Fault diagnosis of rolling bearings is significant for mechanical equipment operation and maintenance. Presently, the deep convolutional neural network (CNN) is increasingly used for fault diagnosis of rolling bearings, but CNN has challenges with incomplete training and lengthy training times. This paper proposes a residual network combined with the transfer learning (ResNet-TL) based diagnosis method for rolling bearing, which can preprocess the one-dimensional data of vibration signals into image data. Then, the transfer learning theory in parameter transfer is applied to the training of the network model, and the ResNet34 network is pre-trained and re-trained; the image data are selected to be the inputs of the fault diagnosis model. The experimental validation of the rolling bearing fault dataset collected from the practical bench and Case Western Reserve University shows the superiority of the ResNet34-TL model compared with other classification models.