Research on rolling bearing fault diagnosis of small dataset based on a new optimal transfer learning network

Abstract

Due to limited conditions of production sites, only the small fault dataset (target dataset) of the rolling bearing can be collected, which leads to the failure construction of the effective deep learning network. Aiming at the above problems, the sufficient fault dataset (source dataset) of other type of rolling bearing is introduced as the auxiliary, and thus a new transfer learning network based on convolutional neural network (CNN) is proposed. The new transfer learning network is with a new structure, and it is trained by a new training strategy, and then it is optimized by a new optimal fusion method of dropout layer 4 and L2 regularization. The measured fault signals of the rolling bearings are tested and verified, and results demonstrate that the proposed transfer learning network has low computation cost, high accuracy and strong diagnosis ability. Furthermore, it performs much better than the traditional transfer learning networks.