Fault diagnosis of rolling bearing based on online transfer convolutional neural network

In order to achieve online fault diagnosis of rolling bearing effectively, this paper proposes a rolling bearing fault diagnosis model based on online transfer convolutional neural network (OTCNN). Firstly, offline convolutional neural network (Off-CNN) and online convolutional neural network (On-CNN) with the same model structure are constructed, and multi-channel data fusion and gray image conversion are used as the input of the model. Then, the source domain features in the fully connected layer and the model parameters are obtained by the pre-trained Off-CNN. Finally, the parameters of the On-CNN are initialized by the parameters of the Off-CNN, and the pre-trained source domain features can be used to achieve domain adaptation. A comprehensive analysis with the traditional algorithms is also performed, the results demonstrate that the proposed model can reduce the training time by half while ensuring the accuracy of it.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a rolling bearing fault diagnosis model based on online transfer convolutional neural network (OTCNN) to achieve effective online fault diagnosis. The model combines offline and online convolutional neural networks and utilizes feature transfer to optimize the accuracy of fault diagnosis and reduce training time.