Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains

Abstract

Intelligent fault detection and diagnosis, as an important approach, play a crucial role in ensuring the stable, reliable and safe operation of rolling bearings, which is one of the most main components in the rotating machinery. However, the data distribution shift is inevitable in the practical scene due to changes in internal and external environments, it is still challenging to establish an effective fault diagnosis model that can eliminate the same distribution assumption. In light of the above demands, a novel transfer learning framework based on deep multi-scale convolutional neural network (MSCNN) is presented in this paper. First, a novel multi-scale module is ingenious established based on dilated convolution, which is used as the key part to obtain differential features through different perceptual fields. Then, in order to further reduce the complexity of the proposed model, a global average pooling technology is adopted to replace the traditional fully-connected layer. Finally, the architecture and weights of the MSCNN pre-trained on source domain are transferred to the other different but similar tasks with proper fine-tuning instead of training a network from scratch. The proposed MSCNN is evaluated by different transfer scenarios constructed on two famous rolling bearing test-bed. Three case studies show that the proposed framework not only has excellent performance on the source domain, but also has superior transferability on variable working conditions and domains.