MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning

Bearing fault diagnosis in actual working conditions often faces the problem that unknown type faults cannot be identified, which seriously restricts the practical application of fault diagnosis technology. To solve this problem, this paper proposes a bearing fault diagnosis method based on transfer learning. Firstly, this paper designs a feature extraction network, the Multi-scale Convolution-Convolutional Reconstruction Network (MCRCNet), which incorporates a multi-scale feature extraction module to extract bearing fault features at multiple scales, thereby enhancing the extraction ability of key information. Secondly, this paper designs an improved convolutional reconstruction module AcConv (Adaptive Convolution reconstruction), which highlights key feature information and reduces redundant features by reconstructing the feature map. Furthermore, this paper also modifies the loss function to improve the performance in the case of data imbalance, and introduces the Wasserstein distance to optimize the adversarial training process. The proposed method is experimentally verified on Case Western Reserve University, Jiangnan University, and laboratory datasets. The experimental results show that the method has good performance in most tasks and has good generalization ability, which provides a feasible solution for the research of bearing fault diagnosis.