Few-Shot Cross-Domain Fault Diagnosis of Transportation Motor Bearings Using MAML-GA

The dynamic working conditions and scarcity of fault samples in train motors pose significant challenges to the generalization capability of diagnostic models. To address this issue, this paper proposes a cross-domain fault diagnosis method with strong generalization capability under few-shot conditions, termed MAML-GA. In the meta-task construction phase, a dynamic meta-task augmentation mechanism is introduced within the meta-learning framework to alleviate the problem of limited data by expanding the sample space and generating virtual meta-tasks. During the training phase, a parameter update operator is designed to regulate the inner-loop gradient descent process, thereby optimizing the training strategy and preventing the model from overfitting to noisy or irrelevant features. These two mechanisms significantly enhance the cross-domain generalization ability of the diagnostic model and reduce the risk of overfitting under few-shot scenarios. Furthermore, a lightweight key feature extraction network is developed as the backbone of the proposed method. This network integrates a multi-dimensional spatial-channel attention module, which improves the extraction of discriminative fault features under both limited data and dynamic working conditions. Experimental validations conducted on both a metro transmission system dataset and a laboratory dataset demonstrate that the proposed method achieves diagnostic accuracies of 99.46% and 97.42%, respectively. Compared with mainstream methods such as ProtoNet and RelationNet, the proposed method shows improvements of 2–6% and 6–15%, respectively, thereby confirming its effectiveness in handling variable rail transit conditions and data scarcity.