STSNet: few-shot bearing fault diagnosis method based on spatio-temporal fusion and Siamese learnable metric
In response to the challenges of sparsely labeled samples and complex feature distribution in bearing fault diagnosis, a novel few-shot bearing fault diagnosis method is proposed by integrating spatio-temporal feature fusion with a Siamese-based learnable metric framework. A multi-scale residual network-Convolutional Block Attention Module network is adopted as the feature extractor, where channel-space attention enhancement and multi-level feature fusion are employed to improve the effective representation of spatio-temporal information in the time–frequency diagrams. To overcome the limitations of fixed distance metrics, a Siamese learnable metric network is introduced to enable adaptive similarity modeling. In addition, a local-global dual-branch fusion mechanism is designed to combine local similarity metrics with global context matching information, thereby enhancing the model’s robustness and generalization performance in few-shot scenarios. Experiments conducted on the Case Western Reserve University and Intelligent Maintenance Systems bearing datasets show that the proposed method achieves superior diagnostic performance under few-shot conditions compared with other approaches. The visualization results using confusion matrices and t-SNE further confirm the method’s strong interclass separability.