Few-Shot Bearing Fault Diagnosis Based on ALA-FMD and MSCA-RN

To address the challenges associated with insufficient bearing fault feature extraction under small sample sizes and variable working conditions, as well as the limited generalization capability of diagnostic models, this paper proposes an intelligent diagnostic method that integrates an Artificial Lemming Algorithm (ALA) for feature mode decomposition parameter optimization (ALA-FMD) with a multi-scale coordinate attention relation network (MSCA-RN). This method employs the ALA to dynamically adjust the model’s parameter optimization strategy, effectively balancing global exploration and local exploitation capabilities. It optimizes the parameters of the feature mode decomposition algorithm to enhance decomposition accuracy, utilizing the minimum residual index as the selection criterion for optimal modal components, thereby facilitating signal denoising. Subsequently, the optimal components are transformed into time–frequency maps. Through a multi-scale coordinate attention (MSCA) mechanism, the global energy distribution and local fault texture features of the bearing vibration signal’s time–frequency maps are captured in parallel. Coupled with the nonlinear metric capability of a relation network (RN), this method enables the discrimination of fault sample similarity, thus improving model robustness under small sample conditions. Experimental results obtained from the Case Western Reserve University (CWRU) bearing dataset under small sample sizes and variable operating conditions demonstrate that the proposed method achieves a maximum accuracy of 96.8%, with an average accuracy of 92.83% on the test data. These results indicate the method’s superior classification capability in the domain of bearing fault diagnosis.