An optimized method for few-shot cross-domain bearing fault diagnosis based on intraclass relationship enhancement
Deep learning has led to notable progress in fault diagnosis. However, many approaches still rely on the availability of abundant fault samples and on matched train-test distributions, conditions that are seldom met in industrial settings. Although model-agnostic meta-learning (MAML) enables rapid adaptation with limited labeled data, existing fault diagnosis methods based on MAML generally lack explicit modeling of intra-class sample relationships and commonly employ a fixed inner-loop update strategy. To address cross-domain diagnosis with few samples, we enhance MAML by incorporating a Sample Relationship Exploration module that learns intra-class similarity to improve class separability. Specifically, the Sample-level Attention component discovers task-specific affinities, the Explicit Guidance component provides an ideal affinity map to supervise similarity learning, and the Channel-wise Adaptive Fusion component fuses original features with category-aggregated features for classification. In addition, to mitigate overfitting, we replace the fixed inner-loop update scheme in MAML with a trapezoidal gradient descent scheduler that adapts the number of inner-loop update steps across training. Experiments on public and self-collected datasets under multiple cross-domain few-shot settings demonstrate that the proposed method achieves better or more stable diagnostic performance in most evaluated settings.