LCDMAML: A novel cross-domain fault diagnosis model for rolling bearings based on meta learning

Abstract Despite remarkable advancements in few-shot transferable fault diagnosis, most studies remain restricted to homogeneous signals; meanwhile, fault diagnosis methods have grown increasingly complex to ensure robust transfer performance, imposing higher computational demands. To address these issues, this paper proposes the light cross domain model-agnostic meta-learning for bearing few-shot transferable fault diagnosis. The method constructs a hierarchical interactive feature encoder based on cross-layer channel attention, which breaks single-layer perspective limitations, extracts complementary channel features, and enhances generalization—meeting heterogeneous signal diagnosis needs in few-shot transfer scenarios. Additionally, replacing fully connected layers with GAP modules reduces model size and improves computational efficiency. Validation using bearing vibration and acoustic signals across two datasets confirms the method’s effectiveness.