A noisy small-sample fault diagnosis method based on dynamic weighted contrastive learning using multisource signals
Industrial equipment fault diagnosis faces significant challenges including severe noise interference, scarcity of annotated data, and inadequate model generalization in small-sample scenarios. Traditional transfer learning methods are limited by known fault information and categories, hindering accurate identification of all faults in target domains and compromising diagnostic performance. To address these issues, this paper proposes a Multi-source Dynamic Contrastive Learning (M-DCL) approach for noisy small-sample fault diagnosis. The framework first designs a heterogeneous temporal encoder that adapts to temporal characteristic differences between current and vibration signals using large/small convolutional kernels respectively, while incorporating channel attention to enhance critical fault features. It then constructs a time-frequency dual-domain physical fusion module that integrates spatiotemporal attention and frequency-domain gating strategies to achieve noise separation and effective feature enhancement. Finally, a Dynamic Contrastive Learning (DCL) is introduced, leveraging noise-aware dynamic weighting and multi-scale feature regularization to boost diagnostic capability against small samples and noise. Experimental results demonstrate the method's effectiveness in bearing fault diagnosis under varying operating conditions.