A Motor Fault Diagnosis Framework With Vibration--Current Multimodal Contrastive Learning for Cross-Device Scenarios

Fault diagnosis in industrial environments is often constrained by the scarcity of labeled data, variability of operating conditions and cross-device deployment. Moreover, relying solely on vibration signals results in limited diagnostic accuracy and robustness for motors under complex working conditions. To address these challenges, this paper proposes a novel self-supervised motor fault diagnosis framework based on vibration–current multimodal contrastive learning, in which current features are innovatively incorporated into the contrastive learning paradigm. Specifically, a vibration–current multimodal contrastive learning is first developed to enhance discriminative representation learning while improving cross-modal feature consistency, thereby enabling deeper semantic interaction and complementary information fusion between different modalities. Subsequently, to address the adaptive feature encoding of multimodal signals, a dynamically weighted multi-scale residual encoder is constructed, which effectively extracts heterogeneous modal features by adaptively adjusting the importance of multi-scale representations. Finally, the effectiveness of the proposed framework is validated through three case studies. Experimental results demonstrate that an average diagnostic accuracy of 97.75% was achieved across all cross-device tasks using only 5% labeled samples. In addition, the proposed model contains 11.89M parameters and requires only 2.58s for fine-tuning, highlighting its robustness, reliability, and strong application potential in label-limited industrial scenarios.