A novel multi-sensor fusion method for diagnosing insulation defects in gas-insulated substations guided by adaptive-attention and contrastive-based few-shot learning

Deep learning based multi-source fusion has shown significant potential in diagnosing insulation defects in gas-insulated switchgear (GIS). However, its applicability in real engineering scenarios remains limited. Existing fusion frameworks struggle to model the heterogeneous sensing characteristics of optical and electrical channels, often relying on rigid or shallow interaction schemes that fail to capture modality complementarity. In addition, field data are typically scarce and distribution-shifted, making it difficult for conventional models to learn discriminative and generalizable features under small-sample conditions. To address these challenges, we propose a novel multi-sensor fusion few-shot learning network (MSFFLN) for GIS insulation defect diagnosis. First, a deep fusion network is developed to construct comprehensive representations of insulation defects. Specifically, a feature weighting fusion module is employed to improve robustness, while an adaptive attention-based fusion block suppresses redundant and aliased information, emphasizing the most discriminative features. Second, a contrastive learning-based few-shot strategy is introduced. By computing global and local contrastive losses and using contrastive learning as an auxiliary task, the model learns more accurate and generalizable feature representations. In addition, salient region mixing across samples is applied to decouple class-level and instance-level feature correlations. Finally, field experiments validate the effectiveness of the MSFFLN. Results show that the MSFFLN achieves a diagnostic accuracy of 95.06% with only 10 support samples, significantly outperforming baseline and ablation models in small-sample GIS insulation defect diagnosis.