VMD-Assisted DGCM-Net: A Dual-Representation Cross-Interaction Network with Multi-SNR Training for Transformer Core Looseness Diagnosis

Vibration-based diagnosis can identify transformer core looseness non-intrusively, but subtle vibration differences and noise-disturbed Gramian angular field (GAF) textures reduce diagnostic accuracy at low signal-to-noise ratios (SNRs). This paper proposes a variational mode decomposition (VMD)-assisted dual-representation cross-interaction network (DGCM-Net) with multi-SNR training. VMD parameters are optimized by particle swarm optimization (PSO), and signals reconstructed from selected modes are encoded as paired Gramian angular summation field (GASF) and Gramian angular difference field (GADF) images. Two independent DenseNet121 branches extract features from the two representations. CrossGAF enables cross-branch interaction before fusion, while the residual bottleneck multi-scale selective kernel (RBMSSK) module processes fused features at different scales. Across five independent runs, the proposed method achieves 99.84 ± 0.11% accuracy under the standard condition and 97.39 ± 0.23% mean accuracy across four noisy test sets, with 91.32 ± 0.62% accuracy at 10 dB. On a public 50 kVA transformer dataset, retraining yields 98.20 ± 0.65% accuracy at 2.5 dB. In cross-transformer few-shot adaptation, using 5% of the target-domain training samples increases the mean accuracy across four noisy conditions from 93.63% to 97.16% over target-only training.