To address the challenges posed by the limited generalization of diagnostic models due to data sparsity in RV reducers, the lack of targeted multimodal fusion strategies, and complex operating conditions, this paper proposes a novel fault diagnosis method based on an enhanced generative adversarial network (GAN) and dual-branch multimodal data fusion. First, an Rotate Vector (RV) reducer fault test bench was established to acquire vibration and current signals. Subsequently, a Wasserstein GAN-residual and attention guidance network, incorporating double-layer residual connections and a multihead self-attention mechanism, was employed for multimodal data augmentation. This approach significantly improves the quality and diversity of generated samples, effectively mitigating data scarcity while ensuring stable training via Wasserstein distance and gradient penalty techniques. The augmented data were then transformed into time-frequency representations using the short-time Fourier transform. Finally, leveraging the global representativeness of vibration signals and the sensitivity of current signals to localized disturbances, an Self-Calibrated Convolution and Vision Transformer Fusion Network for Dual-Modality Classification (SCViT) dual-branch model was developed. This model achieves comprehensive fault diagnosis through multimodal feature fusion. Experimental results demonstrate that the proposed method exhibits superior diagnostic performance under three operating conditions, with diagnostic accuracies of 97.40, 97.83, and 96.54%, respectively. Compared with single-modality diagnosis, the method achieves an average improvement of 3.8 percentage points in diagnostic accuracy. The proposed method maintains high stability and accuracy under loaded conditions and reciprocating motions, providing novel insights for the intelligent maintenance of RV reducers.
Meiwang et al. (Fri,) studied this question.