CVC-Net: A Cross-View Consistency Network for Noise-Generalization Fault Diagnosis

Deep learning applications in fault diagnosis face two critical challenges. First, a significant distribution gap between source domain training data and target domain samples with unknown noise patterns. Second, labeled fault data remain scarce in practice. These issues hinder the practical deployment. This paper presents a Cross-View Consistency Network (CVC-Net) to tackle these problems through noise-generalization capabilities. The method learns robust features from limited source domain data. It maintains diagnostic accuracy with unknown noise data, without prior knowledge of target noise characteristics. CVC-Net processes temporal waveforms and Gramian Angular Field representations through specialized encoders, exploiting their asymmetric noise sensitivities. A cross-view consistency mechanism extracts fault patterns across modalities. The method integrates fault-aware prototype learning for enhanced discrimination with limited labels and employs adaptive fusion that weights view contributions based on cross-view prediction. Experimental validation shows that CVC-Net is effective in challenging scenarios. When tested on target domain with unknown noise types, CVC-Net maintains reliable performance, effectively handling noise patterns not present during source domain training. Under limited-label conditions, it outperforms existing methods in diagnostic performance.