PCHT-GAN: Physics-guided adaptive fusion with dynamic low-rank attention for pipeline leak diagnosis under imbalanced data

In industrial pipeline leak detection, the imbalanced data distribution and complex physical mechanisms limit the accuracy and reliability of intelligent diagnostic models. Although existing data augmentation methods expand sample sizes, their inability to incorporate physical constraints results in generated data deviating from leak response patterns. This significantly degrades model generalization and engineering applicability. To address this, this paper proposes a physically coupled hybrid transformer generative adversarial network (PCHT-GAN) framework that deeply integrates physical mechanisms with generative models for physics-informed, high-reliability data generation. First, a physical mechanism model is embedded into the generator, employing a collaborative mechanism prediction-data compensation paradigm to ensure joint physical distribution consistency. Second, to capture leakage signals' long-range spatiotemporal dependencies and transient characteristics, a dynamic low-rank bilinear spatiotemporal transformer (DLR-BiST) is designed. It compresses computational complexity via dynamic low-rank projections while comprehensively retaining critical features through bilinear spatiotemporal attention. Subsequently, a residual-guided attention gate network (ReAG-Net) is proposed that leverages physical residuals to dynamically generate attention weights, guiding the generator to focus on critical physical anomaly regions and perform adaptive compensation. Finally, a multi-task discriminator is designed, featuring parallel constraint branches to simultaneously ensure a balance between distribution and physical consistency in the generated data. Experimental results demonstrate that the data generated by the proposed model significantly outperforms all baseline methods in physical consistency and distribution quality, leading to substantial improvements in the recognition performance of fault diagnosis models.