DSWT-GAN: A dual-stream Wasserstein transformer generative adversarial network for composite fault data augmentation of aero-engine gas path

The composite fault data of aero-engine gas paths generally exhibit significant multiple correlations and couplings while confronting practical challenges, including sample scarcity, imbalanced distribution, and intense noise interference. Existing methods often fail to fully capture the deep-level feature information of raw data, leading to generated data that not only deviates from the raw data but also retains the inherent coupling characteristics, thereby limiting the accuracy of fault diagnosis models trained on such data. To address these issues, a dual-stream Wasserstein transformer generative adversarial network model is proposed for composite fault data augmentation. This model effectively extracts local detailed features and global distribution patterns from raw data through an elaborately designed dual-stream architecture and realizes adaptive fusion of these two feature components via a dedicated gating mechanism. Furthermore, the Wasserstein distance with gradient penalty is introduced into the adversarial training process, which significantly enhances the stability and convergence of model training. Experimental results demonstrate that the data generated by the proposed method are highly consistent with the raw data in terms of distribution characteristics while achieving effective decoupling of composite fault features, thereby significantly enhancing the performance of fault diagnosis models trained on the generated data. In particular, the area under the curve (AUC) of the convolutional neural network (CNN) model reaches 99.07%. Moreover, under conditions of an extremely small sample size, severely imbalanced data distribution, and extremely high noise intensity, the AUC of the CNN model decreases by merely 0.92%, 1.15%, and 0.42%, respectively.