A Small-Sample Interturn Short-Circuit Diagnostic Framework Based on Gradient Spectral Generative Adversarial Network and Graph Attention

Deep learning (DL) technology based on large-scale labeled data is increasingly used to diagnose permanent magnet synchronous motor (PMSM) interturn short-circuit (ITSC) faults. However, the presence of small fault samples context and the large amounts of unlabeled data are challenging problems in real-world industrial applications. To address these challenges, we propose an optimized Lipschitz generative network-gradient spectral generative adversarial network (GSGAN) within the semi-supervised mechanism. Then, we incorporate the graph attention mechanism to achieve accurate fault severity diagnosis. For the specific, first, the GSGAN utilizes gradient and parameter spectral information to strictly enforce the Lipschitz condition of the generative network for generating realistic-like ITSC fault data. Second, the semi-supervised mechanism enhanced by a temporal causal sparse fusion graph attention neural network (TCSFGAT) is proposed. The TCSFGAT obtains multisample level adjustable fault feature information, which can clearly represent the graph features in temporal PMSM current fault information. In addition, TCSFGAT can effectively mitigate the impact of pattern confusion caused by mislabeled pseudo-labels in the testset diagnostic process. Finally, based on the real industrial environment characterized by high temperatures and strong vibrations, experiments were conducted under 13 specific load and speed conditions to obtain raw fault data. Experimental results indicate that the proposed framework achieves a remarkable 98.7% fault diagnosis accuracy.