Graph neural network based on few-shot learning for fault diagnosis of wind turbines with current signal

Abstract Due to prolonged exposure to harsh operational environments, wind turbines tend to experience a high incidence of failure. However, effective fault data are usually hard to obtain in real applications. To remediate this complication, this study introduces a few-shot learning-based graph neural network for fault diagnosis of wind turbines with current signals. Initially, one-dimensional current signals from the generator of the wind turbine are transformed into two-dimensional data using Continuous Wavelet Transform (CWT). Subsequently, features are extracted through Convolutional Neural Network (CNN), which are input into Graph Neural Network Based on Few-Shot Learning (GNN-FSL) for fault diagnosis with a small dataset. Experiments were conducted using real wind turbine data and the public CWRU dataset, where ResNet50, EfficientNet-B0, DenseNet121, CNN, and GNN were selected for comparative evaluation. Under optimal parameter settings, the proposed model achieved superior performance compared to the baseline models. The results demonstrate that the proposed method outperforms all previously mentioned approaches.