Fault diagnosis of wind turbine with few-shot learning based on acoustic signal

Due to the harsh working conditions of wind turbines, it is difficult to obtain sufficient fault data from the gearbox. To address this issue, this study proposes a graph neural network model based on few-shot learning (GNN-FSL) for the fault diagnosis on planetary gears of wind turbine gearbox with acoustic signals. The short-time Fourier transform (STFT) is chosen for preprocessing of acoustic signals as input data, which converts the raw data into two-dimensional data. Then, convolutional neural networks (CNN) are used to extract data features. Finally, the extracted features are input for the graph neural network for fault classification. The experimental results show that the model performs well in fault classification on small sample datasets, providing an effective method for fault diagnosis of planetary gears in wind turbines. By comparing EfficientNet-b0, ResNet-50, Densenet121, Mobilenet_v2 and DiffKendall models, it is verified that the proposed method is effective in planetary gear fault classification, and its performance is superior to all these networks.