Signal Feature Extract Based on Dual-Channel Wavelet Convolutional Network Mixed With Hypergraph Convolutional Network for Fault Diagnosis

Graph neural networks demonstrate effectiveness in fault diagnosis (FD) due to their capability to handle complex dependencies and nonlinear mappings. However, current learning methods relying on simple graphs fail to fully exploit the representation power between nodes when encountering fault data in time series. Moreover, the information extraction capacity of high-order neighbors is limited. Existing approaches exhibit suboptimal performance when confronted with noise interference and a scarcity of training data. To tackle these challenges, we propose a signal feature extraction method based on a dual-channel wavelet convolutional network and hypergraph convolutional network (WC-HGCN) for fault-type classification. In WC-HGCN, fault data are represented as a hypergraph, enabling the learning of intricate high-order relationships between data through hypergraph convolution. We use wavelet convolution to extract sample trend and noise information, which can effectively solve the problem of noise interference and small samples and increase the robustness of the model. Finally, we evaluate WC-HGCN on publicly available FD datasets, and the results demonstrate its state-of-the-art performance.