Research on fault diagnosis method of vehicle cable terminal based on time series segmentation for graph neural network model

In the field of partial discharge (PD) pattern recognition for vehicle cable terminals, the existing recognition methods often lead to reduced accuracy due to inadequate time–frequency features. This study introduces a novel method for time sequence segmentation to construct graph signals. Additionally, we flexibly integrate the graph self-attention convolution layer (GAT) and the self-attention graph pooling layer (SAG) to build a diagnostic model, allowing for robust feature extraction through multiple attention heads of GAT and effective integration of global features via the SAG pooling layer. High-frequency pulse current was utilized for PD testing on four defect models, with subsequent evaluation of outcomes. Furthermore, examinations of cable terminations in real trains provide further support for our approach by improving recognition accuracy and enhancing train operational stability.