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

•A novel method for constructing weighted graph signals by time division.•Improved detection rate for cable terminals of different defect states with SAG-GAT.•New state detection method of cable terminal to ensure safe transport. 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, the testing of cable terminals on real trains provides further validation for our approach. The research indicates that this approach not only enhances recognition accuracy but also demonstrates strong recognition performance with limited sample sizes.