A Self-Supervised Evaluation Approach of Insulation Condition for Vehicle Cable Terminals Using Hypergraph Neural Network With Dynamic Features

High-speed electric multiple units (EMUs) play a pivotal role as the vital arteries of the national economy, essential components of major public infrastructure projects, and the fundamental backbone of a nation’s comprehensive transportation network. Current detection methodologies struggle to achieve effective online monitoring of cable terminal insulation due to challenges in extracting dynamic temporal signal features and insufficient annotated datasets for pattern recognition. A self-supervised evaluation method of insulation state for vehicle cable terminals based on the hypergraph neural network with dynamic features is proposed in this article. Initially, a novel method for constructing a graph structure with spectral differentiation is introduced to characterize the dynamic features and deterioration trends of the insulation state in vehicle cable terminals. Subsequently, to address the general problem of limited labels on-site, the defined hypergraph neural network is embedded to get the self-supervised hypergraph neural network. Furthermore, the fine-tuned hypergraph model is initialized with partial parameters derived from the embedded self-supervised pretrained model. Ultimately, the proposed approach is experimentally verified by vehicle cable terminal HFCT experiments. The proposed method demonstrates robust feature representation and generalization capabilities for assessing the insulation of vehicle cable terminals. It achieves an accuracy of up to 99.01%, providing a reliable framework for intelligent fault diagnosis in high-speed EMU traction power supply systems and industrial predictive maintenance.