Evaluation Method for Contamination Level of the Onboard Insulators in HSTs Using Leakage Current Monitoring and Optimized SDP Images

Accurately determining the contamination level (CL) of on-board insulators is crucial for ensuring the safe and stable operation of high-speed trains (HSTs). This study proposes an evaluation method using leakage current (LC) monitoring and optimized symmetrized dot pattern (OSDP) images to accurately and automatically detect the CL of on-board insulators in HSTs. Firstly, insulator samples and an LC measurement platform were prepared in the laboratory to obtain time-series LC signals under five different CLs. Then, the encoding principle of the OSDP was proposed, which involves integrating effective multimodal components derived from the decomposition of LC signals using the ensemble empirical mode decomposition (EEMD) algorithm into a polar coordinate system. This method effectively emphasizes the multiscale characteristics of LC signals, significantly enhancing data visualization. Finally, the OSDP images were integrated with four typical deep convolutional networks (DCNs) models to evaluate the CL of the insulators. The results indicate that OSDP images combined with the ResNet-18 model achieve the highest performance in CL evaluation, with a recognition accuracy of 98.5%, which is 3.8% higher than the accuracy of traditional SDP images. Additionally, an online monitoring device for LC signals was developed to facilitate the application of the proposed technique to actual on-board insulators in HSTs.