Defect Identification Method of Cable Termination Based on Improved Gramian Angular Field and ResNet

This paper proposes a defect identification method for vehicle-mounted cable terminals in electric multiple units (EMUs) based on the improved Graham angle field and residual network to address the issue of low recognition accuracy caused by the lack of partial discharge (PD) and identification data for Ethylene Propylene Rubber (EPR) cable terminal defects. The improved Gramian angular field (IGAF) characteristic transformation method was used to transform the PD one-dimensional time-series signal into a two-dimensional one after cable terminals with four common insulation defects were constructed, and a PD detection platform was built. Finally, an anti-aliasing downsampling module and attention mechanism were added to the residual network ResNet101 model. The Center loss and Softmax loss functions were integrated to increase accuracy for training and recognition classification. Topological feature images improved the distinguishability of defect categories. The test results showed that the diagnostic method has an accuracy rate of 97.3% for identifying PD at the cable terminal. The proposed diagnosis model has higher recognition accuracy and better balance than other conventional fault diagnosis methods, making it suitable for diagnosing high-voltage cable faults in EMU trains.