Partial discharge detection is considered a crucial technique for evaluating insulation performance and identifying defect types in cable terminals of high-speed electric multiple units (EMUs). In this study, terminal samples exhibiting four typical defects were prepared from high-speed EMUs. A cable discharge testing system, utilizing high-frequency current sensing, was developed to collect discharge signals, and datasets corresponding to these defects were established. This study proposes the use of the convolutional neural network (CNN) for the classification of discharge signals associated with specific defects, comparing this method with two existing neural network (NN)-based classification models that employ the back-propagation NN and the radial basis function NN, respectively. The comparative results demonstrate that the CNN-based model excels in accurately identifying signals from various defect types in the cable terminals of high-speed EMUs, surpassing the two existing NN-based classification models.

Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals.

This paper presents a machine learning-based approach to identify and separate partial discharge (PD) and two typical pulse interference (PI) signals in the vehicle-mounted cable terminations of high-speed electric multiple units (EMUs). First, a test platform was established to capture PD and two typical PI signals in these terminations. The acquired signals were then processed using the square envelope method to extract feature parameters, such as the rise time proportion, the left–right symmetry, and the upper–lower symmetry. PD signal classification was carried out on these signals, utilizing waveform parameters derived from a hierarchical clustering algorithm. The results validate that the extracted feature components effectively classify and separate PD and two typical PI signals in the vehicle-mounted cable terminations of high-speed EMUs.

The detection of defect severity in cable terminations plays a critical role in ensuring the safe and stable operation of high-speed trains (HST). However, the partial discharge (PD) characteristics of the same type of defect can appear similar across different severities, posing challenges for accurate insulation defect degree identification. Consequently, this paper proposes an image transformation method, named the equivalent bandwidth matrix of relative locations (EBMRL), coupled with the Self-guided Transformer (SG-Former) algorithm, which is more effective for fine-grained image recognition, to accurately identify different degrees of defects with similar PD characteristics. In the proposed approach, the original PD signals are first converted into images using EBMRL. This transformation embeds the characteristic and bandwidth information from the original PD data into the images, thereby reducing the similarity of information between classes in the transformed images and enhancing their distinguishability. Subsequently, local and global features of the transformed EBMRL images are extracted to train the SG-Former model. The model is finally utilized to identify the severity of defects in cable terminations. The results demonstrate that the method proposed in this paper achieves better performance compared with some of the state-of-the-art methods.

The air gap at the interface inside the cable terminations for high‐speed trains and the partial discharge caused by it are two important factors affecting the insulating performance. The development of air gap and partial discharge will eventually lead to breakdown faults. To investigate the evolutionary characteristics of the air gap and partial discharge, the simulation models and samples of cable terminations containing defects are constructed in this paper. By analysing the variation law of the electric field and the multidimensional information of partial discharge, the evolution process of the air gap is divided into four well‐characterised stages. Especially in the third stage, the partial discharge extinction voltage is 51.41% lower than that of the defect‐free samples and even lower than the working voltage. The asymmetry of discharge is the most significant factor. The volume of discharge in the third quadrant is significantly higher than that in the first quadrant. This important feature can be applied to the inspection and evaluation of the insulating state of the cable terminations. The partial discharge characteristics of the air gap revealed in this paper are proposed to provide an important theoretical supplement to the study of interface discharges between heterogeneous dielectrics.

Background: 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. Methods: 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. Results: The test results showed that the diagnostic method has an accuracy rate of 97.3% for identifying PD at the cable terminal. Conclusion: 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.