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Cable terminal defect detection plays an important role in ensuring the safe and stable operation of high-speed trains. In this paper, a numerical model of the electromagnetic thermal field of overheating defects of cable terminal shielding grids and a method of detecting internal defects of cable terminals—even-order temperature derivative is proposed for quantitative detection of internal defective structures of vehicle-mounted cable terminals. Firstly, a numerical model of the electromagnetic thermal field of cable terminals under the condition of leakage current is constructed, through which the temperature field distribution characteristics of different defective structures are analyzed. Then, the intuitive location of the defective region is obtained by investigating the derivative characteristics of the temperature image, and the depth and intensity of the defects are quantitatively assessed by using the main side peak (MSP) distances and the MSP values extracted from the derivative curves. Finally, the simulation and experimental results achieve the identification of defect structures and the quantitative detection of defect depth and intensity, proving the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Thermal defects widely occur in electrical equipment and affect its stable operation. This paper proposes a novel detection method based on derivatives for thermal defects of electrical equipment. First, the equivalent models of the equipment are established to analyze the surface temperature formed by different defects. Second, the theoretical foundation of the derivative-based detection method is described, and numerical calculations on thermal defect detection based on derivative characteristic curves are carried out. The results show that the method can detect the position effectively and calculate the depth with an error of 1.6%. Then, the thermal defects in different situations are analyzed, and the results indicate that the method is effective in all cases. Finally, the experiment is conducted by artificially preparing samples with thermal defects, and the achieved calculation error of the experimental results is within 3.3%, which indicates the validity of this method. [ABSTRACT FROM AUTHOR]