###### Abstract  
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. electric multiple unit; high-speed railway; cable termination; partial discharge; corona discharge; wavelet transform; deep belief network +
Footnote †: journal:  
There are three kinds of common interference signals, which are white noise interference, periodic narrowband interference, and random pulse interference [23; 24]. Domestic and foreign scholars have conducted a lot of research on filtering white noise and periodic narrowband interference, but the research on filtering random pulse interference is relatively scarce [23; 24; 25; 26; 27]. [PERSON] [28] proposed that the advantages of the recursive continuous S-type algorithm are integrated into the adaptive noise cancellation system, which enhances the filtering performance of the system and can solve the problem of partial discharge noise in Gaussian and pulse scenarios at the same time. [PERSON] [29] proposed the use of particle swarm optimization algorithms to optimize variational mode decomposition, adaptively breaking down the original partial discharge signals into a series of narrowband modes. A decision principle based on kurtosis is implemented to identify the pertinent components associated with partial discharge. Subsequently, residual white noise is reduced using wavelet transforms. [PERSON] [30] presented a method founded on maximum overlap discrete wavelet conversion for the purpose of denoising partial discharge signals generated by power cable defects contaminated with noise at different levels, achieving good denoising results. [PERSON] [31] utilized adaptive noise complete ensemble empirical mode decomposition to decompose power cable signals and used singular value decomposition to suppress frequency aliasing in the acquired components. Affected by the complex operating environment of the train, different types of faults will occur in the cable terminal of the vehicle, and the discharge caused by the surface dirt of the cable terminal is corona discharge. As an interference signal, the presence of corona discharge will affect the diagnosis of cable terminal insulation faults. The realization of accurate identification of different discharge signals of cable terminals is helpful to obtain accurate partial discharge signals of cable terminals and to diagnose the process of internal fault occurrence in cable terminals [32; 33; 34]. With rapid developments in artificial intelligence (AI), its techniques are now widely used in electrical and energy systems [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. These AI-related techniques include not only advanced methods but also foundational data analysis methods, which are integral to developing AI-powered pattern recognition methods. Researchers have explored the development of these comprehensive AI-powered methods for detecting and classifying partial discharge signals. These methods include those based on correlation coefficients [47; 48], cluster analysis [49; 50; 51], support vector machines [52; 53; 54; 55; 56], and neural networks [49; 57; 58; 59; 60; 61; 62; 63], among others.