## 5 Conclusions  
This paper presents a method for classifying discharge signals based on WT-DBN. A platform for detecting partial discharge in vehicle-mounted cable termination, based on the HFCT method, has been developed in the laboratory, employing the WT-DBN method presented in the text for classification. The study explores the impact of different optimizers, wavelet analysis denoising algorithms, and quantities of training datasets on model accuracy. The proposed method's practicability is confirmed through the identification of raw signal detection in the field environment. The main points are summarized as follows:  
1. When selecting the sigmoid function as the activator for training the model, the average loss is low, the average accuracy is higher, and the training model is much more stable.
2. The WT-DBN presented in this study is compared with DBN in terms of classification effect, where wavelet transform eliminates white noise interference from the raw signals.
3. The evaluation of WT-DBN's classification effectiveness with varying quantities of training dataset shows that increased dataset for training improves classification precision. When the quantity of the training dataset amounts to 30 instances, the accuracy can reach over 94%. This enhances the usability of the input dataset, making the model more flexible for various situations.
4. By comparing the three methods of WT-DBN, WT-BP, and WT-SVM, the proposed WT-DBN achieves a recognition accuracy of 98.75% compared to the other methods.
5. By training the WT-DBN model with 150 sets of standard signals, the model is used to identify mixed signals, effectively recognizing partial discharge signals and partial discharge mixed with corona interference and removing corona discharge.  
The method presented in the text circumvents the conventional process of manually extracting features in machine learning, achieving high accuracy in classifying partial discharge and corona discharge and can also classify incomplete signals with high accuracy. This forms the basis for eliminating impulsive jamming in the partial discharge signals of vehicle-mounted cable termination.  
Conceptualization, Z.L.; methodology, J.L.; software, T.Z.; validation, S.C., G.G. and G.W.; formal analysis, Y.-C.L.; investigation, C.S.; resources, K.L.; data curation, K.C. and D.X.; writing--original draft preparation, J.L.; writing--review and editing, Y.-C.L. and T.Z.; visualization, G.G.; supervision, G.W.; project administration, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (2022 YFB4301202-17), National Natural Science Foundation of China (U1966602, 52377161, 52007158),  
Figure 15: Raw signal recognition outcomes founded on WT-DBN.  
Excellent Young Scientists Fund of China (51922090), Southwest Jiaotong University New Interdisciplinary Cultivation Project (YH1500112432273 and YH15001124322105), and Fundamental Research Funds for the Central Universities (A0920502052301-170).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available (subject to applicable restrictions) on request from the authors.  
**Conflicts of Interest:** [PERSON] is employed by CRRC Changchun Railway Vehicles Co., Ltd., and [PERSON] by CRRC Qingdao Sifang Co., Ltd. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.