Machine learning for fault diagnosis of high-speed train traction systems: A review

Abstract

High-speed trains (HSTs) have the advantages of comfort, efficiency, and convenience and have gradually become the mainstream means of transportation. As the operating scale of HSTs continues to increase, ensuring their safety and reliability has become more imperative. As the core component of HST, the reliability of the traction system has a substantially influence on the train. During the long-term operation of HSTs, the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures, thus threatening the running safety of the train. Therefore, performing fault monitoring and diagnosis on the traction system of the HST is necessary. In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis. Machine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensive systematic review is still lacking in this field. This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint. First, the structure and function of the HST traction system are briefly introduced. Then, the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed. Finally, the challenges for accurate fault diagnosis under actual operating conditions are revealed, and the future research trends of machine learning in traction systems are discussed.

Graphical abstract

Keywords

high-speed train / traction systems / machine learning / fault diagnosis

References

[1] AydinIKarakose MAkınE (2014). Monitoring of pantograph–catenary interaction by using particle swarm based contact wire tracking. In: International Conference on Systems, Signals and Image Processing. Dubrovnik: IEEE, 23–26

[2] Aydin, I Karakose, M Akin, E (2015). Anomaly detection using a modified kernel-based tracking in the pantograph–catenary system. Expert Systems with Applications, 42( 2): 938–948

[3] Bacha, K Souahlia, S Gossa, M (2012). Power transformer fault diagnosis based on dissolved gas analysis by support vector machine. Electric Power Systems Research, 83( 1): 73–79

[4] BiSFengD LinSGuoX PanW (2020). State evaluation method of traction transformer based on variable weight coefficient and Bayesian network. In: 11th International Conference on Prognostics and System Health Management. Jinan: IEEE, 163–168

[5] BrahimiMMedjaher KLeouatniMZerhouniN (2016). Development of a prognostics and health management system for the railway infrastructure: Review and methodology. In: Prognostics and System Health Management Conference. Chengdu: IEEE, 1–8

[6] CaoJCuiH LiN (2014). Research on fault detection method and device of EMU traction motors. In: International Conference on Electrical and Information Technologies for Rail Transportation. Berlin, Heidelberg: Springer, 293–301

[7] Carvalho, S Partidario, M Sheate, W (2017). High speed rail comparative strategic assessments in EU member states. Environmental Impact Assessment Review, 66: 1–13

[8] Chen, H Jiang, B (2020a). A review of fault detection and diagnosis for the traction system in high-speed trains. IEEE Transactions on Intelligent Transportation Systems, 21( 2): 450–465

[9] Chen, H Jiang, B Ding, S X Huang, B (2022). Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives. IEEE Transactions on Intelligent Transportation Systems, 23( 3): 1700–1716

[10] Chen, L C Papandreou, G Kokkinos, I Murphy, K Yuille, A L (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40( 4): 834–848

[11] Chen, X Zhang, B Gao, D (2021). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32( 4): 971–987

[12] ChenZChen WTaoHPengT (2020b). Sensor fault diagnosis for high-speed traction converter system based on Bayesian network. In: Chinese Automation Congress. Shanghai: IEEE, 4969–4974

[13] Chen, Z Li, X Yang, C Peng, T Yang, C Karimi, H R Gui, W (2019). A data-driven ground fault detection and isolation method for main circuit in railway electrical traction system. ISA Transactions, 87: 264–271

[14] ChengHYao X (2018). Research on fault diagnosis of traction motor based on group decision making. In: 2nd International Workshop on Structural Health Monitoring for Railway System. Qingdao, China

[15] Cheng, Y Loo, B P Y Vickerman, R (2015). High-speed rail networks, economic integration and regional specialisation in China and Europe. Travel Behaviour & Society, 2( 1): 1–14

[16] Cherif, B D E Bendiabdellah, A Tabbakh, M (2020). An automatic diagnosis of an inverter IGBT open-circuit fault based on HHT-ANN. Electric Power Components and Systems, 48( 6–7): 589–602

[17] Dai, C Liu, Z Hu, K Huang, K (2016). Fault diagnosis approach of traction transformers in high-speed railway combining kernel principal component analysis with random forest. IET Electrical Systems in Transportation, 6( 3): 202–206

[18] Dai, J Song, H Sheng, G Jiang, X (2017). Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network. IEEE Transactions on Dielectrics and Electrical Insulation, 24( 5): 2828–2835

[19] DingGWang LSongJLinZ (2010). Neural network based on wavelet packet-characteristic entropy and rough set theory for fault diagnosis. In: 2nd International Conference on Computer Engineering and Technology. Chengdu: IEEE, 560–564

[20] Dong, H Chen, F Wang, Z Jia, L Qin, Y Man, J (2021). An adaptive multisensor fault diagnosis method for high-speed train traction converters. IEEE Transactions on Power Electronics, 36( 6): 6288–6302

[21] DrabekPPittermann MCedlM (2010). Primary traction converter for multi-system locomotives. In: IEEE International Symposium on Industrial Electronics. Bari: IEEE, 1010–1015

[22] DuHMinkuL L ZhouH (2020). MARLINE: Multi-source mapping transfer learning for non-stationary environments. In: IEEE International Conference on Data Mining. Sorrento: IEEE, 122–131

[23] DujicDKieferndorf FCanalesFDrofenikU (2012). Power electronic traction transformer technology. In: 7th International Power Electronics and Motion Control Conference. Harbin: IEEE, 636–642

[24] Gou, B Xu, Y Xia, Y Deng, Q Ge, X (2020). An online data-driven method for simultaneous diagnosis of IGBT and current sensor fault of three-phase PWM inverter in induction motor drives. IEEE Transactions on Power Electronics, 35( 12): 13281–13294

[25] Gu, J Huang, M (2020). Fault diagnosis method for bearing of high-speed train based on multitask deep learning. Shock and Vibration, 8873504

[26] Guo, Q Zhang, X Li, J Li, G (2022). Fault diagnosis of modular multilevel converter based on adaptive chirp mode decomposition and temporal convolutional network. Engineering Applications of Artificial Intelligence, 107: 104544

[27] Guzinski, J Abu-Rub, H Diguet, M Krzeminski, Z Lewicki, A (2010). Speed and load torque observer application in high-speed train electric drive. IEEE Transactions on Industrial Electronics, 57( 2): 565–574

[28] Han, T Jiang, D (2016). Rolling bearing fault diagnostic method based on VMD-AR model and random forest classifier. Shock and Vibration, 5132046

[29] Han, T Li, Y F Qian, M (2021a). A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions. IEEE Transactions on Instrumentation and Measurement, 70: 1–11

[30] Han, T Liu, C Wu, R Jiang, D (2021b). Deep transfer learning with limited data for machinery fault diagnosis. Applied Soft Computing, 103: 107150

[31] HeKZhangX RenSSunJ (2016). Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 770–778

[32] Hu, H Feng, F Wang, T (2020). Open-circuit fault diagnosis of NPC inverter IGBT based on independent component analysis and neural network. Energy Reports, 6: 134–143

[33] Hu, K Liu, Z Lin, S (2016). Wavelet entropy-based traction inverter open switch fault diagnosis in high-speed railways. Entropy, 18( 3): 78

[34] Huang, S Chen, W Sun, B Tao, T Yang, L (2020). Arc detection and recognition in the pantograph–catenary system based on multi-information fusion. Transportation Research Record: Journal of the Transportation Research Board, 2674( 10): 229–240

[35] Huang, S Zhai, Y Zhang, M Hou, X (2019). Arc detection and recognition in pantograph–catenary system based on convolutional neural network. Information Sciences, 501: 363–376

[36] HugoNStefanutti PPellerinMAkdagA (2007). Power electronics traction transformer. In: European Conference on Power Electronics and Applications. Aalborg: IEEE, 1–10

[37] JiangSWei XYangZ (2019). Defect detection of pantograph slider based on improved faster R-CNN. In: Chinese Control and Decision Conference. Nanchang: IEEE, 5278–5283

[38] JiaoZMa CLinCNieXQingA (2021). Real-time detection of pantograph using improved CenterNet. In: 16th Conference on Industrial Electronics and Applications. Chengdu: IEEE, 85–89

[39] Jordan, M I Mitchell, T M (2015). Machine learning: Trends, perspectives, and prospects. Science, 349( 6245): 255–260

[40] Karaduman, G Akin, E (2020). A deep learning based method for detecting of wear on the current collector strips’ surfaces of the pantograph in railways. IEEE Access, 8: 183799–183812

[41] Karaduman, G Akin, E (2022). A new approach based on predictive maintenance using the fuzzy classifier in pantograph–catenary systems. IEEE Transactions on Intelligent Transportation Systems, 23( 5): 4236–4246

[42] KaradumanGKarakose MAkinE (2017). Deep learning based arc detection in pantograph–catenary systems. In: 10th International Conference on Electrical and Electronics Engineering. Bursa: IEEE, 904–908

[43] Karakose, E Gencoglu, M T Karakose, M Aydin, I Akin, E (2017). A new experimental approach using image processing-based tracking for an efficient fault diagnosis in pantograph–catenary systems. IEEE Transactions on Industrial Informatics, 13( 2): 635–643

[44] Karakose, E Gencoglu, M T Karakose, M Yaman, O Aydin, I Akin, E (2018). A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems. Journal of Intelligent Manufacturing, 29( 4): 839–856

[45] KeLLiuZ ZhangY (2020). Fault diagnosis of modular multilevel converter based on optimized support vector machine. In: 39th Chinese Control Conference. Shenyang: IEEE, 4204–4209

[46] Khamidov, O Grishchenko, A (2021). Locomotive asynchronous traction motor rolling bearing fault detection based on current intelligent methods. Journal of Physics: Conference Series, 2131( 4): 042084

[47] Kotsiantis, S B Zaharakis, I D Pintelas, P E (2006). Machine learning: A review of classification and combining techniques. Artificial Intelligence Review, 26( 3): 159–190

[48] Kou, L Liu, C Cai, G Zhang, Z (2020a). Fault diagnosis for power electronics converters based on deep feedforward network and wavelet compression. Electric Power Systems Research, 185: 106370

[49] Kou, L Liu, C Cai, G Zhang, Z Zhou, J N Wang, X M (2020b). Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features. ISA Transactions, 101: 399–407

[50] Kulkarni, S Pappalardo, C M Shabana, A A (2017). Pantograph/Catenary contact formulations. Journal of Vibration and Acoustics, 139( 1): 011010

[51] LawrenceM BBullock R GLiuZ (2019). China’s High-Speed Rail Development. Washington, D.C.: World Bank Publications

[52] LeCun, Y Bengio, Y Hinton, G (2015). Deep learning. Nature, 521( 7553): 436–444

[53] Lei, Y Yang, B Jiang, X Jia, F Li, N Nandi, A K (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138: 106587

[54] Li, B Luo, C Wang, Z (2020). Application of GWO-SVM algorithm in arc detection of pantograph. IEEE Access, 8: 173865–173873

[55] Li, J Hai, C Feng, Z Li, G (2021a). A transformer fault diagnosis method based on parameters optimization of hybrid kernel extreme learning machine. IEEE Access, 9: 126891–126902

[56] Li, J Zhang, Q Wang, K Wang, J Zhou, T Zhang, Y (2016). Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine. IEEE Transactions on Dielectrics and Electrical Insulation, 23( 2): 1198–1206

[57] Li, L Wu, M Wu, S Li, J Song, K (2019). A three-phase to single-phase AC-DC-AC topology based on multi-converter in AC electric railway application. IEEE Access, 7: 111539–111558

[58] Li, X Sun, Z Xue, J Ma, Z (2021b). A concise review of recent few-shot meta-learning methods. Neurocomputing, 456: 463–468

[59] Li, Y (2022). Exploring real-time fault detection of high-speed train traction motor based on machine learning and wavelet analysis. Neural Computing & Applications, 34: 9301–9314

[60] LiYWeiX (2018). Pantograph slide plate abrasion detection based on deep learning network. In: 3rd International Conference on Electrical and Information Technologies for Rail Transportation. Singapore: Springer, 215–224

[61] Li, Y H Tian, X J Li, X Q (2013). Identification of magnetizing inrush and internal short-circuit fault current in v/x-type traction transformer. Advances in Mechanical Engineering, 5: 905202

[62] Li, Z Zhang, Y Abu-Siada, A Chen, X Li, Z Xu, Y Zhang, L Tong, Y (2021c). Fault diagnosis of transformer windings based on decision tree and fully connected neural network. Energies, 14( 6): 1531

[63] Liao, W Yang, D Wang, Y Ren, X (2021). Fault diagnosis of power transformers using graph convolutional network. CSEE Journal of Power and Energy Systems, 7( 2): 241–249

[64] Lin, J Su, L Yan, Y Sheng, G Xie, D Jiang, X (2018). Prediction method for power transformer running state based on LSTM_DBN network. Energies, 11( 7): 1880

[65] LinWPengG WuMLinY JinL (2020). A fault detection method of high speed train pantograph based on deep learning. In: 8th International Conference on Condition Monitoring and Diagnosis. Phuket: IEEE, 254–257

[66] Liu, C Gryllias, K (2022). Simulation-driven domain adaptation for rolling element bearing fault diagnosis. IEEE Transactions on Industrial Informatics, 18( 9): 5760–5770

[67] LiuHHanM (2012). Research of prognostics and health management for EMU. In: Prognostics and System Health Management Conference. Beijing: IEEE, 1–6

[68] Liu, J Zhao, Z Tang, C Yao, C Li, C Islam, S (2019a). Classifying transformer winding deformation fault types and degrees using FRA based on support vector machine. IEEE Access, 7: 112494–112504

[69] Liu, R Yang, B Zio, E Chen, X (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108: 33–47

[70] Liu, S Yu, L Zhang, D (2019b). An efficient method for high-speed railway dropper fault detection based on depthwise separable convolution. IEEE Access, 7: 135678–135688

[71] Liu, W Wang, Z Han, J Wang, G (2013). Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM. Renewable Energy, 50: 1–6

[72] Liu, Z Wang, H Liu, J Qin, Y Peng, D (2021). Multitask learning based on lightweight 1DCNN for fault diagnosis of wheelset bearings. IEEE Transactions on Instrumentation and Measurement, 70: 1–11

[73] LongHMa MGuoWLiFZhangX (2020). Fault diagnosis for IGBTs open-circuit faults in photovoltaic grid-connected inverters based on statistical analysis and machine learning. In: 1st China International Youth Conference on Electrical Engineering. Wuhan: IEEE, 1–6

[74] Lu, S Liu, Z Li, D Shen, Y (2021). Automatic wear measurement of pantograph slider based on multiview analysis. IEEE Transactions on Industrial Informatics, 17( 5): 3111–3121

[75] Luo, Y Yang, Q Liu, S (2019). Novel vision-based abnormal behavior localization of pantograph–catenary for high-speed trains. IEEE Access, 7: 180935–180946

[76] Ma, M Sun, C Chen, X (2018). Deep coupling autoencoder for fault diagnosis with multimodal sensory data. IEEE Transactions on Industrial Informatics, 14( 3): 1137–1145

[77] MehdipourPichaHBoRChenH RanaM MHuang JHuF (2019). Transformer fault diagnosis using deep neural network. In: IEEE Innovative Smart Grid Technologies. Chengdu: IEEE, 4241–4245

[78] MinkuL L (2019). Transfer learning in non-stationary environments. In: Sayed-Mouchaweh M, ed. Learning from Data Streams in Evolving Environments. Cham: Springer, 13–37

[79] MoosaviS SDjerdir AAït-AmiratYKhaburiD A (2012a). Fault detection in 3-phase traction motor using artificial neural networks. In: IEEE Transportation Electrification Conference and Expo. Dearborn, MI: IEEE, 1–6

[80] MoosaviS SDjerdir AAït-AmiratYKkuburiD A (2012b). Artificial neural networks based fault detection in 3-phase PMSM traction motor. In: 20th International Conference on Electrical Machines. Marseille: IEEE, 1579–1585

[81] Na, K Lee, K Shin, S Kim, H (2020). Detecting deformation on pantograph contact strip of railway vehicle on image processing and deep learning. Applied Sciences, 23( 10): 8509

[82] Nategh, S Boglietti, A Liu, Y Barber, D Brammer, R Lindberg, D Aglen, O (2020). A review on different aspects of traction motor design for railway applications. IEEE Transactions on Industry Applications, 56( 3): 2148–2157

[83] PengDLiu CDesmetWGrylliasK (2021). Deep unsupervised transfer learning for health status prediction of a fleet of wind turbines with unbalanced data. In: Annual Conference of the PHM Society

[84] Peng, D Liu, Z Wang, H Qin, Y Jia, L (2019). A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains. IEEE Access, 7: 10278–10293

[85] Peng, D Wang, H Liu, Z Zhang, W Zuo, M J Chen, J (2020a). Multibranch and multiscale CNN for fault diagnosis of wheelset bearings under strong noise and variable load condition. IEEE Transactions on Industrial Informatics, 16( 7): 4949–4960

[86] PengTDai LChenZYeCPengX (2020b). A probabilistic finite state automata-based fault detection method for traction motor. In: 29th International Symposium on Industrial Electronics. Delft: IEEE, 1199–1204

[87] PhalaKDoorsamy WPaulB S (2021). An intelligent fault monitoring system for railway neutral sections. In: International Conference on Communication and Computational Technologies. Singapore: Springer, 835–844

[88] Popescu, M Goss, J Staton, D A Hawkins, D Chong, Y C Boglietti, A (2018). Electrical vehicles: Practical solutions for power traction motor systems. IEEE Transactions on Industry Applications, 54( 3): 2751–2762

[89] QinJZhouB MiZ (2019). Research of fault diagnosis and location of power transformer based on convolutional neural network. In: IEEE Innovative Smart Grid Technologies. Chengdu: IEEE, 3589–3594

[90] Qu, Z Yuan, S Chi, R Chang, L Zhao, L (2019). Genetic optimization method of pantograph and catenary comprehensive monitor status prediction model based on Adadelta deep neural network. IEEE Access, 7: 23210–23221

[91] Ray, D K Rai, A Khetan, A K Mishra, A Chattopadhyay, S (2020). Brush fault analysis for Indian DC traction locomotive using DWT-based multi-resolution analysis. Journal of The Institution of Engineers: Series B, 101( 4): 335–345

[92] Ren, L Lv, W Jiang, S Xiao, Y (2016). Fault diagnosis using a joint model based on sparse representation and SVM. IEEE Transactions on Instrumentation and Measurement, 65( 10): 2313–2320

[93] Ren, S He, K Girshick, R Sun, J (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39( 6): 1137–1149

[94] SakaidaniYKondo M (2018). Bearing fault detection for railway traction motors through leakage current. In: 13th International Conference on Electrical Machines. Alexandroupoli: IEEE, 1768–1774

[95] Sarita, K Kumar, S Saket, R K (2021). OC fault diagnosis of multilevel inverter using SVM technique and detection algorithm. Computers & Electrical Engineering, 96: 107481

[96] SeifeddineSKhmais BAbdelkaderC (2012). Power transformer fault diagnosis based on dissolved gas analysis by artificial neural network. In: 1st International Conference on Renewable Energies and Vehicular Technology. Nabeu: IEEE, 230–236

[97] Shao, H Jiang, H Zhao, H Wang, F (2017). A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 95: 187–204

[98] Shao, S Yan, R Lu, Y Wang, P Gao, R X (2020). DCNN-based multi-signal induction motor fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 69( 6): 2658–2669

[99] ShenYLiu ZChangL (2018). A pantograph horn detection method based on deep learning network. In: 3rd Optoelectronics Global Conference. Shenzhen: IEEE, 85–89

[100] Shi, Y Yi, C Lin, J Zhuang, Z Lai, S (2020). Ensemble empirical mode decomposition-entropy and feature selection for pantograph fault diagnosis. Journal of Vibration and Control, 26( 23–24): 2230–2242

[101] Song, H Dai, J Luo, L Sheng, G Jiang, X (2018). Power transformer operating state prediction method based on an LSTM network. Energies, 11( 4): 914

[102] SongHKim MParkDShinYLeeJ G (2022). Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, in press,

[103] SunRLiL ChenXWang JChaiXZhengS (2020). Unsupervised learning based target localization method for pantograph video. In: 16th International Conference on Computational Intelligence and Security. Guilin: IEEE, 318–323

[104] SunXMaoZ JiangBLi M (2017). EEMD based incipient fault diagnosis for sensors faults in high-speed train traction systems. In: Chinese Automation Congress. Jinan: IEEE, 4804–4809

[105] TastimurCKaraduman GAkinE (2021). A novel method based on deep learning and image processing techniques for wearing inspection on the pantograph surface. In: Innovations in Intelligent Systems and Applications Conference. Elazig: IEEE, 1–7

[106] Tran, V T Cattley, R Ball, A Liang, B Iwnicki, S (2013). Fault diagnosis of induction motor based on a novel intelligent framework and transient current signals. Chemical Engineering Transactions, 33: 691–696

[107] Uma Maheswari, R Umamaheswari, R (2017). Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train: A contemporary survey. Mechanical Systems and Signal Processing, 85: 296–311

[108] WanGLiuX DongD (2009). Global fault diagnosis method of traction transformer based on improved fuzzy cellular neural network. In: 4th IEEE Conference on Industrial Electronics and Applications. Xi’an: IEEE, 353–357

[109] Wang, H Liu, Z Peng, D Cheng, Z (2022). Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA Transactions, 128( Part B): 470–484

[110] Wang, H Xu, J Yan, R Gao, R X (2020a). A new intelligent bearing fault diagnosis method using SDP representation and SE-CNN. IEEE Transactions on Instrumentation and Measurement, 69( 5): 2377–2389

[111] WangHZhang CZhangNChenYChenY (2019). Fault diagnosis for IGBTs open-circuit faults in high-speed trains based on convolutional neural network. In: Prognostics and System Health Management Conference. Qingdao: IEEE, 1–8

[112] Wang, L Zhao, X Pei, J Tang, G (2016). Transformer fault diagnosis using continuous sparse autoencoder. SpringerPlus, 5( 1): 448

[113] Wang, T He, Y Li, B Shi, T (2018). Transformer fault diagnosis using self-powered RFID sensor and deep learning approach. IEEE Sensors Journal, 18( 15): 6399–6411

[114] WangXYang BLiuQTuJChenC (2021). Diagnosis for IGBT open-circuit faults in photovoltaic inverters: A compressed sensing and CNN based method. In: 19th International Conference on Industrial Informatics. Palma de Mallorca: IEEE, 1–6

[115] Wang, Y Quan, W Lu, X Peng, Y Zhou, N Zou, D Liu, Y Guo, S Zheng, D (2020b). Anomaly detection of pantograph based on salient segmentation and generative adversarial networks. Journal of Physics: Conference Series, 1544( 1): 012140

[116] Wei, X Jiang, S Li, Y Li, C Jia, L Li, Y (2020). Defect detection of pantograph slide based on deep learning and image processing technology. IEEE Transactions on Intelligent Transportation Systems, 21( 3): 947–958

[117] WuCZhaoJ HuangCZhang J (2012). Data-based fault diagnosis of traction converter and simulation study. In: 7th IEEE Conference on Industrial Electronics and Applications. Singapore: IEEE, 1512–1516

[118] Xia, Y Gou, B Xu, Y (2018a). A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter. Protection and Control of Modern Power Systems, 3( 1): 33

[119] XiaYGouB XuYWilsonG (2018b). Ensemble-based randomized classifier for data-driven fault diagnosis of IGBT in traction converters. In: IEEE Innovative Smart Grid Technologies. Singapore: IEEE, 74–79

[120] Xia, Y Xu, Y (2021). A transferrable data-driven method for IGBT open-circuit fault diagnosis in three-phase inverters. IEEE Transactions on Power Electronics, 36( 12): 13478–13488

[121] Xia, Y Xu, Y Gou, B (2020). A data-driven method for IGBT open-circuit fault diagnosis based on hybrid ensemble learning and sliding-window classification. IEEE Transactions on Industrial Informatics, 16( 8): 5223–5233

[122] Xian, X Tang, H Tian, Y Liu, Q Fan, Y (2021). Performance analysis of different machine learning algorithms for identifying and classifying the failures of traction motors. Journal of Physics: Conference Series, 2095( 1): 012058

[123] Xiao, Y Pan, W Guo, X Bi, S Feng, D Lin, S (2020). Fault diagnosis of traction transformer based on Bayesian network. Energies, 13( 18): 4966

[124] Xu, W A Zhou, J Qiu, G (2018). China’s high-speed rail network construction and planning over time: A network analysis. Journal of Transport Geography, 70: 40–54

[125] Xu, Y Cai, W Xie, T (2021a). Fault diagnosis of subway traction motor bearing based on information fusion under variable working conditions. Shock and Vibration, 5522887

[126] Xu, Y Li, C Xie, T (2021b). Intelligent diagnosis of subway traction motor bearing fault based on improved stacked denoising autoencoder. Shock and Vibration, 6656635

[127] Yang, H Dobruszkes, F Wang, J Dijst, M Witte, P (2018). Comparing China’s urban systems in high-speed railway and airline networks. Journal of Transport Geography, 68: 233–244

[128] YangZHuang XWuSPengH (2010). Traction technology for Chinese railways. In: International Power Electronics Conference. Sapporo: IEEE, 2842–2848

[129] YetisHKarakose MAydinIAkinE (2019). Bearing fault diagnosis in traction motor using the features extracted from filtered signals. In: International Artificial Intelligence and Data Processing Symposium. Malatya: IEEE, 1–4

[130] Yuan, F Guo, J Xiao, Z Zeng, B Zhu, W Huang, S (2019). A transformer fault diagnosis model based on chemical reaction optimization and twin support vector machine. Energies, 12( 5): 960

[131] Zang, Y Shangguan, W Cai, B Wang, H Pecht, M G (2019). Methods for fault diagnosis of high-speed railways: A review. Proceedings of the Institution of Mechanical Engineers: Part O, Journal of Risk and Reliability, 233( 5): 908–922

[132] Zeng, B Guo, J Zhu, W Xiao, Z Yuan, F Huang, S (2019). A transformer fault diagnosis model based on hybrid grey wolf optimizer and LS-SVM. Energies, 12( 21): 4170

[133] Zhang, C He, Y Du, B Yuan, L Li, B Jiang, S (2020a). Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning. Future Generation Computer Systems, 108: 533–545

[134] Zhang, C Wang, C Lu, N Jiang, B (2019). An RBMs-BN method to RUL prediction of traction converter of CRH2 trains. Engineering Applications of Artificial Intelligence, 85: 46–56

[135] Zhang, D Gao, S Yu, L Kang, G Zhan, D Wei, X (2020b). A robust pantograph–catenary interaction condition monitoring method based on deep convolutional network. IEEE Transactions on Instrumentation and Measurement, 69( 5): 1920–1929

[136] Zhang, Y Ding, X Liu, Y Griffin, P J (1996). An artificial neural network approach to transformer fault diagnosis. IEEE Transactions on Power Delivery, 11( 4): 1836–1841

[137] Zhang, Y Tiňo, P Leonardis, A Tang, K (2021a). A survey on neural network interpretability. IEEE Transactions on Emerging Topics in Computational Intelligence, 5( 5): 726–742

[138] Zhang, Z Zhao, Z Li, X Zhang, X Wang, S Yan, R Chen, X (2021b). Faster multiscale dictionary learning method with adaptive parameter estimation for fault diagnosis of traction motor bearings. IEEE Transactions on Instrumentation and Measurement, 70: 1–13

[139] ZhaoJWu CHuangCWuF (2014). Parameter optimization algorithm of SVM for fault classification in traction converter. In: 26th Chinese Control and Decision Conference. Changsha: IEEE, 3786–3791

[140] Zhao, R Yan, R Chen, Z Mao, K Wang, P Gao, R X (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115: 213–237

[141] Zhong, S Fu, S Lin, L (2019). A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement, 137: 435–453

[142] Zhou, L Wang, D Cui, Y Zhang, L Wang, L Guo, L (2021a). A method for diagnosing the state of insulation paper in traction transformer based on FDS test and CS-DQ algorithm. IEEE Transactions on Transportation Electrification, 7( 1): 91–103

[143] Zhou, Y Yang, X Tao, L Yang, L (2021b). Transformer fault diagnosis model based on improved gray wolf optimizer and probabilistic neural network. Energies, 14( 11): 3029

[144] ZhuJChenT FuQ (2014). The research and application of WNN in the fault diagnosis technology of electric locomotive main transformer. In: 7th IET International Conference on Power Electronics, Machines and Drives. Manchester: IEEE, 1–6

[145] ZhuJChenT FuQChengS (2015). Detection of early failures within traction transformers based on Gaussian-PSO. In: 3rd International Conference on Electric Power Equipment, Switching Technology. Busan: IEEE, 488–491

[146] Zhu, J Li, S Dong, H (2021). Running status diagnosis of onboard traction transformers based on kernel principal component analysis and fuzzy clustering. IEEE Access, 9: 121835–121844

[147] Zollanvari, A Kunanbayev, K Akhavan Bitaghsir, S Bagheri, M (2021). Transformer fault prognosis using deep recurrent neural network over vibration signals. IEEE Transactions on Instrumentation and Measurement, 70: 1–11

[148] Zou, Y Zhang, Y Mao, H (2021). Fault diagnosis on the bearing of traction motor in high-speed trains based on deep learning. Alexandria Engineering Journal, 60( 1): 1209–1219

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