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.
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References
Aydin I, Karakose M, Akin E (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
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
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
Bi S, Feng D, Lin S, Guo X, Pan W (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
Brahimi M, Medjaher K, Leouatni M, Zerhouni N (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
Cao J, Cui H, Li N (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
Carvalho S, Partidario M, Sheate W (2017). High speed rail comparative strategic assessments in EU member states. Environmental Impact Assessment Review, 66: 1–13
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
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
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
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
Chen Z, Chen W, Tao H, Peng T (2020b). Sensor fault diagnosis for high-speed traction converter system based on Bayesian network. In: Chinese Automation Congress. Shanghai: IEEE, 4969–4974
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
Cheng H, Yao 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
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
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
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
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
Ding G, Wang L, Song J, Lin Z (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
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
Drabek P, Pittermann M, Cedl M (2010). Primary traction converter for multi-system locomotives. In: IEEE International Symposium on Industrial Electronics. Bari: IEEE, 1010–1015
Du H, Minku L L, Zhou H (2020). MARLINE: Multi-source mapping transfer learning for non-stationary environments. In: IEEE International Conference on Data Mining. Sorrento: IEEE, 122–131
Dujic D, Kieferndorf F, Canales F, Drofenik U (2012). Power electronic traction transformer technology. In: 7th International Power Electronics and Motion Control Conference. Harbin: IEEE, 636–642
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
Gu J, Huang M (2020). Fault diagnosis method for bearing of high-speed train based on multitask deep learning. Shock and Vibration, 8873504
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
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
Han T, Jiang D (2016). Rolling bearing fault diagnostic method based on VMD-AR model and random forest classifier. Shock and Vibration, 5132046
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
Han T, Liu C, Wu R, Jiang D (2021b). Deep transfer learning with limited data for machinery fault diagnosis. Applied Soft Computing, 103: 107150
He K, Zhang X, Ren S, Sun J (2016). Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 770–778
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
Hu K, Liu Z, Lin S (2016). Wavelet entropy-based traction inverter open switch fault diagnosis in high-speed railways. Entropy, 18(3): 78
Huang S, Chen W, Sun B, Tao T, Yang L (2020). Arc detection and recognition in the pantograph–catenary system based on multiinformation fusion. Transportation Research Record: Journal of the Transportation Research Board, 2674(10): 229–240
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
Hugo N, Stefanutti P, Pellerin M, Akdag A (2007). Power electronics traction transformer. In: European Conference on Power Electronics and Applications. Aalborg: IEEE, 1–10
Jiang S, Wei X, Yang Z (2019). Defect detection of pantograph slider based on improved faster R-CNN. In: Chinese Control and Decision Conference. Nanchang: IEEE, 5278–5283
Jiao Z, Ma C, Lin C, Nie X, Qing A (2021). Real-time detection of pantograph using improved CenterNet. In: 16th Conference on Industrial Electronics and Applications. Chengdu: IEEE, 85–89
Jordan M I, Mitchell T M (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245): 255–260
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
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
Karaduman G, Karakose M, Akin E (2017). Deep learning based arc detection in pantograph–catenary systems. In: 10th International Conference on Electrical and Electronics Engineering. Bursa: IEEE, 904–908
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
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
Ke L, Liu Z, Zhang Y (2020). Fault diagnosis of modular multilevel converter based on optimized support vector machine. In: 39th Chinese Control Conference. Shenyang: IEEE, 4204–4209
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
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
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
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
Kulkarni S, Pappalardo C M, Shabana A A (2017). Pantograph/Catenary contact formulations. Journal of Vibration and Acoustics, 139(1): 011010
Lawrence M B, Bullock R G, Liu Z (2019). China’s High-Speed Rail Development. Washington, D.C.: World Bank Publications
LeCun Y, Bengio Y, Hinton G (2015). Deep learning. Nature, 521(7553): 436–444
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
Li B, Luo C, Wang Z (2020). Application of GWO-SVM algorithm in arc detection of pantograph. IEEE Access, 8: 173865–173873
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
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
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
Li X, Sun Z, Xue J, Ma Z (2021b). A concise review of recent few-shot meta-learning methods. Neurocomputing, 456: 463–468
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
Li Y, Wei X (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
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
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
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
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
Lin W, Peng G, Wu M, Lin Y, Jin L (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
Liu C, Gryllias K (2022). Simulation-driven domain adaptation for rolling element bearing fault diagnosis. IEEE Transactions on Industrial Informatics, 18(9): 5760–5770
Liu H, Han M (2012). Research of prognostics and health management for EMU. In: Prognostics and System Health Management Conference. Beijing: IEEE, 1–6
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
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
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
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
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
Long H, Ma M, Guo W, Li F, Zhang X (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
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
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
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
MehdipourPicha H, Bo R, Chen H, Rana M M, Huang J, Hu F (2019). Transformer fault diagnosis using deep neural network. In: IEEE Innovative Smart Grid Technologies. Chengdu: IEEE, 4241–4245
Minku L L (2019). Transfer learning in non-stationary environments. In: Sayed-Mouchaweh M, ed. Learning from Data Streams in Evolving Environments. Cham: Springer, 13–37
Moosavi S S, Djerdir A, Aït-Amirat Y, Khaburi D 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
Moosavi S S, Djerdir A, Aït-Amirat Y, Kkuburi D 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
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
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
Peng D, Liu C, Desmet W, Gryllias K (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
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
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
Peng T, Dai L, Chen Z, Ye C, Peng X (2020b). A probabilistic finite state automata-based fault detection method for traction motor. In: 29th International Symposium on Industrial Electronics. Delft: IEEE, 1199–1204
Phala K, Doorsamy W, Paul B S (2021). An intelligent fault monitoring system for railway neutral sections. In: International Conference on Communication and Computational Technologies. Singapore: Springer, 835–844
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
Qin J, Zhou B, Mi Z (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
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
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
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
Ren S, He K, Girshick R, Sun J (2017). Faster R-CNN: Towards realtime object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137–1149
Sakaidani Y, Kondo M (2018). Bearing fault detection for railway traction motors through leakage current. In: 13th International Conference on Electrical Machines. Alexandroupoli: IEEE, 1768–1774
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
Seifeddine S, Khmais B, Abdelkader C (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
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
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
Shen Y, Liu Z, Chang L (2018). A pantograph horn detection method based on deep learning network. In: 3rd Optoelectronics Global Conference. Shenzhen: IEEE, 85–89
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
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
Song H, Kim M, Park D, Shin Y, Lee J G (2022). Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, in press, doi:https://doi.org/10.1109/TNNLS.2022.3152527
Sun R, Li L, Chen X, Wang J, Chai X, Zheng S (2020). Unsupervised learning based target localization method for pantograph video. In: 16th International Conference on Computational Intelligence and Security. Guilin: IEEE, 318–323
Sun X, Mao Z, Jiang B, Li M (2017). EEMD based incipient fault diagnosis for sensors faults in high-speed train traction systems. In: Chinese Automation Congress. Jinan: IEEE, 4804–4809
Tastimur C, Karaduman G, Akin E (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
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
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
Wan G, Liu X, Dong D (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
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
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
Wang H, Zhang C, Zhang N, Chen Y, Chen Y (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
Wang L, Zhao X, Pei J, Tang G (2016). Transformer fault diagnosis using continuous sparse autoencoder. SpringerPlus, 5(1): 448
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
Wang X, Yang B, Liu Q, Tu J, Chen C (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
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
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
Wu C, Zhao J, Huang C, Zhang 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
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
Xia Y, Gou B, Xu Y, Wilson G (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
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
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
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
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
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
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
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
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
Yang Z, Huang X, Wu S, Peng H (2010). Traction technology for Chinese railways. In: International Power Electronics Conference. Sapporo: IEEE, 2842–2848
Yetis H, Karakose M, Aydin I, Akin E (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
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
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
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
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
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
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
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
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
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
Zhao J, Wu C, Huang C, Wu F (2014). Parameter optimization algorithm of SVM for fault classification in traction converter. In: 26th Chinese Control and Decision Conference. Changsha: IEEE, 3786–3791
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
Zhong S, Fu S, Lin L (2019). A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement, 137: 435–453
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
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
Zhu J, Chen T, Fu Q (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
Zhu J, Chen T, Fu Q, Cheng S (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
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
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
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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This work was supported by the National Natural Science Foundation of China (Grant No. 71731008), the Beijing Municipal Natural Science Foundation – Rail Transit Joint Research Program (Grant No. L191022), and the Zhibo Lucchini Railway Equipment Co., Ltd.
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Wang, H., Li, YF. & Ren, J. Machine learning for fault diagnosis of high-speed train traction systems: A review. Front. Eng. Manag. 11, 62–78 (2024). https://doi.org/10.1007/s42524-023-0256-2
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DOI: https://doi.org/10.1007/s42524-023-0256-2