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

doi:10.1007/s42524-023-0256-2

Front. Eng

2024, Vol. 11

Issue (1) : 62-78 https://doi.org/10.1007/s42524-023-0256-2

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

Huan WANG¹, Yan-Fu LI¹(

), Jianliang REN²

¹. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
². Zhibo Lucchini Railway Equipment Co., Ltd., Taiyuan 030032, China

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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.

Keywords high-speed train traction systems machine learning fault diagnosis

Corresponding Author(s): Yan-Fu LI

Just Accepted Date: 27 April 2023 Online First Date: 01 June 2023 Issue Date: 13 March 2024

Cite this article:

Huan WANG,Yan-Fu LI,Jianliang REN. Machine learning for fault diagnosis of high-speed train traction systems: A review[J]. Front. Eng, 2024, 11(1): 62-78.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-023-0256-2
https://academic.hep.com.cn/fem/EN/Y2024/V11/I1/62

Fig.1 Schematic of high-speed train traction system architecture.

Fig.2 Schematic of AC-DC-AC traction drive system.

Fig.3 Schematic of the structure of the traction motor.

Reference	Method	Data	Application
Li et al. (2020)	Gray wolf algorithm, SVM	Current data	Pantograph arc detection
Shi et al. (2020)	SVM, EEMD, Particle swarm algorithm	Vibration data	Pantograph fault diagnosis
Karakose et al. (2018)	Fuzzy system, S-transform	Current data	Pantograph arc detection
Karaduman and Akin (2022)	Fuzzy classifier, Wavelet decomposition, Hough transform	Image data, Temperature data	Pantograph fault diagnosis
Aydin et al. (2014)	S-transform, Particle swarm algorithm	Video data	Pantograph anomaly detection
Phala et al. (2021)	SVM, K-means model	Vibration data, Temperature data	Pantograph anomaly detection
Aydin et al. (2015)	Gaussian mixture model, S-transform	Voltage and current data	Irregular positioning of the contact wire, Pantograph arc detection
Lu et al. (2021)	Multiview analysis, Subpixel edge detection algorithm	Image data	Slide surface wear monitoring, Defect location
Qu et al. (2019)	Genetic algorithm, Deep neural network	Height, stagger and hard point of contact line; Voltage; Contact force	Comprehensive pantograph and catenary monitor
Karaduman and Akin (2020)	Hough transform, Power law transform, CNN	Image data	Current collector strip surface defect detection
Tastimur et al. (2021)	Histogram equalization, CNN	Image data	Current collector strip surface wear monitoring
Shen et al. (2018)	CNN	Image data	Pantograph horn detection, Pantograph defect detection
Zhang et al. (2020b)	Deep pantograph detection network, Deep pantograph segmentation network, Edge detection, Hough transform	Image data	Contact point detection of pantograph and the catenary
Huang et al. (2019)	CNN	Image data, Video data	Pantograph arc detection and recognition
Lin et al. (2020)	CNN, YOLO v3	Image data, Video data	Pantograph electrical fault identification
Wei et al. (2020)	Pantograph defect detection neural network	Image data	Pantograph slide defect detection
Karaduman et al. (2017)	CNN	Image data	Pantograph arc detection
Luo et al. (2019)	Fast-RCNN	Image data	Pantograph anomaly detection
Liu et al. (2019b)	Depthwise separable convolution, Object detection network, Fault recognition network	Image data, Video data	Pantograph dropper fault detection
Wang et al. (2020b)	Salient segmentation, Generative adversarial networks	Image data	Pantograph anomaly detection
Sun et al. (2020)	Unsupervised learning, Superpixel segmentation	Video data	Pantograph state detection
Jiao et al. (2021)	CNN, Lightweight MobileNet, Feature pyramid network	Image data	Pantograph real-time detection
Huang et al. (2020)	CNN, Multi-information fusion	Visible light image, Infrared light image	Pantograph arc detection and recognition
Li and Wei (2018)	CNN	Image data	Pantograph slide defect detection
Na et al. (2020)	Image processing and deep learning	Image data	Detecting deformation on pantograph contact strip
Jiang et al. (2019)	Fast-RCNN	Image data	Defect detection of pantograph slider

Tab.1 List of research work on pantograph fault diagnosis

Reference	Method	Data	Application
Dai et al. (2016)	Kernel principal component analysis, Random forest	Dissolved gases analysis	Traction transformer fault diagnosis
Zhu et al. (2015)	Radial basis function neural network, Fuzzy C-means algorithm	Dissolved gases analysis	Traction transformer early fault detection
Bi et al. (2020)	Variable weight coefficient, Bayesian network	Historical statistical data	Traction transformer condition monitoring
Zhou et al. (2021a)	Multiclass least-squares SVM, Differential evolution algorithm, Quadratic interpolation method	Frequency-domain dielectric spectrum test	Traction transformer insulation paper condition monitoring
Li et al. (2013)	Empirical mode decomposition, Energy weight, Information entropy	Differential current signal, Magnetizing inrush	Traction transformer fault diagnosis
Wan et al. (2009)	Improved fuzzy cellular neural network	Dissolved gases analysis, Water in oil, Key device resistance and electric current	Traction transformer fault diagnosis
Zhu et al. (2021)	Kernel principal component analysis, Fuzzy clustering	Dissolved gases analysis	Traction transformer condition monitoring
Xiao et al. (2020)	Bayesian network	Insulation resistance, Dielectric loss tangent value, Oil and gas, Power frequency voltage, Leakage current	Traction transformer fault diagnosis
Zhu et al. (2014)	Wavelet neural network, Hybrid particle swarm algorithm	Chromatographic data and electrical test data	Traction transformer fault diagnosis
MehdipourPicha et al. (2019)	Deep neural network	Dissolved gases analysis	Transformer fault diagnosis
Zhang et al. (1996)	ANN	Dissolved gases analysis	Transformer fault diagnosis
Zhang et al. (2020a)	Deep belief networks, Stacked denoising autoencoders, Relevance vector machines	Transformer vibration signal	Transformer fault diagnosis
Bacha et al. (2012)	SVM	Dissolved gases analysis	Transformer fault diagnosis
Wang et al. (2016)	Back propagation (BP) network, Continuous sparse autoencoder	Dissolved gases analysis	Transformer fault diagnosis
Li et al. (2016)	SVM, Genetic algorithm	Dissolved gases analysis	Transformer fault diagnosis
Seifeddine et al. (2012)	ANN	Dissolved gases analysis	Transformer fault diagnosis
Dai et al. (2017)	Deep belief network	Dissolved gases analysis	Transformer fault diagnosis
Zeng et al. (2019)	Hybrid grey wolf optimizer, Least square SVM	Dissolved gases analysis	Transformer fault diagnosis
Yuan et al. (2019)	Restricted Boltzmann machines, SVM	Dissolved gases analysis	Transformer fault diagnosis
Zhou et al. (2021b)	Gray wolf optimizer, Probabilistic neural network	Dissolved gases analysis	Transformer fault diagnosis
Li et al. (2021c)	Decision tree, Fully connected neural network	Frequency response analysis	Transformer windings fault diagnosis
Li et al. (2021a)	Hybrid kernel extreme learning machine, Gray wolf optimization algorithm, Differential evolution algorithm	Dissolved gases analysis	Transformer fault diagnosis
Liu et al. (2019a)	SVM	Frequency response analysis	Transformer winding deformation fault diagnosis
Song et al. (2018)	LSTM network	Transformer-condition-related data	Transformer operating state prediction and fault warning
Lin et al. (2018)	Deep belief network, LSTM network	Dissolved gases analysis	Transformer operating state prediction and fault warning
Qin et al. (2019)	CNN	Dissolved gases analysis, Vibration signal	Transformer fault diagnosis and location
Wang et al. (2018)	Stacking denoising autoencoder	Self-powered radio-frequency identification sensor	Transformer fault diagnosis
Zollanvari et al. (2021)	LSTM, Gated recurrent units	Vibration signal	Transformer fault diagnosis
Liao et al. (2021)	Graph convolutional network	Dissolved gases analysis	Transformer fault diagnosis

Tab.2 List of research work on traction transformer fault diagnosis

Reference	Method	Data	Application
Dong et al. (2021)	LSTM network	Temperature, voltage, current, and power signals; Multisensor information	Traction converter fault diagnosis
Zhao et al. (2014)	Particle swarm optimization, Genetic algorithm, SVM	Current signal	Traction converter fault diagnosis
Chen et al. (2020b)	Bayesian network, Short-time Fourier transformation, Principal component analysis	Current signal	Traction converter current sensor fault diagnosis
Wu et al. (2012)	Wavelet transform, SVM	Current signal	Traction converter fault diagnosis
Xia et al. (2018a)	Random vector functional network	Voltage and current signals	IGBT fault diagnosis
Hu et al. (2016)	Wavelet entropy	Voltage and current signals	Traction inverter open switch fault diagnosis
Zhang et al. (2019)	Bayesian network, Restricted Boltzmann machines	Upper/Lower voltage in the DC-link circuit	RUL prediction of traction converter
Xia et al. (2020)	Fast Fourier transform, Extreme learning machine, Random vector functional link network, Hybrid ensemble learning scheme	Current signal	IGBT open-circuit fault diagnosis
Gou et al. (2020)	Fast Fourier transform, Random vector functional link network	Current signal	IGBT and current sensor fault diagnosis
Cherif et al. (2020)	Complete empirical ensemble mode decomposition, Hilbert–Huang transform, ANN	Current signal	IGBT open-circuit fault diagnosis
Xia et al. (2018b)	Extreme learning machine, Ensemble classifier structure	Current signal	IGBT open-circuit fault diagnosis
Wang et al. (2019)	CNN, K-gray	Current signal	IGBT open-circuit fault diagnosis
Xia and Xu (2021)	Extreme learning machine, Transferrable data-driven fault diagnosis	Current signal	IGBT open-circuit fault diagnosis
Ke et al. (2020)	SVM, Genetic algorithm	Current signal	IGBT open-circuit fault diagnosis
Long et al. (2020)	Empirical mode decomposition, Statistical analysis, Generalized discriminant analysis, BP neural network	Current signal	IGBT open-circuit fault diagnosis
Wang et al. (2021)	Compressed sensing, CNN	Current signal	IGBT open-circuit fault diagnosis
Hu et al. (2020)	Independent component analysis, Neural network	Voltage and current signals	IGBT open-circuit fault diagnosis
Kou et al. (2020a)	Wavelet transform, Deep feedforward network	Voltage and current signals	IGBT open-circuit fault diagnosis
Kou et al. (2020b)	Deep feedforward network classifier	Current signal	IGBT open-circuit fault diagnosis
Guo et al. (2022)	Chirp mode decomposition and temporal convolutional network	Current signal	Modular multilevel converter fault diagnosis
Sarita et al. (2021)	Wavelet packets, SVM	Current signal	IGBT open-circuit fault diagnosis

Tab.3 List of research work on traction converter fault diagnosis

Reference	Method	Data	Application
Zhang et al. (2021b)	Faster adaptive parameter multiscale dictionary learning method	Simulation and industrial data, Vibration signal	Traction motor rolling bearing fault diagnosis
Khamidov and Grishchenko (2021)	ANN	Current and vibration signals	Locomotive asynchronous traction motor fault detection
Moosavi et al. (2012a)	ANN	Current and voltage signals	Three-phase traction motor fault detection
Yetis et al. (2019)	ANN, SVM	Vibration signal	Early fault diagnosis of traction motor bearing
Moosavi et al. (2012b)	ANN	Current and voltage signals	Traction motors condition monitoring
Xu et al. (2021a)	WPD, CNN	Acoustic emission and vibration acceleration signals	Fault diagnosis of subway traction motor bearing (variable working conditions)
Li (2022)	WPD, SVM	Electromagnetic torque, speed, and six-phase current signals	Traction motor fault diagnosis
Peng et al. (2020b)	Probabilistic finite state automata, D-Markov machine	Current and voltage signals	Traction motor fault diagnosis
Xu et al. (2021b)	Stacked denoising autoencoder	Vibration signal	Subway traction motor bearing fault diagnosis
Sakaidani and Kondo (2018)	Octave band analysis, Machine learning	Leakage current signal	Traction motor bearing fault diagnosis
Sun et al. (2017)	EEMD, SVM	Current and voltage signals, Speed signal	Traction motor sensor fault diagnosis
Cao et al. (2014)	Hilbert transform, WPE analysis	Current and voltage signals	EMU traction motor fault diagnosis
Tran et al. (2013)	Fourier–Bessel expansion, Generalized discriminant analysis, Relevance vector machine	Transient current signal	Traction motor bearing fault diagnosis
Ray et al. (2020)	DWT-based multiresolution analysis	Current signal	Brush fault analysis of DC traction locomotive
Zou et al. (2021)	DWT, Improved deep belief network	Vibration signal	Traction motor bearing fault diagnosis
Ding et al. (2010)	WPD, BP neural network	Vibration signal	Traction motor fault diagnosis
Cheng and Yao (2018)	Fuzzy theory, Neural network, SVM	Current and voltage signals	Traction motor fault diagnosis
Xian et al. (2021)	Random forest classification, SVM, Neural network classification	Vibration signal	Traction motor fault diagnosis

Tab.4 List of research work on traction motor fault diagnosis

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