High-speed Train Traction System Research Articles

Generalized autoencoder-based fault detection method for traction systems with performance degradation

Fault diagnosis of traction systems is important for the safety operation of high-speed trains. Long-term operation of the trains will degrade the performance of systems, which decreases the fault detection accuracy. To solve this problem, this paper proposes a fault detection method developed by a Generalized Autoencoder (GAE) for systems with performance degradation. The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation. Regardless of the probability distribution, it can handle any data, and the GAE has extremely high sensitivity in anomaly detection. Finally, the effectiveness of this method is verified through the Traction Drive Control System (TDCS) platform. At different performance degradation levels, our method’s experimental results are superior to traditional methods.

A Loss-Model-Based Efficiency Optimization Control Method for Induction Traction System of High-Speed Train Under Emergency Self-Propelled Mode

A Loss-Model-Based Efficiency Optimization Control Method for Induction Traction System of High-Speed Train Under Emergency Self-Propelled Mode

Just-in-time Learning-aided Nonlinear Fault Detection for Traction Systems of High-speed Trains

Just-in-time Learning-aided Nonlinear Fault Detection for Traction Systems of High-speed Trains

Overview of fault prognosis for traction systems in high-speed trains: A deep learning perspective

As the “heart” of high-speed train, traction systems play an important role in the safe operation of trains, of which the operation and maintenance level is still unable to meet the needs of modern railway transportation. Fortunately, multifarious advanced fault prognosis methods have been developed to deal with the dilemma. Among them, deep learning ones have received special attention due to their unique advantages. This paper first reveals the structural characteristics of traction systems in high-speed trains. Then, various representative deep learning based prognosis methods are compared and summarized, focusing on the analysis of their pros and cons. Finally, we point out the challenges and speculate the future trends in this field. This paper may serve as a referee for the interested researchers in fault prognosis and high-speed train traction systems fields, along with the potential directions.

Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests

A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is a fundamental problem in the technical field of train operation. In response to this problem, this study attempts to apply testability technology to the test capability analysis of train traction systems for rail transit. In view of the uncertainty in actual tests, a method for constructing a fault diagnosis strategy for a traction system under unreliable testing is proposed. The concept of test credibility is introduced for the first time, and the quantitative evaluation of test credibility is realized using a cloud model, so as to construct a new “fault-test” credibility correlation matrix. On this basis, a single-fault diagnosis strategy of the traction system is constructed and compared based on information theory. The results show that a using a fault diagnosis strategy under the condition of unreliable testing is more similar to actual maintenance work, proving the significance of the diagnosis strategy constructed using this method for the practical application of the project.

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

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

Deep PCA-Based Incipient Fault Diagnosis and Diagnosability Analysis of High-Speed Railway Traction System via FNR Enhancement

In recent years, the data-driven based FDD (Fault Detection and Diagnosis) of high-speed train electric traction systems has made rapid progress, as the safe operation of traction system is closely related to the reliability and stability of high-speed trains. The internal complexity and external complexity of the environment mean that fault diagnosis of high-speed train traction system faces great challenges. In this paper, a wavelet transform-based FNR (Fault to Noise Ratio) enhancement is realised to highlight incipient fault information and a Deep PCA (Principal Component Analysis)-based diagnosability analysis framework is proposed. First, a scheme for FNR enhancement-based fault data preprocessing with selection of the intelligent decomposition levels and optimal noise threshold is proposed. Second, fault information enhancement technology based on continuous wavelet transform is proposed from the perspective of energy. Further, a Deep-PCA based incipient fault detectability and isolatability analysis are provided via geometric descriptions. Finally, experiments on the TDCS-FIB (Traction Drive Control System–Fault Injection Benchmark) platform fully demonstrate the effectiveness of the method proposed in this paper.

Extended Relevance Vector Machine-Based Remaining Useful Life Prediction for DC-Link Capacitor in High-Speed Train.

Remaining useful life (RUL) prediction is a reliable tool for the health management of components. The main concern of RUL prediction is how to accurately predict the RUL under uncertainties. In order to enhance the prediction accuracy under uncertain conditions, the relevance vector machine (RVM) is extended into the probability manifold to compensate for the weakness caused by evidence approximation of the RVM. First, tendency features are selected based on the batch samples. Then, a dynamic multistep regression model is built for well describing the influence of uncertainties. Furthermore, the degradation tendency is estimated to monitor degradation status continuously. As poorly estimated hyperparameters of RVM may result in low prediction accuracy, the established RVM model is extended to the probabilistic manifold for estimating the degradation tendency exactly. The RUL is then prognosticated by the first hitting time (FHT) method based on the estimated degradation tendency. The proposed schemes are illustrated by a case study, which investigated the capacitors' performance degradation in traction systems of high-speed trains.

Review and prospect of maintenance technology for traction system of high-speed train

Abstract In the past few decades, high-speed trains have witnessed tremendous and vigorous development with the appearance of the oil crisis and industrialization, which became a significant trend in the transportation industry the world over. With the increase of high-speed railway mileage, the amount of high-speed train has grown sharply, the service life of the trains has increased gradually and the components of the vehicle traction system have become worn and aged as a result. Therefore, advanced maintenance technology and its application are key factors to reduce maintenance cost, human resource input and ensure safe, stable and reliable operation of trains. This paper summarizes and discusses the development, application mode, maintenance management and maintenance technology of high-speed railways of the major countries in the world, especially discusses the condition-based maintenance and the key technology of the traction electrical system, and offers the prospect of research direction and the development of traction maintenance technology.

Incipient fault diagnosis for high-speed train traction systems via improved LSTM

The incipient fault diagnosis of high-speed train traction systemsis a fundamental but essential task to guarantee the safety and reliability of high-speed trains.Traditional intelligent data-driven methods require comprehensive expertiseand cannot diagnose incipient faults in the early period.This paper focuses on the fault diagnosis approach for incipient time-varying faults and unknown faultsusing sensor signals.Considering that the sequential signal has non-stationary characteristicsand the incipient faults are slow time-varying,an improved LSTM (long short-term memory) network that isan unsupervised recurrent neural network, is developed.Combined with the GRU (gated recurrent unit) decoder,an LSTM-based encoder-decoder model is proposed to extract the feature vectors,which has the robustness to non-stationary characteristics.Then, the t-SNE (t-distributed stochastic neighbor embedding) approach is used to reduce the dimension of extracted feature vectors,and the DBSCAN (density-based spatial clustering of applications with noise)is utilized for clustering and recognizing faults to realize the unsupervised fault diagnosis.The proposed approach is verified using the data from a semi-physical platformof the CRH$_2$ traction system, in which the known and unknown faults are illustrated.The results show that the proposed unsupervised approachcan achieve a fault diagnosis rate of over 95%.

Outstanding Technical Features of Traction System in N700S Shinkansen New Generation Standardized High Speed Train

This paper overviews a traction system for Tokaido Shinkansen trains and presents a novel silicon carbide (SiC)-applied traction system of the new generation Shinkansen train, “N700S”, which began commercial service in the summer of 2020. To achieve compactness and weight reduction, N700S's traction system aimed to take advantage of the merits of SiC devices, which have a lower power loss, higher frequency, and a higher current than Si devices, not only in the conversion system but also in the entire traction system. This weight reduction and compactness allowed for flexibility in the design of underfloor equipment of the car body and enabled the realization of a lithium-ion battery self-traction system that could operate catenary-free at low speeds in the event of a power outage. It also contributed toward achieving flexibility by the easy redesign of different configurations of trains such as 8-car or 12-car sets rather than the standard 16-car trains used for the Tokaido Shinkansen, the concept of which is referred to as the “Standardized Shinkansen train”. The continuous introduction of energy-saving Shinkansen trains including N700S has also led to a decrease in Tokaido Shinkansen's energy consumption. This is the first application of an SiC device and a lithium-ion battery to a high-speed train's traction system in the world.

Incipient Fault Diagnosis for High-Speed Train Traction Systems via Stacked Generalization.

Diagnosing the fault as early as possible is significant to guarantee the safety and reliability of the high-speed train. Incipient fault always makes the monitored signals deviate from their normal values, which may lead to serious consequences gradually. Due to the obscure early stage symptoms, incipient faults are difficult to detect. This article develops a stacked generalization (stacking)-based incipient fault diagnosis scheme for the traction system of high-speed trains. To extract the fault feature from the faulty data signals, which are similar to the normal ones, the extreme gradient boosting (XGBoost), random forest (RF), extra trees (ET), and light gradient boosting machine (LightGBM) are chosen as the base estimators in the first layer of the stacking. Then, the logistic regression (LR) is taken as the meta estimator in the second layer to integrate the results from the base estimators for fault classification. Thanks to the generalization ability of stacking, the incipient fault diagnosis performance of the proposed stacking-based method is better than that of the single model (XGBoost, RF, ET, and LightGBM), although they can be used to detect the incipient faults, separately. Moreover, to find out the optimal hyperparameters of the base estimators, a swarm intelligent optimization algorithm, pigeon-inspired optimization (PIO), is employed. The proposed method is tested on a semiphysical platform of the CRH2 traction system in CRRC Zhuzhou Locomotive Company Ltd. The results show that the fault diagnosis rate of the proposed scheme is over 96%.

A Tacholess Order Tracking Method Based on Inverse Short Time Fourier Transform and Singular Value Decomposition for Bearing Fault Diagnosis.

Order tracking has been widely used to diagnose failures of variable speed rotating machines. The performance of the TOT (Time-Frequency Domain Tacholess Order Tracking) methods is based on the correct separation of the target component strictly related to the shaft rotation frequency. Currently, most of the methods have focused on obtaining the instantaneous frequency with accuracy. In this paper, a new TOT method has been proposed that combines the inverse short-time Fourier transform (ISTFT) with singular value decomposition (SVD). The target component closely related to the shaft rotation frequency is selected and filtered approximately in the time-frequency domain. Hence, the ISTFT is adopted to reverse the target component into the time domain. Next, SVD is used to refine the roughly filtered target component. Finally, the phase of the refined signal is extracted to resample the original signal. The performance of the method was tested using real vibration signals collected from a large-scale test rig of a high-speed train traction system.

A Newly Robust Fault Detection and Diagnosis Method for High-Speed Trains

Incipient faults in high-speed trains are usually masked by noises and disturbances from process and sensors, which severely increases the difficulty of incipient fault detection and diagnosis. By introducing Hellinger distance into multivariate statistical analysis framework, this paper develops a robust detection and diagnosis method for incipient faults under the principal component analysis. The proposed method can detect all incipient sensor faults in traction systems of high-speed trains in real time by comparing reference probability density functions (PDFs) with the online estimated PDFs. According to the fault detection information, an accurate fault diagnosis can be achieved online through Bayesian inference. Key advantages of the proposed method are its salient robustness to unknown noises and disturbances, as well as the high sensitivity to incipient faults. In addition, the proposed method does not require any information on system models of high-speed trains or any human intervention. The effectiveness of the proposed method has been firstly proven by mathematical derivations and then been verified by numerical simulations. Finally, the proposed method has been applied to the practical experiment platform of the high-speed trains.

FPGA-Based Real-Time Simulation of High-Power Electronic System With Nonlinear IGBT Characteristics

The hardware-in-the-loop simulation plays a vital role in the test of high-power electronic system. Although the application of field-programmable gate array (FPGA) embedded system has enabled the real-time system simulating below 500 ns, the transient characteristic of high-voltage insulated-gate bipolar transistor (HVIGBT) is largely compromised. In this paper, a new piecewise HVIGBT model, considering its driver circuit effect and parasite parameter, is proposed for FPGA-based real-time simulation applications. With the attempt to reduce the simulation latency, we propose an FPGA solver with a parallel structural to divide the system into several layers. The model could not only provide accurate system-level performance of the power electronic converter but also give an insight into the transient behavior effect of high-power electronic system. Finally, a case study about emulation of traction system of high-speed train is also presented. Implementations are made on an FPGA Kintex-7 embedded in National Instruments FlexRIO PXIe-7975. The obtained results show that the proposed modeling algorithm can achieve both accuracy and efficiency within a fixed real-time simulation time step of 25 ns.

Real-time incipient fault detection for electrical traction systems of CRH2

Abstract Electrical traction systems in a high-speed train are the core parts to provide traction force for the whole train. Due to performance degradation of electronic components and the prolonged operation under variously complicated operating environments, incipient faults will inevitably happen and will evolve into faults or failures if they are not successfully detected. Currently, the univariate control charts are used to monitor electrical traction systems of high-speed trains. However, this primitive solution is unable to deal with incipient faults with satisfactory performance. In this paper, a Kullback–Leibler divergence (KLD) and independent component analysis (ICA)-based method is proposed to perform incipient fault detection (FD) in electrical traction systems. Compared with the existing ICA-based methods, the proposed strategy is more sensitive to incipient faults; meanwhile it has low computational load because estimating the probability density functions (PDFs) of the derived independent components and the residuals is avoided. On the experimental platform of the traction system for China Railway High-speed 2-type (CRH2) trains, three typical incipient faults are successfully injected, and the proposed method is successful in detecting these incipient faults.

Accurate Prediction of RUL under Uncertainty Conditions: Application to the Traction System of a High-speed Train

Traction systems of high-speed trains suffer with many uncertainties in their degraded process. The uncertainties mainly include the inherent uncertainty associated with the progression of the degradation over time and the inevitable uncertainty caused by noise and disturbance. Handling uncertainties is important to improve the prediction accuracy of remaining useful life (RUL) in the degraded process from fault to failure. This study takes uncertainties into consideration via the relevant vector machine (RVM) approach in order to achieve a good accuracy. Firstly, a target vector is derived by first hitting time (FHT) to characterize the RUL. This is fulfilled off-line based on historical sample data. Then, a stochastic model is established by RVM under Bayesian framework. Further, parameters are updated using the expectation-maximization (EM) algorithm for an optimized RVM model. And then, the on-line RUL prediction is implemented with the updated RVM model. Finally, the proposed method is demonstrated by two real case studies of traction faults occurred in the high-speed train. The prediction results show the effectiveness of the proposed method.

Distinguished Lecture by Dr. Gerard-Andre Capolino in Singapore [Society News

Dr. Gerard-Andre Capolino, IEEE Fellow and 2012-2013 Industrial Electronics Society (IES) president from the University of Picardie "Jules Verne," Amiens, France, was invited by the Singapore Institute of Manufacturing Technology (SIMTech) to give an IES distinguished lecture (DL) on 25 May 2012, as well as to visit the institute and some laboratories at the Nanyang University of Technology. Dr. Capolino's DL was titled "Condition Monitoring and Fault Detection in Complex Electromechanical Systems - Example of a Traction System." Condition monitoring and fault detection are key issues for electricity production and use. After presenting the structure of the electrical engineering research lab at the University of Picardie, Dr. Capolino went on to present different techniques that have been developed since the 1980s. His DL started by presenting a modeling approach for electromechanical systems with multiple mechanical and electrical faults. After that, classical and new signal-processing techniques for condition monitoring were presented, as well as suitable instrumentations to be implemented in realworld systems. All modern techniques related to condition monitoring in industrial applications for early fault detection in different areas such as railway traction systems, cranes, and wind turbine generators were focused on. The example of a high-speed train traction system was developed and analyzed from the point of view of condition monitoring. The DL, which lasted an hour and a half, was attended by more than 100 participants.

Railway Static Power Conditioners for High-speed Train Traction Power Supply Systems Using Three-phase V/V Transformers

In order to eliminate the negative-sequence and harmonic currents in the high-speed train traction systems with three-phase V/V transformers, a compensation strategy based on the railway static power conditioners (RPC) is proposed in this paper. An RPC contains two converters that are connected back to back by sharing the same dc link. In this paper, the structure and principle to compensate negative-sequence currents for the RPC with a three-phase V/V transformer are explained, and a strategy to provide the compensation references for negative-sequence and harmonic currents is proposed. Also, a method to separate active current, reactive current, and harmonic current references from the total negative-sequence and harmonic current references is given. Moreover, a controller is proposed to maintain the dc-link voltage and to compensate the negative-sequence and harmonic currents. Simulation and experimental results are provided to demonstrate that the proposed strategy is very effective.