Safe Operation Of High-speed Trains Research Articles

Research on frequency shift signal detection algorithm for jointless track circuits based on improved relaxation algorithm

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

Laboratory Experimental Investigation on the Structural Optimization of a Novel Coupled Energy Tunnel

Freezing damage to tunnels in cold regions has long posed a threat to the safe operation of high-speed trains and other means of transportation. Finding a reasonable and effective solution to this problem, while also considering green, low-carbon, energy-saving, and environmental protection measures, has garnered widespread attention. Herein, the concept of a novel coupled energy tunnel is proposed, which combines the technologies of an air curtain and ground source heat pump (GSHP). The aim is to effectively address the issue of freezing damage in tunnels located in cold regions, while ensuring traffic safety. First, the multifunctional experimental apparatus for testing the anti-freezing and insulation performance of a coupled energy tunnel was independently designed and developed for laboratory experiments. Second, single-factor experiments and orthogonal experiments are conducted, and the influences of five key factors (i.e., the air outlet hole diameter, air outlet hole spacing, circulating water temperature of the GSHP, wind speed at the tunnel model entrance, and airflow jet angle) on the internal temperature field of the tunnel model are discussed. Third, combined with range analysis and variance analysis, the ranking of importance for each key factor and the optimal scheme of the coupled energy tunnel are obtained as follows: wind speed at the tunnel model entrance D > circulating water temperature of GSHP C > airflow jet angle E > air outlet hole spacing B > air outlet hole diameter A, and the optimal scheme is A2B1C4D1E2, i.e., the air outlet hole diameter is 3 mm, the air outlet hole spacing is 10 mm, the circulating water temperature of GSHP is 50 °C, the wind speed at the tunnel model entrance is 1.5 m/s and the airflow jet angle is 45°. In conclusion, the research achievements presented in this paper can offer a new perspective for the structural design of tunnels in cold regions. Additionally, they contribute to the early achievement of a carbon dioxide emissions peak and carbon neutrality, and provide some valuable and scientific references for both innovators and practitioners.

Prediction of thermal regime and frost heave deformations of subgrade soils during non-stationary heat and moisture transfer

In order to ensure the safe operation of high-speed trains in cold regions, frost heave is one of the basic problems in the research for prediction and control roadbed deformation in such regions. In this paper, based on the classical hydrodynamic model we developed a method of studying thermal regime of soils and predicting frost heave deformations of subgrade soils. Our coupling model takes into account the mutual influence of temperature and soil moisture during non-stationary processes of heat and moisture transfer, calculation of frost heave deformation based on calculated result of the temperature and moisture fields of the roadbed. We have also observed the change of the soil temperatures and frost heave deformation in a freezing-thawing cycle at the geodesic center of PGUPS. The field observation results show that the soil temperature variations amplitude decreases with depth. The simulation verification results and the field observation data show good reproducibility, thus the reliability of the model is confirmed. At last, the paper presents the application of the method in improving high speed railway lines (HSRL) subgrade design in order to reduce frost heave deformations. Based on the results of calculations, it is recommended to lay a thermal insulation layer at the depth of 0.2 m below the subgrade and on the slopes to reduce frost depth and frost heave deformations of subgrade soils.

Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions.

The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.

Thermal fatigue life prediction of brake disks based on coupled thermal simulation

The thermal-mechanical performance of friction pairs of the foundation brake gear is an important guarantee for the safe operation of high-speed trains. The thermal stress of brake disks under high temperature is the primary cause of thermal fatigue damage. In this article, the brake bench tests were carried out under braking speed of 350km/h and a thermo-mechanical-wear coupled model was established to simulate tests. By analyzing the results of bench test and finite element simulation, it shows the top temperature of brake disk was over 600°Cand the friction surfaces of brake friction pair show obvious wear. Combining the FE simulation and test results of the thermal fatigue sample, the thermal fatigue characteristics of brake disk material were studied. Based on the residual stress results, the propagation life of brake disk thermal fatigue crack was estimated. It shows that when the crack length increases to 10mm, the crack propagation life of brake disk is halved, and continues to decrease as the crack grows.

Track Irregularity Identification Method of High-Speed Railway Based on CNN-Bi-LSTM.

Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train.

Early fault diagnosis strategy for high-speed train suspension systems based on model-agnostic meta-learning

Early fault diagnosis of suspension systems is essential for the safe operation of high-speed trains. However, neural network-based fault diagnosis methods have two remaining issues: the fault samples are difficult to obtain in practice and the early fault features are too weak to be extracted directly from the raw vibration signals using neural networks. A novel strategy is proposed for early faults diagnosis in suspension systems (i.e. component performance degradation within 20%) by integrating a new sample reconstruction method, a new grouping normalisation method, and model-agnostic meta-learning (MAML) algorithm. First, the 1D raw vibration signals are converted to 2D feature matrices consisting of artificial features using the sample reconstruction method; meanwhile, the grouping normalisation method is used to enhance the early weak fault features in the feature matrices. Second, MAML specialises in few-shot model training for early fault diagnosis, with the feature matrices as the training samples. Finally, the results are compared with those obtained using other current methods. The numerical results show that the proposed strategy yielded excellent performance in the few-shot early faults diagnosis of suspension systems, achieving a maximum accuracy of 94.75%.

Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains. For this reason, a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper. First, a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples. Second, an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image, which improved the accuracy of defect detection. Subsequently, to optimize the detection performance of the proposed model, the Mish activation function was used to design the block module of the feature extraction network. Finally, the proposed rail defect detection model was trained. The experimental results showed that the precision rate and F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} score of the proposed method were as high as 98%, while the model’s recall rate reached 99%. Specifically, good detection results were achieved for different types of defects, which provides a reference for the engineering application of internal defect detection. Experimental results verified the effectiveness of the proposed method.

Study on the mechanism of mechanical properties deterioration of brake disc materials of high-speed train

The mechanical properties of high-speed train brake disc will deteriorate after service, it is necessary to study the degradation mechanism in order to better understand the service process of the material. In this study, the microstructure evolution of the material in the hot spot area of the brake disc was observed by microstructure analysis, and a mixed structure of coarse and fine grains was formed. Through DIC tensile test and in-situ tensile test, the mechanism of mechanical property degradation of brake disc was obtained. The fine grain zone in the hot spot area forms a constraint effect on the coarse grain zone, resulting in uncoordinated deformation. It further causes strain localization and micro-cracks, resulting in a decrease in the bearing capacity and strength of the material. According to EBSD analysis, it is found that the distribution difference of grain boundary orientation further increases the degree of uncoordinated deformation between coarse and fine grains, resulting in stress concentration at the junction and inducing the formation of microcracks. The research results provide guidance for crack damage identification of brake disc. It is suggested that attention should be paid to the change of microstructure during the condition-based maintenance of brake disc, which is of great significance to ensure the safe operation of high-speed trains.

Improved RAkEL's Fault Diagnosis Method for High-Speed Train Traction Transformer.

The traction system is very important to ensure the safe operation of high-speed trains, and the failure of the traction transformer is the most likely fault in the traction system. Fault diagnosis in actual work relies largely on manual experience. This paper proposes an improved RAkEL (Random k-Labelsets) algorithm for the fault diagnosis of high-speed train traction transformers. Firstly, this article starts from the large amount of "sleeping" fault maintenance data accumulated by the railway department, takes a single maintenance record as an instance, uses specific monitoring values to construct an instance vector, and uses the fault phenomena corresponding to the monitoring indicators as labels. Then, this paper improves the step of selecting k-labelsets in RAkEL, and extracts associated faults using the Relief algorithm. Finally, this paper excavates and uses the association rules between data and faults to identify traction transformer faults. The results showed that the improved RAkEL diagnostic method had a significant improvement in the evaluation indicators. Compared with other multi-label classification algorithms, including BR (Binary Relevance) and CLR (Calibrated Label Ranking), this method performs well on multiple evaluation indicators. It can further help engineers perform timely maintenance work in the future.

Study on mathematical model construction of typical gorge wind field

Wind conditions in gorges have a significant impact on the safe operation of high-speed trains, due to the lack of a unified gorge wind model and the limitations of traditional simulations that use oversimplified wind models, the complex wind speed distribution arising from the mountain surface boundary layer cannot be accurately captured. To address this, a three-dimensional, incompressible, steady calculation method is used to study wind field characteristics in a typical gorge. We propose a two-dimensional mathematical model to study the effects of gorge width on model parameters, including wind speed growth indices α1 and α2 in the height and horizontal directions, respectively. Our results demonstrate that the thickness of the mountain boundary layer can reach a maximum of approximately 30 metres, and the values of α1 and α2 range from 0.11 to 0.19 and 0.21 to 0.5, respectively. As gorge width increases, boundary layer thickness remains constant, α1 gradually decreases, α2 remains unchanged above 250 m height. Our findings provide more accurate boundary conditions for numerical simulations of high-speed train operation in gorge wind conditions and offer theoretical recommendations for safe high-speed train operation through bridges, tunnels, and railways in mountainous regions.

Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes

A data-driven fault diagnosis method is proposed in this study to address the challenge of handling a large volume of pressure data in the air brake pipe of high-speed trains. The suggested method utilizes a BP (back propagation) neural network to transform the time series pressure data into model elements in the model space, ensuring simplicity and stability. Various fitting functions, including Fourier basis, Gaussian basis, polynomial basis, sine basis, and others, are employed to accurately fit the pressure curve of the air brake pipe. The fault diagnosis process involves two steps: classifying the fault based on an optimal approximation equation and diagnosing it by analyzing the topological relationship of the model elements in the model space. The proposed method achieves an average fault diagnosis accuracy of 89.8%, with high accuracy rates for different fault states: 98% for normal state, 88% for blockage state, 84% for leakage state, and 96% for compressor fault state. Compared to the hidden Markov model method, the proposed method improves the average diagnostic accuracy by 2% for known working conditions and 4.87% for all working conditions, demonstrating its effectiveness and reliability. The fault diagnosis of the air brake tube in high-speed trains is of great significance, which aims to realize accurate fault diagnosis and prediction through sensor data monitoring and signal processing technology, so as to ensure the safe operation of high-speed trains. These studies provide an important theoretical and practical basis for the improvement and application of fault diagnosis methods.

Dynamic response of high-speed train-track-bridge coupled system under combined wind and snow

The dynamic response of high-speed trains under meteorological hazards concerns operation safety and serviceability. This paper investigates the dynamic response of high-speed train-track-bridge coupled system subject to combined wind and snow. A numerical model integrating a computational fluid dynamics model and a multi-body dynamics model was established to predict the dynamic response of the couple system. The computational fluid dynamics model was used to analyze the aerodynamic loads applied to the coupled systems. The multi-body dynamics model was used to analyze the dynamic response of the coupled system under the aerodynamic loads. The effects of train speed, wind speed, and snow depth on the dynamic response were evaluated. A law was formulated to specify the speed limits for safe operation of high-speed trains. The results showed that combining wind and snow aggravated the dynamic response of high-speed trains via altering the aerodynamic loads applied to the coupled system. This research will promote practical operation and management of high-speed trains.

A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train

High-speed train is a large-scale electromechanical coupling equipment with a complex structure, where the coupling is interlaced between various system components, and the excitation sources are complex and diverse. Therefore, reliability has become the top priority for the safe operation of high-speed trains. As the operating mileage of high-speed trains increases, various key systems experience various degrees of performance degradation and damage failures. Moreover, it is accompanied by the influence of external environmental high interference noise and weak early fault information. Thus, those factors are serious challenges for the condition monitoring and fault diagnosis of high-speed trains. Therefore, this paper summarizes the research progress and theoretical results of the fault detection, fault isolation, and fault diagnosis methods of the key systems of high-speed trains. Finally, the paper summarizes the applicability of the main methods, discusses the challenges and opportunities of condition monitoring and fault diagnosis of high-speed trains, and looks forward to improving its diagnosis level.

Study on power spectral density curves of track dynamic irregularity caused by earthquake-induced damage

A series of studies on track dynamic irregularity caused by earthquake-induced damage should be conducted in order to guarantee safe operation of high-speed trains. In this study, a nonlinear time history analysis of high-speed railway CRTS Ⅱ track-bridge system under transverse random earthquakes is carried out, and the post-earthquake stiffness degradation degree of main components of track-bridge system is analyzed. A three-dimensional coupled vibration model of train-track-bridge system based on earthquake-induced damage is established, and a sample library of earthquake-induced track dynamic irregularity is constructed. Kolmogorov-Smirnov method is used to test the normal distribution of earthquake-induced irregularity samples. A calculation method of the earthquake-induced dynamic irregularity power spectral density curves based on probability guarantee rate is proposed. The power spectral density curve is fitted by piecewise polynomial, and the influence of train operation speeds and peak ground accelerations (PGAs) on the power spectrum of earthquake-induced dynamic irregularity is analyzed. The results demonstrate that the degradation degree of piers and bearings of the track-bridge system under transverse random earthquake presents the characteristics of large in the middle and small on both sides. The earthquake-induced track directional irregularity samples follow the normal distribution. The shape of the power spectral density curve has obvious segmentation characteristics in the double logarithmic coordinate system. The power spectrum amplitude decreases significantly with the increase in spatial frequency and fluctuates significantly in the high frequency band. There are little influence of the train operation speeds on the power spectrum curves. In contrast, the influence of the PGAs on the power spectrum is significant. The amplitude of the power spectrum increases obviously as the PGA increases.

Improved Interval Prediction of Small-Amplitude Hunting of High-Speed Trains

Hunting is an important factor in the safe operation of high-speed trains. Most techniques used for monitoring hunting aim at detecting hunting occurrence and some of them can deal with small-amplitude hunting. However, they do not provide information about evolution of small-amplitude hunting. The present work studies the evolution of small-amplitude hunting with the goal of predicting the occurrence of the hunting instability. An improved method contained multiple hidden for predicting the interval of the amplitude of small-amplitude hunting is proposed, which is improved via a two-level model. The method is computationally efficient and converges rapidly. Upon applying the proposed method to high-speed train data, the coverage probability of the resulting prediction interval is 100% and its normalized average width is 0.187, which means a higher coverage and smaller width than the interval predicted by existing methods. The confidence-level of the prediction is also high.

Regularity and sensitivity analysis of main parameters of plate effects on the aerodynamic braking drag of a high-speed train

Abstract With increase of train speed, braking plate technology has a good application prospect in the high-speed stage of the train. Based on the 1/8 scaled symmetrical train model composed of two half cars, the Reynolds Average Navier-Stokes (RANS) equations and Shear Stress Transfer (SST) k-ω turbulence model are adopted to simulate the aerodynamic performance of the train with plate. The aerodynamic drag dependence of single parameter of the plate (shape, area, angle, position and number) is analysed, and identification research of the main aerodynamic parameters of the plate is carried out. The numerical settings used in this paper are verified by wind tunnel test data. Results show that the braking plate with an aspect ratio of one has better performance on aerodynamic drag. The area, opening angle and number of plates are basically positively correlated with the total aerodynamic drag of the target car and plate. Arranging plates at the downstream of the vehicle is a good method of raising total aerodynamic drag. Within the range of plate parameter design in this paper, by using orthogonal design of experiment and the method of range analysis and analysis of variance, the influence degrees of plate parameters on aerodynamic drag are determined, and the order is number, area and opening angle of plate. The research results provide theoretical support for the design and safe operation of high-speed trains with aerodynamic braking plates.

Effect of frost heave deformation of bridge foundation on operation safety of high-speed railway

The frost heave deformation of the bridge foundation in seasonal frozen soil regions threatens the safe operation of high-speed trains. The first high-speed railway in extremely cold zones, the Harbin-Dalian Line, is used as the background in this study, and finite element analysis (FEA) is used to investigate the impact of freezing temperature and moisture content on the frost heave force and deformation of the bridge foundation. Furthermore, the dynamic response of the train crossing the frost heave deformation region of the bridge foundation is analyzed based on finite element (FE) and multi-body dynamics co-simulation. Conclusively, the frost heave threshold of the bridge foundation is proposed in accordance with the limit of the train’s dynamic response indicators. The results indicate that the normal frost heave stress at the bottom section of the cap is distributed in a “W” shape along the lateral direction of the cap. The magnitude of the stress at the edges of the cap and the inner hole of the pile foundation is relatively large. the frost heave deformation is negatively and positively linked with the fluctuations of temperature and moisture content, respectively. The train's vertical acceleration grows gradually as the pier's upward deformation increases, while there is no discernible relationship between the train’s lateral acceleration and the pier’s upward deformation. Additionally, variations in the train speed and the pier’s upward deformation are positively correlated with the derailment coefficient and wheel load reduction rate. To enable the high-speed railway’s operation safety, it is proposed that the deformation threshold of frost heave be 14.4 mm.

Diagnosis Method for Multiple Compensation Capacitor Faults of Jointless Track Circuit

The compensation capacitor fault of jointless track circuit has an important impact on the safe operation of high-speed trains. In order to solve the problem of multiple compensation capacitors breaking at the same time in jointless track circuit, a fault diagnosis method based on the amplitude characteristics of induced voltage envelope curve is proposed. Firstly, the shunt state model of track circuit is established, and the envelope curves of induced voltage amplitude corresponding to the disconnection fault of compensation capacitors at different positions are obtained by simulating analysis. Secondly, taking the distance between adjacent compensation capacitors as a step, the envelope curves of induced voltage amplitude corresponding to the faults of compensation capacitors at different positions are calculated, and the curves are segmented by according to the half step distance, and the eigenvalues of each segment are computed. Thirdly, the characteristic values of each segment are detected from the transmitter to the receiver to find the specific values, so as to realize the preliminary fault diagnosis of the compensation capacitor. Then, in order to make the fault diagnosis of compensation capacitor more accurate, according to the unique fault type, the slope is employed as the constraint condition for secondary diagnosis, so as to locate the fault capacitor position when multiple compensation capacitors break at the same time. Finally, by building a simulation platform and selecting the actual data of a road section, the proposed method is verified and compared. The results show that the algorithm can diagnose the position of the fault capacitor when multiple compensation capacitors on the track circuit break at the same time. The accuracy of the proposed algorithm is 99.04%, and it has high real-time performance.

Characteristics of rail deformation caused by tunnel floor heave and corresponding running risk of high-speed train

• A four-year-long observation of uneven rail deformation is performed in two HSR tunnels. • The predominant wavelength and amplitude of rail deformation in tunnel are 3–30 m and 2–10 mm, respectively. • The heave transfer within track structure exhibits more significantly on wavelength than on amplitude. • Speed raising has strong effects on the magnitude and location of wheel-rail contact force. • The risk of derailment and passenger comfort is more sensitive to short- and long-wavelength floor heave, respectively. Floor heave is a huge threat to the safe operation of high-speed trains in tunnels. However, rare research recognizes the characteristics of the rail irregularity caused by tunnel floor heave and the corresponding running risk of high-speed trains. In this paper, uneven rail profiles observed within four years in two HSR tunnels are used to investigate the temporal variation and spatial distribution of tunnel floor heave. The predominant wavelength and amplitude of the heave profile are determined. Hypothetic floor heave profiles with a wide range of wavelength and amplitude are used as the input in the simulation of the dynamic response of a VRT (vehicle-rail-track) system. Based on the simulation, the transfer laws for the wavelength and amplitude of heave within the track structure are revealed. The wheel-rail contact force, the wheel load reduction rate, and the sperling factor under various floor heave profiles are calculated. The floor heave profile that may lead to a high risk of derailment and low passenger comfort is presented for two train speeds. The research reveals that the predominant range of the wavelength for the rail heave in the tunnel is from 3 m to 30 m, and that of the amplitude is 2 mm to 10 mm. The heave transfer law within the track structure is strongly affected by both the wavelength and amplitude of the floor heave profile. The deformation of the rail varies more significantly on wavelength than on amplitude. Speed raising strongly influences the wheel-rail contact force, the wheel load reduction rate, and the sperling factor. The passenger comfort is more sensitive to longer-wavelength floor heave, whereas the risk of the derailment is more sensitive to shorter-wavelength floor heave.