Safe Operation Of High-speed Railway Research Articles

Fatigue life prediction for CA mortar in CRTS II railway slab track subjected to combined thermal action and vehicle load by mesoscale numerical modelling

The fatigue life of the cement asphalt (CA) mortar in the slab track of the China Railway Track System (CRTS) II is crucial to the smooth and safe operation of high-speed railways. To investigate the fatigue life of the CA mortar under the combined thermal action and vehicle loads, this paper develops a new coupled thermal-mechanical finite element (FE) model for the concrete slab track at the mesoscale. First, the meteorology and heat transfer principle are adopted to simulate the temperature distribution of the concrete slab track. The heterogeneous characteristics of the concrete of the precast slab are modelled by the three-phase composite material at the mesoscale using the new developed random aggregate algorithm. Then, the energy-based exponential cohesive zone model is adopted to simulate the interface between the CA mortar and the precast concrete slab. The proposed mesoscale numerical model can provide reliable predictions for the temperature distribution and the deformation of the precast concrete slab under thermal action, which are confirmed by the relevant field measurements. Finally, the fatigue life of the CA mortar is estimated by the nonlinear damage cumulative method associated with the stress time history under the combined thermal action and vehicle load. From the obtained results for the numerical example, the thermal action can decrease severely the fatigue life of the CA mortar, and the fatigue life varies significantly at different positions, with the most vulnerable zone located near but not directly below the rails.

Intelligent Detection of Surface Defects in High-Speed Railway Ballastless Track Based on Self-Attention and Transfer Learning

The detection of ballastless track surface (BTS) defects is a prerequisite for ensuring the safe operation of high-speed railways. Traditional convolutional neural networks fail to fully exploit contextual information and lack global pixel representations. The extensive stacking of convolutions leads deep learning models to play a black-box detection role, lacking interpretability. Due to the current lack of sufficient high-quality surface data for ballastless tracks, it is a severe constraint on the accurate identification of the substructure state in high-speed railways. This paper proposes an intelligent detection method for BTS defects named TrackNet based on self-attention and transfer learning. The method enhances the fusion ability of global features of BTS defects using multihead self-attention. The model’s dependence on extensive defect data is reduced by transferring knowledge from large-scale publicly available datasets. Experimental results demonstrate that compared to advanced Swin Transformer model results, the TrackNet model achieves improvements in average accuracy and F1-score by 5.15% and 5.16%, respectively, on limited test data. The TrackNet model visualizes the decision regions of the model in identifying BTS defects, revealing the black-box recognition mechanism of deep learning models. This research performs engineering applications and provides valuable insights for the multiclass recognition of BTS defects in high-speed railways.

Numerical analysis of dropper stress under a moving load based on the uplift displacement for a high-speed railway

<abstract> <p>The most essential cause of the fracture of the dropper is the effect of alternating stress for a long time. Therefore, in order to ensure the safe operation of high-speed railways, the influence of moving loads on the stress of a dropper was investigated in this study. Due to a high-voltage catenary system, it is very difficult to measure the moving load. Thus, the uplift displacement measured by some software and hardware devices has been applied to the contact wire instead of the moving load. The response equation for the contact wire has been derived so as to determine the initial and boundary conditions of each dropper. Then it was combined with the equation for vibration analysis of the dropper and the stress of each dropper was calculated by using the finite-difference method based on a written MATLAB program. The results show that the dropper stress, during a certain period goes through two stages of immediate rebound and bending compression when the uplift displacement is large. After the pantograph passes, the vibration of the dropper tends to be smooth; also, dropper stress variation with timecan be described by three stages: immediate rebound, vibration attenuation, and bending compression. In addition, the maximum tensile stress of dropper Ⅳ was the highest. It indicates that dropper Ⅳ was more prone to fracture than other droppers.</p> </abstract>

Fault Diagnosis of a Switch Machine to Prevent High-Speed Railway Accidents Combining Bi-Directional Long Short-Term Memory with the Multiple Learning Classification Based on Associations Model

The fault diagnosis of a switch machine is vital for high-speed railway operations because switch machines play an important role in the safe operation of high-speed railways, which often have faults because of their complicated working conditions. To improve the accuracy of turnout fault diagnosis for high-speed railways and prevent accidents from occurring, a combination of bi-directional long short-term memory (BiLSTM) with the multiple learning classification based on associations (MLCBA) model using the operation and maintenance text data of switch machines is proposed in this research. Due to the small probability of faults for a switch machine, it is difficult to form a diagnosis with the small amount of sample data, and more fault text features can be extracted with feedforward in a BiLSTM model. Then, the high-quality rules of the text data can be acquired by replacing the SoftMax classification with MLCBA in the output of the BiLSTM model. In this way, the identification of switch machine faults in a high-speed railway can be realized, and the experimental results show that the Accuracy and Recall of the fault diagnosis can reach 95.66% and 96.29%, respectively, as shown in the analysis of the ZYJ7 turnout fault text data of a Chinese railway bureau from five recent years. Therefore, the combined BiLSTM and MLCBA model can not only realize the accurate diagnosis of small-probability turnout faults but can also prevent high-speed railway accidents from occurring and ensure the safe operation of high-speed railways.

Data assimilation of PS-InSAR vertical deformation into a frost heave model to analyze subgrade deformation of high-speed railway in northwest China

Temperature changes may cause irregular soil uplift or thawing settlements in frozen soil areas, potentially affecting the safe operation of High-Speed Railways (HSR). Analyzing and predicting these deformation characteristics is thus critical. However, the conventional forecasting and analysis techniques rarely considered factors such as dynamic parameter variations, uncertainties, and measurement errors, which hinder accurate regional scale forecasting. To bridge this gap, this paper introduces a novel time-series coupling method, which integrates post-processing deformation from Interferometric Synthetic Aperture Radar (InSAR) with a frost heave model (FHM), facilitated by the ensemble Kalman filter (EnKF) assimilation algorithm. We obtained deformation observations along the HSR using Persistent Scatterer InSAR (PS-InSAR) technology in combination with time-series post-processing techniques. Considering the causative factors for deformation, we structured the FHM. By integrating FHM with observational data using the EnKF algorithm achieved an efficient upgrade of the posterior distribution of model parameters. This integration significantly improves the predictive accuracy, it facilitates an efficient update to the posterior distribution of model parameters, leading to enhanced prediction accuracy of our model. Our experimental results indicate that the effectiveness of this approach, with observational data assimilation into FHM reducing the average Root Mean Square Error (RMSE) to a mere 0.247 mm. Concurrently, both the Normalized Reduction Error Index (NER) and the Assimilation Efficiency Factor (EFF) values surpassed 0.60 and 0.84 respectively. These underlines signify a successful update of our model parameters, which in turn elevates the accuracy of future deformation predictions, thereby promising safer railway operations.

Nondestructive determination of longitudinal rail stress from guided wave dispersion properties

The determination of the longitudinal stress in continuous welded rails is essential for the safe operation of high-speed railways. Guided wave inspections have many advantages, and can provide an effective tool for non-destructive measurements of the longitudinal stress in rails. This paper provides a new method for determining longitudinal rail stress using ultrasonic guided wave dispersion properties. First, the characteristic equations of guided waves propagating in a waveguide with existing longitudinal stress are constructed, generating the dispersion properties of the waveguide such as the wavenumber, phase velocity and group velocity. The sensitivity of the dispersion properties of propagative modes to the longitudinal rail stress is investigated, which is then used for selecting appropriate modes for guided wave inspections. On the basis of the perturbation method, the relationship between the change in longitudinal rail stress and the change in dispersion properties is developed, which can be used for determining the longitudinal rail stress from dispersion properties. Furthermore, a mode selection index considering both the stress sensitivity and mode shape characteristics is employed for selecting appropriate propagative modes for guide wave inspections. Finally, a numerical example for CHN60 rail is adopted to demonstrate the effectiveness of the proposed method. From the results, the proposed method can accurately determine the longitudinal stress in the rail by using the dispersion properties available.

Mesoscale modelling of CRTS II slab tracks subjected to thermal action and vehicle loads

Evaluation of the damage and deformation of cast-in-situ concrete joints between the precast concrete track slabs of the China Railway Track System (CRTS) II is crucial to the safe operation of high-speed railways. To investigate the damage and deformation evolution of concrete joints under thermal action (caused by the natural meteorological environment) and vehicle loads, a two-dimensional thermal–mechanical coupled numerical model of concrete joints at the mesoscale was developed. This model analyses the influence of three factors – concrete strength, maximum diameter of the joint concrete aggregate and vehicle speed. First, meteorology and heat transfer theory were used in thermal simulations. Then, the non-linear characteristics of the joint concrete were modelled as a two-phase composite material based on the random aggregate algorithm and strain-based elastic damage theory at the mesoscale. The cohesive zone model was used to simulate the interfaces between precast slabs. The reliability of the proposed model was assessed using field measurements. The results of a numerical example showed that the maximum aggregate diameter of the joint concrete significantly affected damage evolution in the joint concrete and concrete strength has a slight effect on joint uplift.

Handover Optimization Algorithm Based on T2RFS-FNN.

As a key technology for highly reliable communication in the fifth generation mobile communication for railway (5G-R) high-speed railway wireless communication system, once the handover fails, it will pose a serious risk to the safe operation of high-speed railway. As the speed of high-speed trains continues to increase, the handover will become more frequent, and how to improve the success rate of the handover is a key problem that needs to be solved. In this paper, we proposed an optimization algorithm based on the interval type 2 feature selection recurrent fuzzy neural network (T2RFS-FNN), which is a recurrent fuzzy neural network with interval type 2 feature selection, to address the problem of fixed hysteresis threshold and single consideration for the handover algorithm between the control plane and the user plane of the high-speed railway under 5G-R. The algorithm integrates reference signal receiving power (RSRP). Reference signal receiving quality (RSRQ) and throughput to optimise the hysteresis threshold. First, a feedforward neural network structure is designed to implement fuzzy logic inference, and an interval type-two Gaussian subordination function is used to improve the nonlinear expressiveness of the model. Then, a feature selection layer is added to determine the output of the affiliation function, which completes the optimization of the hysteresis threshold and overcomes the drawback of the fixed hysteresis threshold of the handover algorithm. Finally, simulation analysis of the control-plane and user-plane handover algorithms is carried out separately. The results show that the proposed method can effectively improve the success rate and reduce the ping-pong handover rate compared to the comparison algorithms. The results provide a theoretical reference for the speedup of high-speed railway trains and the evolution of the global system for mobile communications for railway (GSM-R) to 5G-R.

Study on the Interaction between Wheel Polygon and Rail Corrugation in High-Speed Railways.

The wheel polygonization and rail corrugation are typical wheel-rail periodic wear problems, which seriously affect the safe operation of high-speed railways. In the present paper, the interaction between the wheel polygon and the rail corrugation in the long-slope section of high-speed railways is mainly studied based on theory of friction coupling vibration. Firstly, the simulation model of the wheel-rail contact model is established, as well as the polygonal wear of the wheel and the corrugated wear of the rail. Then, the stability analyses of the wheel-rail system with periodic wear are studied, in which the four working conditions of smooth rail-smooth wheel, polygonal wheel-smooth rail, smooth wheel-corrugated rail and polygonal wheel-corrugated rail are compared. Finally, the competition mechanisms between the wheel polygon and rail corrugation under different parameters are discussed, including the wheel-rail friction coefficient and the depth of periodic wear of the wheel-rail system. The numerical results show that both the periodic wear of the wheel and rail with certain relevance will increase the friction coupling vibration of the wheel-rail system, which may aggravate the subsequent relevant wheel polygonal and rail corrugation wear. With the increase of the friction coefficient between wheel and rail, as well as the depth of the wheel polygon and rail corrugation, the vibration trend of the friction coupling vibration of the wheel-rail system increases gradually. Moreover, the proportion of the wheel polygon's influence on the friction coupling vibration of the wheel-rail system is greater than that of rail corrugation.

Train wheel degradation generation and prediction based on the time series generation adversarial network

To ensure the safe operation of high-speed railways, it is necessary to assess the reliability of its key components. Among them, as wheels are prone to wear degradation and the wear data acquisition process has the disadvantages of high cost and long cycle. There are few wheels degradation samples, which in turn makes the wheel degradation prediction have large errors. Hence, this paper uses the time series generator adversarial network (TimeGAN) to generate synthetic wheel degradation, in which the original data is segmented through a sliding window to obtain more input sets, and the noise distribution in the generator network is combined with the stationary gamma process (SGP). Then, the wheel degradation at measured distance k is predicted by the Gated Recurrent Unit (GRU) network. To evaluate the effectiveness of the proposed method, different methods in this paper are conducted for the experiment comparison. The experiment result shows that the proposed method has a better effect on the generation of train wheel degradation, and the Kullback-Leibler (KL) divergence and the prediction error are the smallest in the comparison. Hence, the proposed method can provide support for the further reliability analysis of railways and further ensure their operational safety.

Study on contactless measurement method of dropper load

The fatigue fracture of dropper will threaten the safe operation of high-speed railway. The accurate measurement of dropper load plays an important role in dropper fatigue test and service life prediction. In this work, according to the structural characteristics of catenary suspension and based on string vibration theory, a contactless method for measuring dropper load is proposed. In this method, the edge recognition technology is used to obtain the vibration displacement time history data of the dropper clamp position on the messenger wire, the random vibration is transformed into the linear superposition of sinusoidal vibration through Fourier transform to solve the vibration differential equation. Through the relationship between the shape change of messenger wire and concentrated force, the change of dropper load is analyzed. The measurement accuracy is tested on the test platform built in the laboratory, and the results show the measurement accuracy can reach 0.50%.

Research of ZnO Arrester Deterioration Mechanism Based on Electrical Performance and Micro Material Test

The traction power supply system of an Electrical Multiple Unit (EMU) often suffers from overvoltage impact. As an important protection device for on-board electrical equipment, the working environment of a roof arrester is worse than that of a power system. In recent years, the explosion failure of the roof arresters of an EMU has occurred from time to time, which seriously endangers the safe operation of high-speed railways. In this paper, the electrical performance test and material micro test of roof arrester in three states of normal, defect, and exploded, are carried out in order to study the internal causes of roof arrester explosion and clarify its deterioration mechanism. Using the DC reference voltage test and leakage current test, the electrical performance differences of normal, defective, and exploded arresters are obtained. By studying the disassembly of an arrester, the appearance characteristics of arrester varistor in three states are obtained. The micro morphology and chemical elements of the varistor are analyzed by Scanning Electron Microscope and Energy Dispersive Spectrometer. The deterioration mechanism of the arrester varistor is then revealed, and preventive measures for the explosion failure of the roof arrester are put forward. The obtained results show that, during the long-term operation of the roof arrester of an EMU, the varistor may be damp, and therefore the aluminum electrode layer and side insulation layer of the varistor may deteriorate. After the deterioration of the aluminum electrode layer, the content of the O element increases, and multiple film structures are formed on the surface. After the deterioration of the side insulating layer, the content of the O element increases, and the surface becomes uneven. Improving the sealing performance requirements of the roof arrester and optimizing the maintenance process can reduce its explosion failure.

Novel anti-frost subgrade bed structures a high speed railways in deep seasonally frozen ground regions: Experimental and numerical studies

To reduce the threat of frost damage to the speedy and safe operation of high-speed railways (HSRs) in deep seasonally frozen ground regions, it is necessary to study the anti-frost subgrade bed structure to improve the long-term service performance of HSRs. First, the thermal–mechanical behaviour and preventative efficacy of the original anti-frost subgrade bed structure with graded-crushed rock filling materials are studied. Then, the general change laws of the thermal–mechanical behaviours for embankment engineering in deep seasonally frozen ground regions are revealed. Finally, a finite element model is developed to investigate the anti-frost effects of six novel subgrade bed structures with differential filling materials. The results show that 1) the initial freezing time, initial thawing time and freezing rate of the embankment are substantially different from those of the natural ground; 2) the deformation of the embankment is closely related to the filling depth and frost depth of the embankment. Furthermore, the deformation process is closely related to the seasonal air temperature change; 3) the seasonal frozen depth of the embankment can be reduced by changing the heat entering the subgrade bed and the heat diffusion in the embankment body; and 4) an asphalt concrete pavement + cement stabilized macadam + insulation board subgrade bed structure can best reduce the frost depth. This research provides important technical guidance for the design of the anti-frost subgrade bed structure of the HSR subgrade in deep seasonally frozen ground regions.

Novel method for detection of void defects under track slabs using air-coupled ultrasonic sensors

Void defects under track slabs are the main danger affecting the safe operation of high-speed railways. In the short high-speed railway maintenance periods, China’s high-speed railway line maintenance operations must quickly and dynamically determine void defects under track slabs that are in service without contact. However, the detection of void defects under track slabs still mainly relies on the manual inspection and flaw detection by railway workers during the railway maintenance period. If the defects are not quickly identified, the consequences could be disastrous. This article presents a new method for the non-contact dynamic detection of void defects under track slabs. The method involves the use of air-coupled ultrasonic sensors to generate and receive ultrasonic guided waves in the track slab to quantitatively represent the size of the void defect according to the principle of energy leakage of guided waves in the propagation process. The characteristics of the position-amplitude curve, taking the position of the beam axis as the abscissa and the amplitude of the time domain signal as the ordinate, were numerically calculated and analyzed. The quantitative relationship between the convex interval of the position-amplitude curve and the size of the void defect was obtained, and an imaging method of the void defects based on x, y two-dimensional line scanning data fusion is proposed. The excitation and reception methods of air-coupled ultrasonic guided waves were studied, and a 1:1 model of the track structure was built in the laboratory to verify the method and detect the void defect under the track slab. The experimental results show that ultrasonic guided waves can be excited and received in the track slab by air-coupled ultrasonic sensors. Based on the guided wave energy leakage principle, the quantitative characterization and imaging of the void defect under track slabs can be realized.

Experimental and Numerical Investigation on Ballast Flight from Perspective of Individual Particles

Ballast flight significantly affects the safe operation of high-speed railway. A wind tunnel experiment and computational fluid dynamics simulation were performed to investigate the occurrence and development mechanism of ballast flight from the perspective of individual particles, which was based on the capture, reconstruction, and analysis approach of ballast features. The dynamic characteristics of ballast particles under wind load, the flow field characteristics such as the distribution of air pressure and streamline, and the influence of the ballast mass and shape, windward direction, and wind speed on the ballast flight were investigated. The results show that the critical velocity of the ballast weighing from 5 g to 200 g is between 14.22 m/s and 29.89 m/s. The massive ballast with the shape of an oblate is unlikely to fly. The air pressure difference is proportional to the square of the wind speed, and it increases 14.9 times when the wind speed increases from 10 m/s to 40 m/s. In order to avoid ballast flight, recommended suggestions include limiting the proportions of the small and the ellipsoid ballast praticles, laying the ballast with high density and a large size on the top of the ballast bed, and reducing the degradation and fouling of the ballast bed.

Effect of Long-Term Bridge Deformations on Safe Operation of High-Speed Railway and Vibration of Vehicle–Bridge Coupled System

Operation safety of high-speed trains is dependent on their vibration characteristics, which vary with bridge deformation. This paper studies the influence of bridge pier settlement and girder creep camber, which are two typical types of long-term bridge deformation, on the vibration of high-speed trains. To this end, an analytical approach is presented to link the bridge deformation with railway track deformation; the track deformation is used to analyze the vibration of the CRH2 high-speed train in China. The vibration analysis results are validated using the in-situ measurement data. The present study shows that bridge pier settlement greatly affects the vertical acceleration, derailment coefficient and wheel unloading rate of the high-speed train; incorporating bridge girder camber aggravates the vibration of the train–bridge system. The threshold of bridge pier settlement is suggested to be 11.1[Formula: see text]mm for trains moving at 350[Formula: see text]km/h with regard to the code-specified vibration limit. This study has significant implications for the design and operation of high-speed railways.

Recognition algorithm for the disengagement of cement asphalt mortar based on dynamic responses of vehicles

Disengagement of emulsified cement asphalt mortar will increase the dynamic action between the vehicle and the track; as a consequence, the rate of cement asphalt mortar disengagement will increase further. This is a serious threat for the safe operation of high-speed railways and the service life of rail equipment. In this study, a vertical coupled model for the vehicle–China Railway Track System II-type slab track with cement asphalt disengagement was established. The cement asphalt mortar was divided into units in order to simulate the arbitrary length of disengagement. Under different conditions, the effects of the cement asphalt mortar disengagement on the dynamic characteristics of the coupled model were analyzed. The results show that when the length of disengagement exceeds 0.65 m under the condition of horizontal complete disengagement, the dynamic responses of the system increase much sharply than the condition of horizontal partly disengagement. Because of the difficulty in identifying defects in the track substructure, a novel method was proposed to rapidly identify the cement asphalt mortar disengagement based on the dynamic responses of the coupled system and particle swarm optimization–support vector machines. The feature vectors were extracted from the acceleration of the wheelset, which were used as training samples in support vector machines. The classification results show that the recognition algorithm based on the acceleration of the wheelset and support vector machines is effective. The location of the track plate with the cement asphalt mortar disengagement at lengths of 0.65 m, 1.3 m, and 1.95 m can be identified with an acceptable accuracy. The robustness of the proposed algorithm under different vehicle speeds, track spectrums, and signal–noise ratios was verified. Recognition of defects in the track substructure using sensors mounted on in-service vehicles has the potential to provide a valuable tool for ensuring the safe operation of railways and for developing a maintenance plan.

Positioning Reference Signal-Orientated Moving Train Localization in Ricean Channel

In this paper, the challenging problem of train localization is investigated. Precise localization of rail vehicles is a key element for development of more efficient and safe operation of high-speed railway. The localization task is formulated in a 3GPP LTEbased framework that enables us to derive the Doppler frequency offset of moving train, by exploitation of positioning reference signal (PRS) placed in the subframe, in Ricean channel. Then, a PRS-oriented train position approach is proposed. The PRS time difference of arrival from positioning references eNB1 and eNB2, is calculated by sequences cross correlation, and the train running curve is determined by inquiring about the line database of track section. Finally, the train position is evaluated by combining the information of PRS time difference of arrival and the train’s moving trajectory. In the simulation, the localization performance is well demonstrated, when two types of railway alignments, i.e., straight line and circle curve, are considered.

Study on the Safety Management System of High-Speed Railway

Safety management system is a kind of aviation safety management idea[1]. With the mileage increase of the high-speed railway of China, there is an urgent need to establish a structured safety management system according to its own characteristics, which can ensure the safe operation of high-speed railway. First this paper considers the life cycle of high-speed railway, then learns aviation safety management system, and studies the operation characteristics of high-speed railway, finally this paper establishes the system based on the theory of PDCA、risk management, and the principle of the system. The safety management system of high-speed railway have four modules: the basic module, operation module, supervision module, improvement module.

Modeling and Verification for Track Circuit Encoding in Train Control Center Based on UML and TA

The correct implementation of Train Control Center (TCC) software has great significance on the safe operation of high-speed railways. There are some problems in the currently used timed automata (TA) method for modeling TCC software, such as subjectivity and uncertainty in the modeling. In order to better verify the features of TCC software, this paper presents a new modeling method which combines Unified Modeling Language (UML) and Timed Automata (TA). As track circuit encoding function is a key function in TCC software, this paper takes it as an example to show the effectiveness of the proposed modeling method. Firstly, the UML class diagrams and state diagrams of TCC software were established based on the detailed analysis on TCC software. Secondly, the TA models are built according to the corresponding rules between UML and TA model. Finally, TA models were simulated and validated in the validation tool, UPPAAL. The simulation results show that TCC software can meets its functional requirements and performance requirements.