Rail Profile Research Articles

Parameter influence analysis and optimization of wheel–rail creepage characteristics in high-speed railway curves

PurposeTo investigate the influence of vehicle operation speed, curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approachTaking a small-radius curve of a high-speed railway as the research object, field measurements were conducted to obtain track parameters and wheel–rail profiles. A coupled vehicle-track dynamics model was established. Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.FindingsKey service parameters affecting wheel–rail creepage were identified, including the matching relationship between curve geometry and vehicle speed and rail profile parameters. The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis, leading to the establishment of parameter optimization criteria.Originality/valueThis study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves. A response surface-based parameter-creepage relationship model was established, and a multi-parameter coordinated optimization strategy was proposed. The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.

Innovative on-off yaw dampers with switchable dynamic stiffness for enhanced stability of high-speed railway vehicles

The parameters of traditional yaw dampers are typically determined during the vehicle dynamics design stage based on specific wheel and rail profiles. However, dampers with fixed parameters often struggle to effectively mitigate hunting motions due to the significant variations in wheel-rail contact conditions encountered during operation. Unlike the traditional method of adjusting dynamic damping, the goal of this work is to propose an innovative on-off yaw damper that can realize switchable low and high dynamic stiffness, and thus has the ability to suppress both low-frequency carbody hunting and high-frequency bogie hunting under different wheel-rail conicity conditions. First, the working principle of the damper is detailed, highlighting switchable dynamic stiffness in two selectable modes. A physical model is then developed to study the damper’s dynamic characteristics under different combinations of excitation frequency and amplitude. The results reveal a substantial difference in dynamic stiffness between the two modes, with a 74% variance, while dynamic damping remains largely unaffected. A new parameter, termed dynamic stiffness difference, is introduced to describe the frequency-dependent stiffness property of the damper, and its sensitivity to damper parameters is explored, providing insights for design optimization. Finally, numerical simulations demonstrate that the on-off yaw damper significantly improves both low and high conicity stability, thereby enhancing overall vehicle stability across a wide range of wheel-rail contact conditions.

Research on wear and grinding models of high-speed turnouts based on semi-Hertz contact

In order to reduce turnout rail wear, the paper establishes a coupled dynamics model and a turnout rail wear model that consider the true profile of the turnout rail, the vehicle’s continuous traction force while passing, and the operational resistance. Comparative analysis of various models for predicting turnout rail wear indicates that the wear energy model is better suited for this purpose. The ideal profile update step for the turnout rail is 0.05 mm, and the adaptive filtering algorithm, tailored to the turnout characteristics, smooths wear distribution effectively while retaining crucial data features. The wear model developed in the paper predicts wear depth that is 89.91% consistent with the measured data. The grinding model introduced in the paper significantly enhances the wheel-rail contact conditions in the turnout, with lateral and vertical vibration accelerations of the vehicle reduced by 52.56% and 30.43%, respectively, during turnout passage. The research offers theoretical support for mitigating rail wear in high-speed turnouts.

Wheel profile optimization of high-speed locomotive to improve railway line adaptability

Aiming at the low-frequency carbody swaying phenomenon of the locomotive belonging to the power concentrated electric multiple unit (EMU) for high-speed passenger transport, which operated in the early stage after the wheel profile is repaired as the JM3-32 thin flange profile according to the standard, the profile is optimized to improve the locomotive’s dynamic performance. The optimization objective is to improve the equivalent conicity and its distribution concentration of the wheel profile under various rail profiles and rail cants conditions, as well as to improve its wear performance. The mapping relationship between the design variables and the objective functions is realized by constructing the radial basis function neural network (RBFNN) surrogate model. The Nondominated Sorting Genetic Algorithm II (NSGA-II) is used to optimize the position and size of the four arcs in the rolling circle contact area of the wheel profile. The wheel-rail contact characteristics and vehicle dynamics simulation of the wheel profile before and after optimization are compared to reveal the optimization effects. The results show that under different wheel-rail matching conditions, the equivalent conicity of the optimized wheel profile is significantly improved compared with the original wheel profile, and the dispersion degree of its distribution is significantly reduced at the same time. The nominal equivalent conicities corresponding to the 3 mm lateral displacement of the wheelset are all about 0.1. The nonlinear critical velocity is increased by more than 290 km/h under the condition of 1/20 rail cant. Under the four wheel-rail matching conditions, the Locomotive will not have the problem of lateral ride comfort deterioration and vehicle swaying when running at high speed. The curve passing performance and wear performance are also improved.

Fundamental basic S-N curve to study fatigue life of R260 rail under uni-axial fatigue testing

In this research paper to investigate the fatigue life of R260 rail under uni-axial fatigue testing. It has conduct the experiment to determine the fatigue life with using bulk specimen in accordance standard of ASTM E466-15 with seven different loading level.Three specimens selected to evaluate the value of ultimate tensile strength (UTS) in ensuring similar number using UTM below 250 kN. The type of Servo-Pulser fatigue machine was performed of less than 100 kN with using sinusoidal signal frequency of 10Hz and it have based on 20%, 30%, 35%, 40%, 45%, 55%, and 65% as below value on ultimate tensile strength (UTS) of 1145 MPa. The results on total fatigue life cycles for R260 obtained on experiment shows the significant highest values stress influences in shortest the fatigue life on specimen and a lowest level of stress values in producing typically more cycles. While, findings shows that two regimes categorized has been defined as low cycles fatigue and high cycles fatigue for R260 rail profile and endurance limit to withstand never failure and break off. The value of stress ratio R=-1 was set up in experiment as principal to establish S-N curve. This approach is high valuable and contribute in terms of nature material behaviour on fatigue life.

Development of hunting oscillation detection algorithm for railway vehicles by using accelerometers

In this study, an algorithm was developed to detect hunting oscillation in railway vehicles using only accelerometers. Typically, the wheel profile of most railway vehicles has a conical shape, causing inherent hunting oscillation. The critical speed at which hunting oscillation occurs is determined by the wheel profile, rail profile, and dynamic characteristics of the railway vehicles. Hunting oscillation significantly impairs ride comfort, reduces running stability, and increases the risk of accidents such as derailment. Therefore, if hunting oscillation occurs during railway operation, it must be detected, and a deceleration procedure should be applied immediately to ensure dynamic stability. The detection algorithm in this study is based on the geometric behavior of the bogie. It utilizes longitudinal and lateral acceleration data from accelerometers installed on the upper frame of the bogie above the axle boxes. The algorithm processes the acceleration data to obtain the hunting oscillation frequency and harmonics, integrating all this data to calculate the Index of Hunting Oscillation. Validation was performed through simulations using a standard electric train model, confirming the algorithm’s effectiveness. Additionally, tests on actual bogies using a roller rig validated the algorithm’s performance in detecting hunting oscillation effectively.

A high-precision visual detection method for rail profile based on Scheimpflug condition

Rail profile is an important indicator of rail service status, which should be always kept in good condition. How to efficiently collect rail profiles with high precision is a critical issue for their condition-based maintenance. This paper focuses on the method for accurate visual detection of rail full profile (RFP), including optical modeling, visual calibration, and error compensation. The proposed optical unit, featuring double line structured-light sensors, introduces an innovative method under the Scheimpflug condition to avoid the limitation of depth of field in RFP measurement. Subsequently, double visual sensors are calibrated together using the common reference target to extract the internal and external parameters respectively. The dynamic errors resulting from the impact of vehicle vibrations are ultimately investigated to rectify the distorted profiles to the normal. Both laboratory and field tests are performed to verify the effectiveness of the proposed system in terms of precision and efficiency.

Laser Scan Compression for Rail Inspection

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors’ limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans.

Effects of rail hardness on transverse profile evolution and computed contact conditions in a full-scale wheel-rail test rig evaluation

This paper revisits a comprehensive data set generated in a prior test program using a full-scale wheel-rail test rig. The test program evaluated premium and standard grade rail steels with respect to wear and rolling contact fatigue (RCF). The current work evaluates the evolution of rail profiles throughout the test cases, taking advantage of the database of wheel and rail profiles that were collected. Contact conditions are modelled using the CONTACT library, including recent developments in the handling of conformal geometries and interfacial layers. Observations are made regarding the relative characteristics of rail profiles for each steel type, as they evolve with accumulated wheel passes, on the basis of the computed contact conditions.

A case study of railway curve squeal radiated from both the outer and inner wheel

This case study is presented after observations were made that partially contrast the prevailing picture of railway curve squeal given in the literature. These are observations with significance both for modelling and condition monitoring. The current case study will be followed by a subsequent investigation focused on the validation and application of a simulation model to investigate root-causes for curve squeal at the Stockholm metro. Curve squeal in a 213 m radius curve on the Stockholm metro is studied. Data includes track characteristics (rail profile, rail roughness and gauge width) and noise measured at the trackside as well as by two vehicles equipped with an on-board mounted noise monitoring system. The current case study contrasts field measurements of curve squeal reported in the literature by a relative shift of emitted noise towards higher frequencies. Wayside noise measurements in the studied curve during a few hours showed squeal generation for all vehicle passages with dominating 1/3 octave band centre frequencies in the range between 6.3–15.8 kHz. Noise data measured during one year of regular traffic of two vehicles equipped with a monitoring system were obtained. The occurrence of curve squeal was analysed through an implementation of the curve squeal detection algorithm in operation at the Stockholm metro. This algorithm was also applied to search for events of squeal noise radiation from the outer wheel. Results show emissions of squeal noise from the inner and outer wheel for 65 % and 8 % of the vehicle passages through the studied curve, respectively. Further, the occurrence of curve squeal radiated from the inner wheel was found to increase by 10 % after rail grinding. In the literature, squeal radiated from the outer wheel is described as having an intermittent character with magnified spectral components in the frequency range between 5–10 kHz. In contrast, the current work presents sustained tonal squeal generated from the outer wheel with similar noise characteristics as typically related to ordinary curve squeal.

Wheel-rail wear characteristics of a modern tram passing curves with small radius

The wheel-rail wear was predicted for modern trams with independently rotating wheelset running on a groove-shaped rail. The tram vehicle-track coupled dynamics model, considering the wheel-rail multi-point contact, was developed using software UM to evaluate the dynamic responses of the vehicle and track. Hertz contact theory, the FastSim algorithm, and Archard material wear model were used to solve the normal contact stress, tangential contact stress, and wheel-ail wear, respectively. The wheel and rail profiles were updated when the wear depth reached to 0.1 mm. In calculation, the wheel and rail wear was calculated and analyzed separately. The results of groove rail and independently rotating wheelset wear predicted by this model coincided with relevant literature, which proved the effectiveness of this model. The wheel and rail wear characteristics of modern trams passing through curved tracks with small radii and the influence of wheel-rail friction coefficient on wheel and rail wear were calculated and analyzed using this model. The results show that the wheel and rail wear on the circular curve and the rear transition curve is more intense. The wheel wear is the most intense on circular curve while the rail wear is on the rear transition curve. The wear of inner and outer rails and wheels of wheelset one increases with the increase of wheel-rail friction coefficient while the wear of inner and outer wheels of wheelset three have no obvious change. The wear of the inner and outer rails is the most intense at the gauge corner position while is the lightest at the guard rail. The wear of wheelset one is more intense than that of wheelset three and the inner wheel wear is more intense than that of the outer wheel. The present analysis contributes to improve the life of tram wheel and rail and reduce the frequency of wheel-rail maintenance during operation.

Prediction of rail wear band evolution under excitation by periodic track irregularities and its influence on vehicle dynamic performance

Periodic track irregularities (PTIs) are an important and sensitive source of vehicle vibration and have attracted considerable attention from railway researchers. However, previous studies have mainly focused on the irregularities themselves, with limited investigation of how the rail wear band (RWB) generated by PTIs excitation affects vehicle responses. To assess this, a three-dimensional rail wear prediction model is developed in this study to simulate the evolution of the RWB under PTIs conditions and then the effect of RWB excitation on the vehicle dynamic performance is investigated. The results show that the spatial trace of the rail contact points generated by the PTIs also exhibits clear periodic characteristics. Accordingly, the distribution pattern of the RWB also reflects these periodic features. The excitation of the periodic rail wear band (PRWB) has a negative effect on the vehicle running stability, especially when the vehicle is traveling at speeds that induce coupling resonance, which exacerbates the consequences. As the wear level of the PRWB increases, there is also an escalation in the vibration of the carbody. Furthermore, the amplitude of the change in the wheelset balance position resulting from variations in rail profile is found to serve as an effective descriptor for characterizing the wear intensity of PRWB, and the vibration amplitude of the carbody exhibits a clear linear correlation with PRWB excitation. The research results are helpful for understanding the relationship between PRWB excitation and vehicle vibration behavior and serve as a theoretical basis for track maintenance and profile optimization.

Fault Diagnosis for Rail Profile Data Using Refined Dispersion Entropy and Dependence Measurements

Fault Diagnosis for Rail Profile Data Using Refined Dispersion Entropy and Dependence Measurements

Utilizing on-board sensing of passing train vehicles for virtual sensing of bridges

This study proposes a bridge response reconstruction approach using exclusively measurements obtained from instrumented train vehicles with known properties. The approach relies on an Augmented Kalman Filter (AKF) technique to estimate contact forces without prior knowledge of rail profile irregularities. Furthermore, the proposed methodology addresses the challenge of accurate vehicle-bridge interaction (VBI) contact force estimation in the presence of rigid-body modes in the train model, by separating rigid-body from flexible modes, and establishing a state-space formulation that incorporates the vehicle dynamics of solely flexible modes. The study examines, by means of numerical simulations, the impact of measurement and model errors, vehicle speed, and rail irregularities on both contact force estimation and bridge response reconstruction. It also investigates sensor configurations that minimize the number of sensors required on the train and shows that acceleration at all wheels and strain of at least one primary suspension per bogie are necessary for accurate force estimation. The results emphasize the significant role of VBI in achieving accurate bridge response reconstruction. The estimated contact forces in combination with a bridge model allow the reconstruction of the bridge response through virtual sensing, providing valuable information on the bridge’s health status.

Application of Online Automated Segmentation and Evaluation Method in Anomaly Detection at Rail Profile Based on Pattern Matching and Complex Networks

Application of Online Automated Segmentation and Evaluation Method in Anomaly Detection at Rail Profile Based on Pattern Matching and Complex Networks

Investigation of the effect of residual waviness formed by rail profile milling of reshaped surface on wheel-rail contact stresses and low cyclic fatigue

The rail profile milling process allows considerable thickness to be removed in a single pass. However, the residual waviness formation law after milling and its subsequent effect on rail performance remains uncertain. In this paper, the wavelength and wave height of the residual waviness are identified as the key characteristics. The relationship between milling speed and residual waviness is determined, and a numerical model of the residual waviness formed on the surface after milling is established based on the commonly used three milling speeds of 300, 600, and 1000 m/h, and its accuracy is verified using the parameters of the residual waviness detected by the milling experiments. A three-dimensional finite element analysis model of wheel-rail contact was employed to analyze the contact stresses and low fatigue cycles at a wheel load of 11.5 tons with no residual waviness on the rail profile surface and with three types of residual waviness, respectively. The results show that the residual waviness changes the wheel-rail contact position and the morphology of the contact area, reduces the maximum contact stress and increases the strain fatigue cycles. The application of elevated milling speeds reduces the wheel-rail contact stresses after milling, thereby increasing the low fatigue cycles.

Predicting the wear on the non-working side of the rail profile registration method and its validation

Abstract Rail cross-section profile detection can assess the wear and tear of measured rails, providing crucial references for railway maintenance and upkeep. It is challenging to use the conventional method to register only the profile of rail head detection accurately. After the rail edge adjustment in some conventional railways, it becomes difficult to determine the base point of rail profile registration. Due to the wear of non-working edge of rail, a rail profile registration method is proposed. Firstly, a wear prediction model based on the generalized regression neural network is constructed by optimizing smoothing parameters through ten-fold cross-validation to achieve the optimal values. This model predicts the wear values on the non-working rail edge, providing reliable coordinates for two wear points as alignment reference points. Secondly, an initial alignment between the measured profile and the target profile is achieved using the nearest point iterative algorithm, which ensures that both profiles are in the same region and oriented similarly. Thirdly, the weight is assigned to the wear measurement points based on their respective wear values. The predicted positions of wear points are applied for calculating the translation and rotation parameters. These parameters could align the measured profile and facilitate the final profile alignment. Lastly, the experimental profiles under rail adjustment conditions were registered, verifying the accuracy of the proposed method. The research results indicate that under rail adjustment conditions, the mean squared error (MSE) between the calculated and actual values of lateral wear is 0.094 mm2, which is lower than the MSE from manual measurements. The calculated lateral wear value for experimental profiles achieved high alignment and calculation accuracy. This method can be applied in practical projects, providing an effective solution for rail head profile alignment and serving as a reference for profile alignment on the non-working rail edge with incomplete measurement data.

Design of an Innovative Twin-Disc Device for the Evaluation of Wheel and Rail Profile Wear

Design of an Innovative Twin-Disc Device for the Evaluation of Wheel and Rail Profile Wear

Динамічні якості пасажирського поїзда зчленованої конструкції

The importance of this work stems from the need for renewing the home rolling stock and integrating it into the European market of transport services. The introduction of speedy train operation calls for increased attention to theoretical studies aimed at ride performance improvement. The analysis of passenger traffic safety for home and European tracks of different and geometries calls for assessing the ride performance of passenger trains. In doing so, steady and transient traffic conditions must be considered. This approach to theoretical studies allows one to improve the safety indices of speedy passenger trains. This paper presents the results of theoretical evaluation of the ride performance of an articulated and a standard passenger train. The cars of both trains are equipped with wheels of different profiles used on the Ukrainian and European railways. An assessment of the predicted dynamic performance of the articulated passenger train shows that its cars demonstrate a better ride performance, thus improving passenger comfort and train operating safety throughout the allowable speed range in normal conditions. It is undesirable for such trains to move in curves of small and medium radius. In addition, the articulated train cost is lower than the traditional train one because of a lower number of trucks. The ride performance is also assessed for cars whose wheels are compatible with different rail profiles, which allows them to be used both on the Ukrainian and on the European railways. As shown by calculations, the universal wear-resistant wheel profile does not impair the car ride performance of the trains considered. better ride performance.

Vibration-Based Railway Suspension Monitoring Under Varying Operating Conditions via Robust Statistical Time Series Type Methods

The on-board condition monitoring of railway suspension components is important for ensuring passenger comfort, maintaining proper operation, enhancing safety, and reducing operational costs. In this context statistical time series type random-vibration-based methods constitute a potentially efficient and cost-effective framework. Yet a significant challenge emerges from the competing requirements for high sensitivity to suspension component degradation and robustness to varying Operating Conditions (OCs) and uncertainty. Varying Operating Conditions and uncertainty are related to factors such as varying payload, travelling speed, and rail profile. The problem is further aggravated by the complexity of the vehicle dynamics, including the wheelbase filtering effect. Despite the significant progress made so far and the development of various methods, the challenges related to varying OCs and uncertainty largely remain as a technical barrier. The aim of the present study is the introduction of a data-based methodology aspiring to overcome them. The methodology is based on Multiple-Input Single-Output Transmittance Function (MISO-TF) time series models of a special form allowing for eliminating the effects of varying rail profile while properly treating the wheelbase filtering effect. Monitoring is based on a MISO-TF feature space, using either a Random Coefficient Gaussian Mixture or a Multiple Model based scheme. The former, although more elaborate, allows for the introduction of a complete probabilistic framework. An interesting advantage of both versions is that operation can be based on a very limited number of sensors and only a partial data-driven model of the dynamics. The methodology is comprehensively assessed via hundreds of SIMPACK based Monte Carlo runs under variable payload and rail profiles. The achievable performance is shown to be very good for both versions and for 10%, 15%, and 20% of condition degradation in either primary or secondary suspension components.