Short pitch corrugation is a typical rail defect that lacks a thorough understanding and adequate root-cause solutions. This paper aims to identify the damage mechanism of short pitch corrugation through a microstructural analysis of a field rail sample. This sample made of R260Mn pearlitic steel was taken from a straight section of the Dutch railway network, and its geometry and surface hardness variation along the corrugation were measured and analyzed. Eleven specimens, including both corrugated and non-corrugated zones, were sectioned from the rail sample and continuously examined using light optical microscopy, scanning electron microscopy and micro-hardness testing. The results indicate that the corrugation damage mechanism can be categorized into three stages: (1) pre-corrugation, characterized by uniform wear and plastic deformation; (2) corrugation initiation, dominated by differential wear; and (3) corrugation growth, involving both differential wear and plastic deformation accumulation. The initiation and growth of corrugation both contribute to an inhomogeneous distribution of plastic deformation layer (PDL) in the rail subsurface, which follows an approximately sinusoidal pattern, matching the corrugation geometry in both wavelength and phase. Consequently, the hardness also varies in phase with the corrugation geometry, with higher hardness values at corrugation peaks. In the non-corrugation zone, the PDL and hardness show relatively small and irregular fluctuations. This study also provides meaningful insights into rail grinding, suggesting that grinding should account for differential PDL thickness to prevent corrugation reoccurrence due to subsurface material inhomogeneity.

As a critical factor in the degradation of rails and wheels, wheel-rail contact heat has been investigated with various analytical and numerical methods. However, the predicted temperature distributions and thermal loads have not been directly validated through measurements due to the challenges associated with accurate measurements. This study employs an infrared camera to measure the temperature variation at the wheel-rail contact under various slip ratio conditions in an in-house wheel-rail dynamic contact test rig. Wheel braking is replicated, and a wheel flat is generated. The temperature field of the contact interface is measured and analyzed, revealing the heating and cooling processes before and after the formation of the wheel flat. The results demonstrate that the contact temperature between the wheel and rail progressively increases with increasing slip ratio until a flat is formed. Notably, at a slip ratio of 8.3%, the observed contact temperature reaches 337.2 ℃ and then rises to 432.8 ℃ at a higher slip ratio of 15.9%. When the wheel flat is generated at a slip ratio of 20.4%, the observed contact temperature between the wheel and rail reaches 652.4 ℃. After the formation of the flat, the contact temperature initially decreases due to more wheel material of lower temperature entering into the contact and rises again with the increase of slip ratio. These measurement findings are valuable for calibrating and validating simulation models and investigating thermal damage related to wheel-rail interactions.

This paper presents a methodology for detecting and monitoring short pitch corrugation (SPC) under varying measurement conditions using vertical and longitudinal axle box acceleration (ABA) measurements. The main objective of the detection algorithm is to determine the likelihood and approximate severity of SPC presence, providing insights for maintenance planning. The methodology combines a validated three-dimensional finite element (3D-FE) model of the ABA responses at SPC and signal processing techniques to extract meaningful data from the real-world on-board measurements. First, a 3D-FE vehicle-track model is validated and used to quantify the physical relationships in the time–frequency responses of ABA at SPC under different levels of corrugation severity and measurement speeds. Then, a measurement train is instrumented with multiple accelerometers to capture field data on ABA at SPC, which is validated with field inspections and Railprof measurements. Finally, the ABA responses are analyzed based on the number of signals detecting SPC and an assessment of severity based on impact energy due to SPC. The methodology is demonstrated by analyzing the track between Assen and Groningen on the Dutch rail network. Results show that the methodology accurately detects registered SPC locations. Further, a whole track analysis is conducted, from which the methodology proposes new locations and severities of SPC, providing crucial information for rail maintenance planning.

Friction behaviour at the wheel–rail interface is of critical importance for railway operations and maintenance and is generally characterised by creep curves. The V-Track test rig was used in this study to measure both the lateral and longitudinal creep curves with uncontaminated dry interface conditions, utilising contact pressures representative of operational railway wheel–rail systems. The novelties of this study are threefold. 1. With proper representations of train/track components, the V-Track tests revealed the effects of structural dynamics on measuring wheel–rail creep curves in real life. 2. Pure lateral and longitudinal creepage conditions were produced with two distinct experimental principles—displacement- and force-controlled—on the V-Track, i.e., by carefully controlling the angle of attack and the traction/braking torque, respectively, and thus the coefficient of friction from lateral and longitudinal creep curves measured on the same platform could be cross-checked. 3. The uncertainties in the measured creep curves were analysed, which was rarely addressed in previous studies on creep curve measurements. In addition, the measured creep curves were compared against the theoretical creep curves obtained from Kalker’s CONTACT. The influence of wheel rolling speed and torque direction on the creep curve characteristics was then investigated. The measurement results and findings demonstrate the reliability of the V-Track to measure wheel–rail creep curves and study the wheel–rail frictional rolling contact.

This study experimentally and numerically investigated wheel–rail rolling contact fatigue (RCF), focusing on the initiation mechanisms of head check (HC). The experimental study was conducted using V-Track, a scaled test rig developed at TU Delft that is able to simulate real-life wheel–rail contact with controllable contact geometries and loading conditions. Ratcheting and HCs were generated on the V-Track rails with wheel–rail frictional rolling contact loading for up to 60,000 cycles. Rail samples with HCs were then examined with a microscopic analysis focusing on the R260MN steel grade. The boundary element method (BEM) and finite element method (FEM) were then applied to calculate wheel–rail contact-induced stress states in and below the rail surface under the same contact conditions as the experiment. The rail surface shear stresses calculated with BEM exhibited a strong correlation to the ratcheting observed within the rail running band in the microscopic analysis. Moreover, the plastic flows and cracks outside the running band identified by the microscopic analysis were correlated to the rail surface stresses, especially outside the contact patch, and subsurface stresses calculated with FEM: the results suggested that the accumulation of residual stresses could also contribute to plastic flow and the consequent initiation of cracks outside the running band.

Frictional heat is generated at the wheel-rail interface during train operations, particularly under high slip ratios during acceleration and braking. Thermal effects can accelerate wear, induce plastic deformation, and contribute to thermal fatigue. Reliable modelling of wheel-rail contact that considers friction-induced thermal effects is desirable for the accurate prediction of wheel-rail interface deterioration. Several analytical and numerical models have been proposed to simulate thermal or thermomechanical wheel-rail loads but have rarely been validated, especially in high slip ratio scenarios where flash temperatures exceed 200 °C. This study develops and experimentally validates a three-dimensional thermomechanical finite element (FE) wheel-rail contact model for high slip ratio conditions, with contact temperatures reaching 360 °C. The model incorporates key mechanical parameters, including wheel loads, coefficients of friction, and slip ratios. Simulated rail surface temperatures across various slip ratios (5 %, 10 %, and 15 %) are compared with the flash temperatures measured with an onboard infrared thermal camera, showing good agreement with a maximum deviation of 9.9 %. This confirms the reliability of the model for simulating wheel-rail contact under thermal effects.

Nowadays, rolling stock can be equipped with high-frequency vibration sensors to continuously monitor rail infrastructures and detect defects. These moving sensors measure at high speeds and sampling frequencies, generating a massive amount of data that covers each track position with very short signal durations. These data contain a variety of dynamic and transient responses that vary significantly along the track and are affected by noise. This leads to a large amount of unlabeled and noisy data, complicating the extraction of dynamic responses for effective anomaly detection. To address these challenges, this paper proposes an unsupervised representation learning methodology to automatically capture and extract characteristic features of dynamic responses that reflect the conditions of rail infrastructures. The unsupervised nature allows exploratory analysis of high-frequency vibration signals when prior knowledge or reference information about infrastructure conditions is unavailable or very limited. A collaborative optimization process that synchronizes empirical mode decomposition (EMD) with a convolutional autoencoder (CAE) is presented. The EMD level is tuned to remove noise while preserving effective vibration responses. The CAE is trained using demodulated signals that are considered normal to generate representations that ensure reconstruction quality and differentiate between normal and abnormal conditions. Furthermore, a Gaussian mixture model is used to showcase the effectiveness of the learned representations for rail infrastructures. Applied to validated axle box acceleration data for rail defect detection and train-borne laser Doppler vibrometer data for rail fastener monitoring, our method outperforms other variants of autoencoder-based models and the wavelet-based CAE in accurately identifying the conditions. It achieves an average improvement of 16% with the axle box acceleration data and 21% with the laser Doppler vibrometer data.

Short pitch corrugation is a major rail defect worldwide, and its development mechanism remains not fully understood. This work aims to better understand corrugation and validate some numerical predictions through extensive field monitoring. 105 corrugations on four sections of mainline tracks were continually monitored for five years. Comprehensive field data were collected, including photos, geometry, and vehicle-track dynamic responses. Numerical results of corrugation development agree with the field observations. It confirms that corrugation initiates with necessary initial excitation, consistently grows at fixed locations due to differential wear, and eventually reaches a limiting amplitude. Moreover, vehicle-track longitudinal vibrations are crucial to corrugation initiation, while vertical vibration plays an increasingly important role in corrugation growth.

A train-borne laser Doppler vibrometer (LDV) directly measures the dynamic response of railway track components from a moving train, which has the potential to complement existing train-borne technologies for railway track monitoring. This paper proposes a holistic methodology to characterize train-borne LDV measurements by combining computer-aided approaches and real-life measurements. The focus is on the speed-dependent characteristics because the train speed affects the intensity of railway sleeper vibrations and the intensity of speckle noise, which further affects the quality and usability of the measured signals. First, numerical models are established and validated to simulate sleeper vibrations and speckle noise separately. Then, a vibration–noise separation method is proposed to effectively extract speckle noise and structural vibrations from LDV signals measured at different speeds. The parameters of the separation method are tuned using simulation signals. The method is then validated using laboratory measurements in a vehicle-track test rig and applied to field measurements on a railway track in Rotterdam, the Netherlands. Further, the speed-dependent characteristics of train-borne LDV measurement are determined by analyzing the competition between sleeper vibrations and speckle noise at different speeds. Simulation and measurement results show that an optimal speed range yields the highest signal-to-noise ratio, which varies for different track structures, measurement configurations, and operational conditions. The findings demonstrate the potential of train-borne LDV for large-scale rail infrastructure monitoring.

This article develops and tests a self-contained railway track monitoring system that fits in existing vehicles without the need for speed and load control. Combining a train-borne laser Doppler vibrometer and axle box accelerometers enables synchronized measurements of train-track response under operational conditions. Utilizing a GPS antenna and video camera, we propose the multisignal processing method to obtain train-track vibrations with train position and speed. Then, we fuse the multiple signals to extract an impact index and a resonance index and further propose an interpretable anomaly detection strategy. We test the system on an operational line at 20-60 km/h under different working conditions and verify the detection results using information from conventional technologies. The impact index peaks near joints and welds, and the resonance index yields a good correlation with the measured track geometry. The developed solution achieves the detection, localization, and quantification of surface and support anomalies in railway tracks.

The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research shows that dynamic responses from moving trains via axle box acceleration (ABA) measurements can quantify abrupt changes in the vertical dynamic responses. Thus, this paper proposes a framework to design an enhanced vertical TQI, called EnVTQI, by integrating track longitudinal levels and dynamic responses from ABA measurements. First, measured ABA signals are processed to mitigate the influence of variation in measurement speed. Then, substructure and ballast-related features are extracted, including scale average wavelet power (SAWP) in the ranges 0.04 m^-1 to 0.33 m^-1 (substructure) and 1.25 m^-1 to 2.50 m^-1 (ballast). This enables identifying track conditions at different track layers. Finally, EnVTQI is determined by weight averaging between the conventional vertical TQI and the ABA features from moving trains. The performance of EnVTQI is evaluated based on 48 segments of a 200-m track on a Dutch railway line. The results indicate that EnVTQI helps to distinguish track segments that cause poor train-track interaction, which the conventional TQI does not indicate. EnVTQI can supplement the conventional TQI, improving the effectiveness of track maintenance decision-making.

A transfer function (TF) is an effective representation of the load-response relationship of railway track structures. To fill the gap in measuring track structure TFs over a wide frequency range from a moving vehicle, we develop a TF measurement system and the associated TF estimation methodology. Accelerometers are utilized to estimate the dynamic vehicle load to track structures, and a laser Doppler vibrometer (LDV) is used to scan track structures and measure their vibration response. First, operational modal analysis is applied to vehicle impact response over joints to identify its modal parameters, which support the estimation of dynamic wheel-rail forces from vehicle vibrations. This combination eliminates the need to pre-define the vehicle stiffness, vehicle damping, and vehicle body mass and enables the vehicle parameters to be updated under operational conditions. Meanwhile, a signal processing method is applied to LDV signals to reduce speckle noise and compensate for the effect of vehicle vibration. Then, a continuous track structure is segmented into distributed sections, and a TF is estimated for each track section using the estimated wheel-rail force as input and the extracted track vibration as output. We validate the methodology in a vehicle-track test rig on different track sections (with or without joints) and at different speeds (from 8 km/h to 16 km/h). The results are further compared with trackside measurements and hammer tests. We demonstrate that the track vibrations extracted from the LDV signals are consistent with those measured by trackside accelerometers. The shapes and resonance frequencies of the estimated TFs are in good agreement with those measured from the hammer tests in the frequency range of 200–800 Hz. The developed system captures differences in the TFs between different track sections, suggesting its potential to be used for structural health monitoring of railway tracks.

Railway track stiffness is an important property closely related to track condition and maintenance. Track stiffness variations occur over time and space due to dynamic train loading and aging of track components. Track stiffness variations may further lead to geometry deteriorations and vibration problems. It is therefore essential to continuously monitor track stiffness variations, as well as related track component degradations, over time and space, so that preventive and targeted maintenance can be performed to reduce the life-cycle cost of rail infrastructures.

Various techniques exist for evaluating track stiffness from dynamic responses of the train– track system, including track-side and train-borne measurements. The track-side measurement uses sensors mounted on sleepers to measure the track stiffness at each sleeper support. Nonetheless, because of the cost of sensor deployment, the track-side measurement is more suited for discrete locations in the rail network of special interest, such as transition zones. To scan for track stiffness over a long distance, specialised measurement vehicles were developed. However, they can only measure at low speeds and require track occupation, thus not suitable for frequent measurements. In comparison, train-borne measurements using in-service vehicles are more cost-effective and allow for more frequent surveys of the entire rail network. However, existing techniques are not able to measure the respective stiffness of different track layers, such as the ballast and railpad stiffness. In addition, existing train-borne techniques using in-service vehicles have seldom been validated by field measurement data.

Polygonal wear is a type of damage commonly observed on the railway wheel tread. It induces wheel-rail impacts and consequent train/track components failure. This study presents a finite element (FE) thermomechanical wheel-rail contact model, which is able to cope with the three possible generation and development mechanisms of polygonal wear: initial defects, thermal effect, and structural dynamics. The polygonal wear-induced impact contact and further development of wear are simulated. The simulated elastic contact solutions are verified against the program CONTACT. Different material properties (elastic, elasto-plastic and elasto-plastic-thermo, i.e. with thermal softening) and initial polygonal profiles are then applied to the FE model to investigate the influence of wheel/rail material and wear amplitude on wheel-rail contact stress and wear development. The simulations indicate that the wheel-rail impact-induced temperature may reach up to 362 ℃ at the contact interface, and the high temperature at the contact area influences wheel-rail contact stress and wear depth.

The Coefficient of Friction (CoF) is an important parameter affecting acceleration and braking behavior of trains, and consequently the inter-train distance and utilization of track. To optimize operation schedules, maximize railway capacity, and realize automatic train operations, reliable measurements of the CoFs experienced by in-service trains are desirable. In this study, a train-borne measurement approach is proposed based on a torque modulation concept. It involves superimposing a small-amplitude sinusoidal signal on the motor torque. Because the wheel-rail friction force acts as variable damping, it causes a phase difference between the angular velocity response of the wheelset and the input modulated torque signal. This phase difference can be used to determine the creep coefficient, i.e., the slope of the creep curve, and then to estimate the CoF in combination with the measured Coefficient of Adhesion (CoA), i.e., the ratio between the wheel-rail friction force and normal load. Simulations of torque modulation with VI-Rail are conducted. Variation of phase difference with the increase of the modulated torque is derived theoretically and compared with numerically obtained results using the VI-Rail multibody dynamics model under different CoF conditions. The good agreement between the results indicates the effectiveness of the proposed measurement concept.

This study presents a measuring framework for railway transition zones using a case study on the Swedish line between Boden and Murjek. The final goal is to better understand the vertical dynamics of transition zones using hammer tests, falling weight measurements, and axle box acceleration (ABA) measurements. Frequency response functions (FRFs) from hammer tests indicate two track resonances, for which the FRF magnitudes on the plain track are at least 30% lower than those at the abutment. The falling weight measurements indicate that the track on the bridge has a much higher deflection than the track on the embankment. Two features from ABA signals, the dominant spatial frequency and the scale average wavelet power, show variation along the transition zone. These variations indicate differences in track conditions per location. Finally, the ABA features in the range of 1.05–2.86 m⁻¹ are found to be related to the track resonance in the range of 30–60 Hz. The findings in this paper provide additional support for physically interpreting train-borne measurements for monitoring transition zones.

Short pitch corrugation is a typical defect on rail surfaces that induces high level of noise and increases maintenance costs. Despite numerous research efforts, corrugation development mechanism has not yet been fully understood and root-cause solutions have not been developed. This work numerally simulates the rail corrugation in the V-Track test rig, aiming to better understand corrugation mechanism and also link the scaled laboratory tests to the full-scale reality. A three-dimensional finite element model of the V-Track is established to simulate the vehicle-track dynamic interaction. The fastening and ballast parameters are calibrated by fitting the simulated track receptances to hammer tests. Rail corrugation with a major wavelength of 5.7 mm is successfully reproduced using the FE model, which shares features similar to the experimentally produced corrugation in the V-Track. The numerical simulation demonstrates that a rail longitudinal compression mode at 790 Hz is the ‘wavelength-fixing’ mechanism of corrugation in the V-Track, agreeing with the experimental results. This work numerically verifies the dominance of the rail longitudinal vibration modes on corrugation formation.

Various models for simulating rail ratcheting behaviour were developed to study rolling contact fatigue (RCF) damage in rails. However, limitations remain in terms of the accuracy of wheel–rail contact modelling and computational efficiency of the cyclic loading simulation. This study developed an efficient 3D finite element (FE) procedure to simulate ratcheting in rails subjected to numerous load cycles. The procedure simulates a wheel rolling repeatedly over a rail section with updated stress–strain states, enabling automatically executed cyclic loading simulation given a predefined number of cycles. To ensure the accuracy of the contact modelling, the effect of meshing schemes on subsurface stress distribution was examined. In addition, the FE contact model with the selected meshing scheme, which balances accuracy and computational efficiency, was verified against the widely accepted CONTACT program. Subsequently, a non-linear kinematic hardening (NLKH) steel material was used in the FE model for ratcheting simulations with up to 100 wheel-loading cycles. The rail surface and subsurface stress states were replicated under partial-slip wheel–rail rolling contact conditions with traction coefficients of 0.10, 0.20 and 0.35, respectively. The ratcheting behaviour was extensively analysed in terms of plastic deformation, contact patch evolution, and ratcheting rates. The simulated plastic deformation was found to alter the contact geometry and thus contact stresses, which in turn affect further accumulation of plastic deformation and subsequent ratcheting strains. These findings highlighted the importance of considering the interplay between the rail ratcheting behaviour of the rail and evolving contact conditions for predicting ratcheting and RCF damage in rails.

Wheel-rail high-frequency interaction is closely related to the formation of railway short-wave defects. Finite element (FE) method has been widely used to simulate wheel-rail dynamic systems, but its validity in modelling high-frequency interaction has not been fully demonstrated in three dimensions (3D). This work aims at comprehensively validating the 3D FE modelling of wheel-rail high-frequency interaction using a downscale V-Track test rig. First, the FE model of the V-Track is developed that comprehensively includes the 3D track elasticity. The simulated track dynamic behaviours are validated against hammer tests, and the major vibration modes are analyzed employing modal analysis. Afterwards, the simulate wheel-rail dynamic responses are comprehensively compared with measurement results up to 10 kHz. Their characteristic frequencies are identified and correlated to the eigenmodes of the vehicle-track system. The results indicate that the proposed 3D FE model is capable of comprehensively and accurately simulating the 3D track dynamics and wheel-rail dynamic interaction of the V-Track up to 10 kHz. Rail vibrations dominate the wheel-rail dynamic contact within 10 kHz, while the wheel vibrations play an increasingly important role at higher frequencies and become decisive near the wheel eigenmode frequencies. The V-Track overall achieves dynamic similarity to the real vehicle-track system.

Vibrations resulting from dynamic vehicle-track interactions (VTI) offer valuable insights into track conditions. This paper presents an approach for track condition monitoring by detecting and quantifying multiple track degradations using a digital twin of the VTI system. Unlike existing techniques that focus on a specific degradation type at a single track component, our proposed method provides a generic and integrated framework. By combining a physics-based VTI model with a data-driven model, we dynamically update the digital twin’s state based on measured axle-box accelerations (ABA). We introduce a local ABA feature extracted from its spectrogram and demonstrate its effectiveness in distinguishing various degradations at different track positions. The implementation and capability of the proposed approach were demonstrated in a case study conducted on a transition zone of a railway bridge. The simultaneous track stiffness variations in the railpad/fastening and ballast layers were successfully detected, confirming the effectiveness of our approach. The case study also showcases the generality, interpretability, efficiency, and robustness of the proposed approach in identifying concurrent degradation. Our proposed framework opens new possibilities for cost-effective continuous track monitoring for railway infrastructure management.