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.

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.

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.

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.

Ensuring the safety and longevity of railway bridges requires efficient, non-invasive methods for monitoring their health and detecting structural damage. Drive-by health monitoring (DBHM) has emerged as a promising approach, using vehicle-mounted sensors, such as axle box acceleration (ABA), to assess the structural integrity of bridges. This method offers the advantage of frequent monitoring under operational conditions. However, DBHM faces challenges in real-world applications due to the subtle influence of local damage and disturbances like vehicle dynamics, track irregularities, and noise. This study investigates the feasibility of using ABA to detect structural damage in a real railway bridge. Continuous wavelet transforms and filtering techniques are used to isolate different vibration components within ABA signals. A finite element model of a cracked beam is developed, and simulations reveal that local structural damage introduces a small, local peak in the quasi-static ABA component. Field measurements show the variability of ABA measurements over space and time and the resulting difficulty in directly detecting the local damage. However, probabilistic analysis suggests that reference signals under healthy conditions, combined with frequent monitoring, can enhance the reliability of damage detection using DBHM.

Axle box acceleration (ABA) measurements can be used for continuously monitoring rail infrastructure and detecting rail surface defects such as squats. However, accurately detecting squats is challenging due to their short-duration responses and low occurrence in ABA signals, particularly for light squats that exhibit subtle ABA responses. To address this challenge, we propose using a spiking neural network (SNN) with time-varying weights to enhance the detection performance of rail squats based on ABA measurements. Our approach employs a simple SNN architecture without hidden layers, trained using a method that combines genetic algorithms, k-fold cross-validation, and multi-start gradient-based approach to optimise hyperparameters and weights. The proposed methodology demonstrates competitive accuracy compared to other state-of-the-art SNN-based methods on UCI benchmarks for both binary and multi-class nonlinear problems. Part of its advantages include higher efficiency with a simpler architecture and training approach that reduces computational times while achieving effective spatiotemporal pattern detection. As shown by real-field measurements from Dutch and Swedish railways in anomaly detection, it effectively captures subtle changes in light squat defect responses in ABA signals and achieves a detection performance of 100% for severe squat defects and over 93% for light squat defects. Furthermore, we show that the spike responses, postsynaptic potentials, and membrane potentials can be used as a new way to explain and analyse the ABA signals. The proposed method using time-varying weights highlights a correspondence with the physical problem and offers an ability to capture sudden and subtle changes in the responses, which is crucial, particularly for detecting defects in their early stages.

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.

This paper investigates the growth and treatment of a major type of rail rolling contact fatigue (RCF) known as head checks (HCs). The analysis is based on extensive field data of 212 curved tracks made of R260 steel across the entire Belgian railway network. The HC crack depth was mainly measured by eddy current testing. The growth rates of HCs are analysed in relation to the curve radius, annual traffic load, and rail wear. The key findings are as follows: 1) Tracks with radii between 750 and 1000 m exhibit the highest HC growth rate of about 1.5 mm per 100 million gross tons (MGT) and the largest occurrence probability of about 25 %. 2) A counterintuitive result is that the HC growth per MGT is higher on lines with lower annual traffic loads, consistent with the trend observed in rail wear rates. 3) The artificial wear methods to control RCF, such as preventive grinding, should consider annual traffic load and service time, rather than solely accumulated tonnage, as is the current practice. Based on these findings, a new method is proposed to estimate the magic wear rate for the Belgian railways, which can serve as input for optimising grinding operations to mitigate HCs.

Transition zones in railway tracks often degrade faster than other locations, yet traditional health assessments rely on infrequent track geometry measurements, limiting early detection of dynamic changes. This research presents an approach for more frequent evaluation of transition zone health by integrating data sources from multiple monitoring technologies: track geometry, interferometric synthetic aperture radar (InSAR), and axle box acceleration (ABA). Missing InSAR data are addressed through spatio-temporal interpolation, and track longitudinal levels are predicted using a hybrid neural model that includes a hybrid convolutional neural network (CNN) with gated recurrent units (GRU) network and a hybrid CNN with a long short-term memory (LSTM) network. The models fuse historical and interpolated data from InSAR and ABA, enabling higher-frequency insights. A novel key performance index (KPI) based on predicted longitudinal levels is proposed to quantify track condition. The framework is validated on a transition zone at a railway bridge between Dordrecht and Lage Zwaluwe in the Netherlands. Results show that the hybrid model outperforms standalone methods and offers a good balance between accuracy and computational efficiency. The proposed approach enables earlier detection of irregularities, supporting prescriptive maintenance decisions.

This paper proposes an onboard measurement technology that combines a Laser Doppler Vibrometer (LDV) and an Axle Box Accelerometer (ABA) to approximate the dynamic load-response relationship of railway tracks. Unlike existing track-side and onboard technologies, this paper eliminates the need for load measurement, estimation, or control, enabling continuous measurements under operational conditions. The LDV mounted on the moving vehicle captures the track vibration response contactlessly, while the wheel vibration measured by ABA is used to directly represent the dynamic vehicle load. The LDV and ABA signals are combined to approximate the load-response relationship in the frequency domain. Experimental validation on a vehicle-track test rig demonstrates the effectiveness of the developed system at different speeds. Further comparisons with the hammer test result confirm its ability to capture the local dynamic properties of consecutive track segments along a railway track and also its superiority in measurement efficiency. This paper offers a promising solution for monitoring railway tracks on a large scale and allowing prescriptive maintenance of rail infrastructures.

The ratcheting phenomenon remains a persistent concern in modern railways due to its close association with head checks, a typical type of rolling contact fatigue. This study presents experimental research focussing on elucidating the mechanical, hardening, and material ratcheting properties of one bainitic (B320) and two pearlitic (R220 and R260MN) rail steels. The experiment consisted of monotonic tension, uniaxial cyclic strain range, and uniaxial cyclic stress range tests. Two load cases representing the equivalent stresses experienced by rails under real-life wheel-rail contacts were used in the cyclic stress range tests to assess the rail ratcheting behaviour in railway operating conditions. The test results highlighted that the two pearlitic steels showed similar mechanical strength and ratcheting behaviour; and by contrast, the bainitic steel exhibited superior mechanical strengths and yielded significantly weaker ratcheting responses for both load cases. The study then characterised the three rail steels by calibrating for them the hardening parameters of two classical constitutive models: Chaboche and Ohno-Wang II (OWII) based on the monotonic and cyclic strain range tests. The hardening parameters of the constitutive models were then optimied based on the cyclic stress range tests to represent the material ratcheting behaviours of rail steels for each load case. Notably, the OWII model demonstrated higher precision in reproducing ratcheting strains and rates than the Chaboche model, which faced limitations in simulating relatively low ratcheting rates. This study enhanced the understanding of the mechanical and ratcheting properties of the investigated rail steels and provided insights into the applicability of constitutive models for predicting and mitigating rail ratcheting effects.

The coefficient of friction (COF), defined as the maximum of the adhesion coefficient for a given contact condition, fluctuates rapidly due to environmental and operational factors. This paper introduces a torque modulation-based method for COF estimation. A simplified analytical model of the Manchester benchmark bogie operating under dry adhesion conditions is used to evaluate this method. The study presents an analytical equation that confirms earlier simulation-based findings showing a phase difference between applied torque modulation and resulting motor angular velocity. This phase relationship is shown to reflect the shape of the adhesion-slip curve. Notably, when the phase difference approaches 90°, the locomotive operates near the point of maximum adhesion, corresponding to the COF. Furthermore, the sensitivity of this approach to key system parameters, including normal load, wheel rolling radius, and modulation frequency, is examined. The findings provide valuable insights into the robustness and applicability of torque modulation-based COF estimation techniques in real-time traction control systems. The estimated COF can be further leveraged for adhesion management, driver advisory systems, and autonomous train operation.

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.

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.

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.

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.

This work presents the results of a measurement campaign to demonstrate the effectiveness of the axle box acceleration (ABA) technology for detecting rail defects. The measurements were conducted along the Iron Ore line between Sweden and Norway for the IN2TRACK3 project. This line is mostly single-track with passenger-freight mixed traffic and heavy axle load. Historical data and track information data were not considered in this study. By analyzing data acquired from the accelerometers in vertical and longitudinal directions, rail defects were detected in near real-time using big-data analytics. For our validated sections, 100% of rail defects (including squats) were detected using time-frequency analysis and an outlier detection approach. The methodology also allows for identifying priority locations, e.g., defective welds, joints, transition zones, etc., and its use for prescriptive maintenance recommendations is being explored in the framework of the IAM4RAIL project.

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.