Rail Surface Research Articles

Coupling effect of track irregularity and wheel–rail contact conicity on carbody swaying of a metro vehicle: an experimental investigation

A carbody swaying with low frequency of about 2 Hz occurs during vehicles with linear induction motors (LIM) running in shallow curve and straight sections on a China metro line, which significantly deteriorates the ride comfort of passengers. A series of field tests were carried out to identify the main cause and possible solutions. During the tests, the vibration characteristics, dynamic performance, wheel–rail contact condition and track condition were analysed. The test results show that the abnormal carbody swaying is a manifestation of a frequency coupled resonance between the track excitation frequency, bogie hunting frequency and carbody modal frequency at the operating speed. The periodic excitation of the track is the periodic track alignment irregularity with a wavelength of 11–13 m, which is caused by rail surface wear during long-term operation. The carbody swaying can be suppressed by reprofiling the wheel tread to LMA and optimising the suspension parameters.

RSDNet: A New Multiscale Rail Surface Defect Detection Model.

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

Wear diagnosis for rail profile data using a novel multidimensional scaling clustering method

AbstractThe diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM uses weighted‐probability distribution of dispersion patterns to extract accurate time domain features from rail profile data, and the loss of information is minimized, which can greatly improve the accuracy for wear diagnosis. All the analyzing data for real experiments are collected by a laser scanner camera on an inspection car, where heavy‐haul railway rails with different types of surface wear are inspected. Experimental results with simulated and reality‐based data show that the proposed methods can identify worn profile data and discriminate different types of worn profiles more effectively when compared with existing methods. Thus, the proposed method offers a new thinking for the diagnosis of rail surface wear for heavy‐haul railways.

Track Deterioration Model—State of the Art and Research Potentials

Track deterioration models (TDMs) help to allocate maintenance work (direct costs) to vehicle runs. Furthermore, these models demonstrate the impact of rolling stock properties on infrastructure. This paper review provides an overview of the state of the art in railway track deterioration modelling and outlines the research potential in this domain. The main focus lies on ballast degradation, rail surface wear and fatigue, and their description in an empiric analytic wear formula. The basis for discussion is the wear formula of the Graz University of Technology. While the TDM demonstrates effectiveness, enhancements are sought, particularly with regard to adjusting the track parameters that vary across railway networks. Further exploration aims to refine the description of rail surface wear and rolling contact fatigue (RCF), incorporating factors such as traction energy and short-wave effects and adapting mathematical functions such as the t-Gamma function. This review underscores the need for ongoing research to develop TDMs that are both simple and detailed enough to encourage track-friendly rolling stock design.

Effect of normalizing treatments on microstructure and electrochemical corrosion properties of U75V-welded joints in an acidic environment

Corrosion poses a significant threat to the structural integrity of steel rails, especially in an acidic environment. In this study, we evaluate the corrosion properties of U75V-welded joints using potentiodynamic polarization curves, electrochemical impedance measurements, and corrosion morphology studies under simulated acidic conditions. Compared to non-normalized joints, the normalized joints exhibit a higher self-corrosion current (4.65 × 10−5 A/cm2) and a more negative self-corrosion potential (−0.56919 mV). The smaller and more uniform grain size obtained after normalizing provides multiple active sites on the surface for polarization reactions. Furthermore, owing to significant erosion caused on the rail surface by the acidic solutions, normalizing treatment enhances the passivation effects on the surface, resulting in increased corrosion resistance (221.96 Ω cm2). Our findings provide insights into the impact of normalizing treatments on the corrosion mechanism of U75V rail joints under acidic conditions.

Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer

With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency.

An investigation into the constitutive relation of corrugated metro rail material based on nano-indentation experiment and inverse analysis

To establish the constitutive relation of corrugated rail material, rail corrugation was reproduced. The material mechanical properties of corrugation peaks and troughs were obtained by experiment. A set of dimensionless functions was proposed for the elastic modulus, hardness and yield stress at arbitrary depth of corrugation peaks and troughs. By combining the finite element model and dimensional functions, the constitutive relations were obtained at different vertical and lateral positions of corrugation. The closer the material is to the rail surface, the larger the mechanical property values. The elastic modulus and hardness of corrugated rail material at the trough surface are higher than those at the peak, while the yield stress at the peak surface is higher than the trough.

A vision-based nondestructive detection network for rail surface defects

A vision-based nondestructive detection network for rail surface defects

Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning.

The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between -1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.

Study on monitoring broken rails of heavy haul railway based on ultrasonic guided wave

Real-time monitoring of broken rails in heavy haul railways is crucial for ensuring the safe operation of railway lines. U78CrV steel is a common material used for heavy haul line rails in China. In this study, the semi-analytical finite element (SAFE) method is employed to calculate the dispersion curves and modal shapes of ultrasonic guided waves in U78CrV heavy steel rails. Guided wave modes that are suitable for detecting rail cracks across the entire cross-section are selected based on the total energy of each mode and the vibration energy in the rail head, web, and foot. The excitation method for the chosen mode is determined by analyzing the energy distribution of the mode shape on the rail surface. The ultrasonic guided wave (UGW) signal in the rail is analyzed using ANSYS three-dimensional finite element simulation. The group velocity of the primary mode in the guided wave signal is obtained through continuous wavelet transform to confirm the existence of the selected mode. It is validated that the selected mode can be excited by examining the similarity between the vibration shapes of a specific rail section and all modal vibration shapes obtained through SAFE. Through simulation and field verification, the guided wave mode selected and excited in this study demonstrates good sensitivity to cracks at the rail head, web, and foot, and it can propagate over distances exceeding 1 km in the rail. By detecting the reflected signal of the selected mode or the degree of attenuation of the transmitted wave, long-distance monitoring of broken rails in heavy-haul railway tracks can be achieved.

Intelligent railroad inspection and monitoring

Railways are essential to the global transportation infrastructure, providing eco-friendly and economical solutions for the movement of freight and passengers. Inspecting and maintaining extensive rail networks timely poses significant challenges. My group and collaborators have focused on automated railroad inspection technologies, emphasizing the use of deep learning and computer vision to overcome the limitations of traditional manual inspections. Our research introduces groundbreaking real-time inspection methods, leveraging a specialized dataset of railroad components for enhanced instance segmentation models, achieving unprecedented accuracy and inference speeds. The developed computer vision systems efficiently detect track components and their changes over time, and also quantify rail surface defects. Additionally, our work extends to improving railroad crossing safety, utilizing deep learning frameworks for the detection of unusual pedestrian behaviors and object identification, aimed at reducing crossing incidents and improving emergency response times. Our future research directions aim to further refine the cost-effectiveness and autonomy of railroad inspection systems. Through these innovations, we hope to aid in the inspection and maintenance of railroads, offering practical solutions for railroad and other civil engineering applications.

Deep learning techniques to detect rail indications from ultrasonic data for automated rail monitoring and maintenance

The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

Rail surface defect data enhancement method based on improved ACGAN.

Rail surface defects present a significant safety concern in railway operations. However, the scarcity of data poses challenges for employing deep learning in defect detection. This study proposes an enhanced ACGAN augmentation method to address these issues. Residual blocks mitigate vanishing gradient problems, while a spectral norm regularization-constrained discriminator improves stability and image quality. Substituting the generator's deconvolution layer with upsampling and convolution operations enhances computational efficiency. A gradient penalty mechanism based on regret values addresses gradient abnormality concerns. Experimental validation demonstrates superior image clarity and classification accuracy compared to ACGAN, with a 17.6% reduction in FID value. MNIST dataset experiments verify the model's generalization ability. This approach offers practical value for real-world applications.

Rail surface damage management through monitoring and modelling

ABSTRACT Rolling contact fatigue (RCF) cracking and wear significantly impact rail surfaces. The interaction between them is crucial; wear dominance can remove initiated cracks, while low wear rates may escalate crack propagation, elevating rail failure risks. Grinding, a preventive maintenance technique, removes remaining cracks, applied in fixed-interval or condition-based regimes. Operational constraints, favouring cyclic regimes, may potentially reduce rail life and increase costs. Thus, an optimal asset management strategy involves grinding based on current RCF condition and its predicted growth rate, considering its interaction with wear. London Underground employs diverse rail inspection methods, including advanced MRX-RSCM technology, to measure crack depth. Research used its measurements, developing a novel Combined Shakedown Map and Tγ approach. This predicts RCF growth under wear interaction, indicating the material to be removed by grinding. Its results are displayed for high and low rails of curved track sites, compared with monitoring measurements, enabling observation of changes in damage susceptibility and related grinding requirements over different rails.

New experimental evidences of corrugation formation due to rail longitudinal vibration mode

ABSTRACT This paper aims at identifying the formation mechanism of short pitch rail corrugation using an improved configuration of a 1/5 scaled V-Track test rig. The loading conditions of the V-Track are designed to simulate the wheel-rail dynamic interactions on the tangent track. Corrugation initiation and consistent growth are successfully produced on the rail surface with a characteristic wavelength of about 6.0 mm, equivalent to the field one of 30 mm. By analysing the wheel-rail dynamic forces, the wheel and track dynamic behaviours across different rotations, it is found that the rail longitudinal vibration mode and its induced longitudinal dynamic force determine the corrugation formation with the necessary initial excitation.

Research on control strategy of abrasive belt type rail grinding for corrugation repair process

The rail corrugation will lead to noise pollution and abnormal vibration in the railway operation process which greatly influences the safety, comfort, and stability of the train operation. However, although the advantages of abrasive belt rail grinding in repairing rail diseases have been verified, there is still no relative grinding control strategy for rail corrugation repair process due to its characteristics of regionality and no grinding benchmark. Therefore, this paper analyzed the difference between the grinding process of rail corrugation and ideal rail surface, and established material removal model for repairing of rail corrugation. By comparing the average valid value of moving wave amplitude and the improvement degree of rail corrugation, the most suitable grinding control strategies for repairing different types of corrugation diseases were obtained, which can further improve the grinding quality and efficiency of abrasive belt rail grinding in repairing rail corrugation.

Multi-Objective Optimization of Process Parameters in Laser DED Ni-Based Powder on Steel Rail Using Response Surface Design

With the rise of global industrialization, the requirements for the operating speed and carrying capacity of high-speed trains are increasingly higher. Because the wear and tear of rails gradually increases during the running of high-speed trains, strengthening or repairing rail surfaces is of paramount significance. Laser-directed energy deposition (DED) exhibits significant advantages in improving surface hardness, corrosion resistance, and abrasion resistance. Because of the multiple interacting optimization objectives, the development of a multi-objective optimization method for process parameters is significant for improving DED deposition quality. Response surface design employs multivariate quadratic regression equations to fit the functional relationship between the factors and the responses, which can be employed to find the optimal process parameters and solve multivariate problems. This study develops a multi-objective optimization model with response surface design and 2D process mappings to visually analyze the effects of scanning speed, laser power, and powder feed rate on aspect ratio, dilution rate, and microhardness. The optimal combination of process parameters for Ni-based alloys on U71Mn rail is a laser power of 431 W, a scanning speed of 5.34 mm/s, and a powder feed rate of 1.03 r/min. In addition, a multi-physics field finite element model is developed to analyze the evolution mechanism of the microstructure from the bottom to the top of the single track. This study can provide theoretical and technical support for the surface strengthening or repair of U71Mn rail.

High precision rail surface obstacle detection algorithm based on 3D imaging LiDAR

Railway perimeter intrusion detection is of great significance for ensuring railway safety operations. In this paper, a Light Detection and Ranging (LiDAR) system is designed and developed based on the specific environmental requirements of railway applications. The system is capable of autonomously adjusting the imaging range and angular resolution, enabling high-precision imaging within a 50 m monitoring area. Additionally, based on registration and a series of filtering algorithms, a railway surface obstacle extraction algorithm is developed. The algorithm achieves a 100 % detection rate for obstacles with a minimum height of 15 cm at any position within the monitoring area, and a 70 % detection rate for obstacles with a height of 10 cm. In this algorithm, a coarse registration method based on global planar features combined with the Iterative Closest Point (ICP) registration algorithm achieves sub-centimeter registration accuracy for consecutive point cloud frames. The filtering algorithm based on local planar features enables precise extraction of geometric changes in the railway surface, with simple calculations and fast processing speed, making it highly valuable for extracting object edge features.

Analysis of wheel-rail adhesion redundancy considering the third-body medium on the rail surface

PurposeIn response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface, this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.Design/methodology/approachBased on the PLS-160 wheel-rail adhesion simulation test rig, the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip. Through statistical analysis of multiple sets of experimental data, the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained, and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed. The study analyzes the utilization of traction/braking adhesion, as well as adhesion redundancy, for different medium under small creepage and large slip conditions. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived.FindingsWhen the third-body medium exists on the rail surface, the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance. Compared with the current adhesion control strategy of small creepage, adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization, thereby ensuring the traction/braking performance and operation safety of the train.Originality/valueMost existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions, without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train. Therefore, there is a risk of traction overspeeding/braking skidding. This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.

The influence of asymmetric rail cant on rail corrugation in a small-radius curve section

A suitably designed rail cant can improve the wheel-rail alignment, effectively reduce the wheel-rail contact pressure and reduce the wheel-rail wear. However, the rail base cant has rarely been specifically designed for suppressing rail corrugation. Furthermore, no studies have yet been conducted on using different inner and outer rail cants in small-radius curve sections from the perspective of suppressing rail corrugations and wear. In this study, the growth rate index of rail corrugation is obtained by combining an impact hammer origin admittance test and a coupled vehicle-track multi-body dynamic model simulations. The wear depth of rail surface is obtained by combining a three-dimensional transient wheel-rail rolling contact model and Archard wear theory. From the overall perspective of preventing rail corrugation and slowing down rail wear, based on the multi-objective optimization using a particle swarm optimization algorithm, the optimal configuration of inner and outer rail cants in a small-radius curve section were determined as 1/22 and 1/40, respectively, which provided effective suppression of rail corrugation initiation and further development of rail wear.