Track Defects Research Articles

Detection of rail local defects using in-service trains

As track components deteriorate, their interaction dynamic response will alter accelerations on the train. Measuring acceleration on train components is a method to conduct condition monitoring of track defects. The measured signal can be used for the detection of rail surface defects which implement impacts on measured accelerations. In this paper, the axle-box acceleration (ABA) is measured in a subway as a case study. Fourier transform (FFT), empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods are used to study accelerations relative to the track. Wheel frequencies are calculated using finite element method (FEM) to determine frequency couplings. Velocity-dependent/independent components and source of excitation of the measured signal are distinguished. Results indicated that the FFT approach can be applied for both velocity-dependent and velocity-independent vibration components for frequencies up to 680 Hz. Also, the EEMD method can be used to distinguish the impact component of the measured signal.

Assessment technologies of rail systems' structural adequacy - A review from mechanical engineering perspectives.

The structural adequacy challenges of railroad track structures have received considerable attention globally. Track defects and failures due to weak strength and buckling effect account for one-third of all railroad accidents. The current paper provides a comprehensive study of the recent work on the structural adequacy/bearing capacity of rail systems from mechanical engineering perspectives; existing techniques for track stiffness/modulus evaluations, including standstill and continuous methods. Further, this paper demonstrates the current techniques for track stiffness/modulus evaluation. Prevailing track modulus techniques, while accurate but time-taking, effortful, requires a track closure and provides only single-point information. Also, this review provides a suggestion on the non-destructive and non-invasive technologies for example Ground-penetrating radar (GPR) and evaluation of the substructures of tracks as they have great potential for image subsurface features.

Defect classification of railway fasteners using image preprocessing and alightweight convolutional neural network

Railway fasteners are used to securely fix rails to sleeper blocks. Partial wear or complete loss of these components can lead to serious accidents and cause train derailments. To ensure the safety of railway transportation, computer vision and pattern recognition-based methods are increasingly used to inspect railway infrastructure. In particular, it has become an important task to detect defects in railway tracks. This is challenging since rail track images are acquired using a measuring train in varying environmental conditions, at different times of day and in poor lighting conditions, and the resulting images often have low contrast. In this study, a new method is proposed for the classification of defects on rail track fasteners. The proposed approach uses image enhancement to first filter the rail images and obtain a high contrast image. Then, the rail track and sleeper positions are determined from the high contrast image. The location of the fastener is determined by applying the line local binary pattern method and the defects of the fastener are classified using an improved lightweight convolutional neural network (LCNN) model. Features are extracted from two fully connected layers of the developed LCNN model and the feature vector is constructed by concatenating these layers. The concatenated features are processed using a number of machine learning methods and the optimum classifier is chosen. Experimental results show that Cubic SVM gives the best results with a detection accuracy rate of 99.7%.

Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion.

Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection.

Investigation on train dynamic derailment in railway turnouts caused by track failure

Train derailments in low-speed railway turnout areas are very common due to poor track alignment and rail failures, and in extreme cases can lead to casualties and damage to infrastructure and railway vehicles. This study aims to explore the dynamic derailment behaviour caused by rail joint failures before the railway switch panel, and to understand the effects of crucial parameters on the operating safety of trains. To this end, a 3D dynamic coupled freight train-track simulation model is developed based on the MBS-FEM method. The model takes into consideration the lateral difference between rail joint sections that directly cause the wheel to rise. Flexible deformations of the rail and wheelset are modelled, and the derailment mechanism of wheel jumping and climbing in the railway switch panel is revealed. Results show that increasing the curve radius and distance between the rail joint and switch toe can effectively improve passing safety. When a sharp curve has to be placed before the switch panel due to site limitations, reducing the wheel-rail coefficient can also ensure better operating safety. This study indicates that the proposed MBS-FEM dynamic coupled derailment simulation model is able to efficiently evaluate the operating safety as a result of various wheel and track defects, and thus form a basis for providing a cost-effective platform for the future parameter optimization of railway tracks and vehicles.

Design of a robot for inspection and diagnostic operations of rail track facilities

PurposeThe need to examine the integrity of infrastructure in the rail industry in order to improve its reliability and reduce the chances of breakdown due to defects has brought about development of an inspection and diagnostic robot.Design/methodology/approachIn this study, an inspection robot was designed for detecting crack, corrosion, missing clips and wear on rail track facilities. The robot is designed to use infrared and ultrasonic sensors for obstacles avoidance and crack detection, two 3D-profilometer for wear detection as well as cameras with high resolution to capture real time images and colour sensors for corrosion detection. The robot is also designed with cameras placed in front of it with colour sensors at each side to assist in the detection of corrosion in the rail track. The image processing capability of the robot will permit the analysis of the type and depth of the crack and corrosion captured in the track. The computer aided design and modeling of the robot was carried out using the Solidworks software version 2018 while the simulation of the proposed system was carried out in the MATLAB 2020b environment.FindingsThe results obtained present three frameworks for wear, corrosion and missing clips as well as crack detection. In addition, the design data for the development of the integrated robotic system is also presented in the work. The confusion matrix resulting from the simulation of the proposed system indicates significant sensitivity and accuracy of the system to the presence and detection of fault respectively. Hence, the work provides a design framework for detecting and analysing the presence of defects on the rail track.Practical implicationsThe development and the implementation of the designed robot will bring about a more proactive way to monitor rail track conditions and detect rail track defects so that effort can be geared towards its restoration before it becomes a major problem thus increasing the rail network capacity and availability.Originality/valueThe novelty of this work is based on the fact that the system is designed to work autonomously to avoid obstacles and check for cracks, missing clips, wear and corrosion in the rail tracks with a system of integrated and coordinated components.

Accurate and effective framework for identifying track defects

Timely and effective identification of track surface defects is helpful to improve the safety of railway operation. However, the complex service environment leads to the diversity of defects, and it is difficult to realize automatic identification. Therefore, an improved residual network feature extraction module is proposed, and a framework of track surface defect identification is constructed. To be specific, the overall framework and the core method are described in detail at first. Then, the optimal training parameters are determined through experiments, and the performance of different feature extraction modules is further compared. Finally, the performance of the proposed framework is verified by experiments using multi-class images. The results show that the average identification accuracy is 94.45%, the average frame speed is 0.000052 s. Correspondingly, the feature identification model adopted in this paper offers Accuracy of 97.09%, Loss of 0.0773 and RMSE of 0.06. The proposed model yields strong robustness.

Track Defect Detection for High-Speed Maglev Trains via Deep Learning

The high-speed maglev train is a new type of transportation. The long stator track plays a critical role in the levitation guidance and traction system. Therefore, its condition directly affects the operation of maglev trains. It is extremely important to detect the abnormal condition of high-speed maglev tracks to ensure the stable, safe, and reliable operation of the train. In this article, an onboard image detection system is designed for high-speed maglev tracks, which can accurately obtain the image of long stator tracks under the harsh conditions of limited installation space, insufficient illumination, and rapid operation of vehicles. High-speed maglev trains are not yet in widespread use. In China, there is currently only one demonstration operating line located in Shanghai, and the length of the track test line is limited. Therefore, the number of track images that can be acquired is extremely limited. In view of the lack of defective samples of high-speed maglev tracks, this article proposes a data enhancement method based on sample generation and image fusion to augment the dataset of defective samples. To improve the quality of generated high-speed maglev track defect images, a joint attention layer (JEA) combining squeeze-and-exception (SE) block and spatial attention module (SAM) is designed and introduced into the generative adversarial network (GAN). This work provides a data basis for the study of track defect detection of high-speed maglev trains. In addition, this article detects the defects of high-speed maglev tracks via deep learning-based target detection algorithms, which can automatically detect, accurately classify and locate the defects of stator surface and cables, filling the gap in the field of high-speed maglev track defect detection.

IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis

Rail is one of the most energy efficient and economical modes of transportation amongst many. Regular inspection of railway track health is essential for ensuring robust and secure train operations. Investigations and delayed discovery pose a serious risk to the safe functioning of rail transportation. The traditional method of manually examining the rail track using a railway cart is both inefficient and susceptible to mistakes and bias. It is imperative to automate inspection in order to avert catastrophes and save countless lives particularly in zones where train accidents are numerous. The purpose of this research is to develop an Internet of Things (IoT)-based autonomous railway track fault detection system to enhance the existing railway cart system in order to address the aforementioned issues. In addition to data collection on Pakistani railway lines, this work contributes significantly to railway track fault identification and classification based on acoustic analysis, as well as fault localization. Due to their frequency of occurrences, six types of track’s faults were first targeted: wheel burnt, loose nuts and bolts, crash sleeper, creep, low joint, and point and crossing. Support vector machines, logistic regression, random forest, extra tree classifier, decision tree classifier, multilayer perceptron and ensemble with hard and soft voting were among the machine learning methods used. The results indicate that acoustic data can successfully assist in discriminating the track defects and localizing the defects in real time. The results show that MLP achieved the best results, with an accuracy of 98.4 percent.

Experimental Analysis of Vertical Vibrations of a Railway Bogie

The paper presents a study of the vertical vibrations of a bogie of a passenger vehicle, based on the acceleration of the axles and the bogie frames, measured during the running at a constant velocity. To this purpose, the root mean square (RMS) acceleration is calculated for more measurement sequences at different velocities. In principle, the RMS acceleration increases along with the velocity and influence of the variability of the amplitude in the track defects upon the dispersion of the values in the RMS acceleration. Based on the spectral analysis of the measured acceleration, wheel defects and undulatory wear of the rolling surfaces of wheels and rail are highlighted.

Мониторинг технического состояния железнодорожного пути с использованием метода непрерывной регистрации динамических процессов, возникающих при взаимодействии подвижного состава и пути

Objective: To develop the method to monitor the technical condition of the railway track. Me-thods: A strain-gauge wheel pair is used for continuous recording of vertical and lateral interaction forces in a dynamic wheel–rail system. Results: Stability margin factors of a wagon relative to de-railment have been determined and the defective (prone to derailment) sections of a railway track have been identified with the exact identification of their location (GPS coordinates) on the map using navigation devices. Practical importance: The developed monitoring method makes it possi-ble to promptly re¬gister and eliminate railway track defects

Railway Track Failure Detection System

Indian railway is one of the largest networks in the country. Its motto is “the life line of the country”, and the main transport is completed by the railways of the country. Railroad is one of the cheapest and safest means of transport, but there are also certain accidents on the railroad. 60% of accidents are caused by road failures or the formation of cracks in the road. Today’s rail systems involve manual track inspection, which is cumbersome and not entirely effective. However, the detection and correction of track defects is a problem for all railway companies in the world. The objective of this research work is to detect railroad track failures with the help of ultrasonic sensor and show the exact location of the crack on web app by using GPS module.

Hybrid Reduction Techniques With Covariate Shift Optimization in High-Dimensional Track Geometry

Abstract In discussions of track geometry, track safety takes precedence over other requirements because its shortfall often leads to unrecoverable loss. Track geometry is unanimously positioned as the index for safety evaluation—corrective or predictive—to predict the rightful maintenance regime based on track conditions. A recent study has shown that track defect probability thresholds can best be explored using a hybrid index. Hence, a dimension reduction technique that combines both safety components and geometry quality is needed. It is observed that dimensional space representation of track parameters without prior covariate shift evaluation could affect the overall distribution as the underlying discrepancies could pose a problem for the accuracy of the prediction. In this study, the authors applied a covariate shift framework to track geometry parameters before applying the dimension reduction techniques. While both principal component analysis (PCA) and t-distributed stochastic neighbor embedding (TSNE) are viable techniques that express the probability distribution of parameters based on correlation in their embedded space and inclination to maximize the variance, shift distribution evaluation should be considered. In conclusion, we demonstrate that our framework can detect and evaluate a covariate shift likelihood in a high-dimensional track geometry defect problem.

Railway Track Loss-of-Stiffness Detection Using Bogie Filtered Displacement Data Measured on a Passing Train

This paper presents an innovative numerical framework for railway track monitoring using acceleration measurements from sensors installed on a passenger train. A numerical model including a 10 degrees of freedom train passing over a three-layer track is employed. The bogie filtered displacement (BFD) is obtained from the bogie vertical acceleration using a numerical integration method and a band-pass filter. The BFD is compared to the filtered track longitudinal profile and can be seen to contain the main features of the track profile. This is also experimentally confirmed using field measurements where an in-service Irish Rail train was instrumented using inertial sensors. The proposed algorithm is employed to find the BFDs from the bogie accelerations. A track level survey was also undertaken to validate the measurements. It is shown that the BFDs from several passes are in good agreement with the surveyed profile. Finally, the BFDs are numerically used to find track defects such as hanging sleepers. The mean of the BFDs obtained from two populations of train passes over a healthy and a damaged track are employed to detect the loss of stiffness at the subgrade layer. The effect of the train forward speed variation and measurement noise are also investigated.

Time–Frequency Random Approach for Prediction of Subway Train-Induced Tunnel and Ground Vibrations

A time–frequency random approach is proposed in this paper for the prediction of subway train-induced tunnel and ground vibrations. This is a development of the random approach previously proposed by the authors, which takes the discrete track support, singular track defects, etc., into consideration. The proposed approach is developed using a two-step method. First, the pseudo-excitation method (PEM) and the two-dimensional multibody system/finite element method model are effectively combined to derive the track–tunnel pseudo-interaction forces by employing the power spectral density of track irregularity. Second, the random vibrations of the tunnel–soil system are obtained via the PEM in the wavenumber–frequency domain. To improve the computational efficiency, a fast-computing strategy is proposed based on the multipoint synchronous algorithm. Using numerical examples, the proposed time–frequency hybrid modeling process is verified by comparing it with the fully coupled time-dependent three-dimensional approach. Furthermore, the influence of the discrete track support on the random vibrations of the tunnel and ground is discussed by comparing the results predicted by the proposed approach with those predicted by the previously developed approach.

Rheology study of ballast-sleeper interaction with particle image Velocimetry (PIV) and discrete element modelling (DEM)

• Particle Image Velocimetry (PIV) was applied for DEM model calibration on mesoscopic level. • Ballast rheology (movements) of one ballast bed cross section is presented and analysed. • Lateral resistance of ballast bed to sleeper is studied from the perspective of ballast rheology. Ballast rheology is a phenomenon that describes movements of ballast particles due to the discrete nature, which eventually leads to the ballast bed fluid deformation after a long-time service. In most cases, ballast rheology is the main reason of track irregularity that leads to some track defects, e.g., hanging sleeper and mud spots. Therefore, it is significant to confirm the ballast rheology mechanism, which not only benefits for alleviating track defects, prolonging track service and providing safe transportation, but also provides an innovated means for accurately calibrating the discrete element method (DEM) models. Towards this aim, the Particle Image Velocimetry (PIV) is utilised to study ballast rheology through measuring ballast particle displacements in the single sleeper push test (SSPT). The ballast rheology results are compared with those from the DEM SSPT model, through which the DEM model is calibrated. Results show that the PIV is an effective technical means for ballast rheology study and DEM model calibration. This study is helpful for the researchers to build more precise DEM models, further providing theoretical methodology for ballast track construction and innovations.

Evaluation of lateral stability of railway tracks due to ballast degradation

Track lateral stability is one of the most critical considerations for safe and reliable railway infrastructures. With increasing exposures to high temperatures globally, a greater expansion in continuous welded rails can induce a higher risk of track buckling, especially when track defects exist. In ballasted track structures, ballast layer holds sleepers in place and provides lateral support and stiffness to the track. Note that there are several factors influencing the lateral resistance of ballasted railway tracks. However, the effects of the progressive degradation of the ballast on the track’s lateral resistance have thus far never been fully investigated. Note that the fouling conditions can be due to the accumulation of ballast breakage or external contamination, such as subgrade intrusion or coal dust, and difficult to inspect in the field. It is evidenced that track buckling can incur even if the railway track and ballast seem to be in a good condition by visual inspection. Therefore, this paper presents a more realistic model to study Single Sleeper (Tie) Push Test (STPT) conditions using the Discrete Element Method (DEM) with the objective to evaluate ballasted track lateral resistance considering different fouling scenarios. Note that coal dust, acting as a lubricant, is considered as a fouling agent. The lateral force–displacement curves of sleepers are analysed. The lateral force is derived from the sleeper-ballast contact forces obtained from three main components: sleeper bottom friction, sleeper side friction, and sleeper end force. The fouling conditions are employed by adapting appropriate model parameters to the ballast layer that represents the fouled ballast condition by coal dust in the DEM simulations. Note that the fouling layer is considered to start from the bottom of the ballast layer and is applied all the way to the top to represent the completely fouled ballast layer condition. The results indicate that fouled ballast can significantly undermine the lateral stability of ballasted tracks by more than about 50%. Track lateral stiffness may be reduced significantly due to fouled ballast layer conditions that cannot be inspected visually in the field. This may reduce track restraint and increase the likelihood of track buckling even though the degraded ballast does not have direct contact with the sleeper. Finally, the study will enrich the development of inspection criteria for ballast lateral resistance and support conditions, improve safety and reliability of rail network, and mitigate the risk of delays due to track buckling leading to unplanned maintenance.

Identify severe track geometry defect combinations for maintenance planning

ABSTRACT Combined track defects (multiple types of track defects occurring in the same vicinity of a track section) pose greater challenges and risks for train operational safety. This study proposed a method to identify severe track defect combinations that pose greater risks for vehicle derailments. The method includes three main stages. The first stage uses literature review, industry survey, and Track Recording Car data analysis to determine track defect combinations that exist in reality and are of interest to the infrastructure managers. The second stage uses single vehicle simulations to further fine-tune the focus and select the more severe combinations. The third stage uses comprehensive simulations to determine the increases of derailment risks due to combined track defects. More than 5,000 simulations were conducted in this study using supercomputers. A tabular tool was developed to help infrastructure managers to adjust maintenance reactions when track defects are found in combinations.

Narrowing longitudinally coupled prefabricated slab track maintenance duration with field data analysis of slab deformation under high temperature

The deformation of longitudinally coupled prefabricated slab track (LCPST) due to high temperature may lead to a reduction in ride comfort and safety in high-speed rail (HSR) operation. It is thus critical to understand and track the development of such defects. This study develops an online monitoring system to analyze LCPST deformation at different slab depths under various temperatures. The trackside system, powered by solar energy with STM8L core that is ultra-low in energy consumption, is used to collect data of LCPST deformation and temperature level uninterruptedly. With canonical correlation analysis, it is found that LCPST deformation presents similar periodic variation to yearly temperature fluctuation and large longitudinal force may be generated as heat accumulates in summer, thereby causing track defects. Then the distribution of temperature and deformation data is categorized based on fuzzy c-means clustering. Through the distribution analysis, it is suggested that slab inspection can be shortened to 6 hours, i.e. from 10:00 am to 4:00 pm, reducing 14.3% track inspection workload from the current practice. The price of workload reduction is only a 2% chance of missed detection of slab deformation. The finding of this research can be used to enhance LCPST monitoring efficiency and reduce interruption to HSR operation, which is an essential step in promoting reliable and cost-effective track service.

Effect of track defects on track deformations for high-speed railway bridge

Track defects can change the interaction between layers and affect the nonlinear contact between the beam and base slab, causing deformations in the rail. This study proposes a theoretical model to clarify the influence mechanism of track defects on the track irregularity of high-speed railway bridges under pier settlement. The investigated track defects include the void under slab, interlayer debonding, and fastener fracture. The elongation coefficient of track deformation is used to describe the mapping characteristics of the interlayer parameters to track geometric under the additional bridge deformation. The geometric characteristics and changing trend of the rail surface are quantified under the influence of the failure position and critical length. Finally, the fundamental mechanisms of track deformation are elucidated, which lays a theoretical basis for further study of the long-term evolution of infrastructure.