Track Maintenance Research Articles

Evaluating the efficiency of rail gauge maintenance in Siberia

The state of gauge geometry is the basis for safe and uninterrupted train operations under the mixed traffic conditions, as well as in the sections where trains with increased weight and length, including wagons with an increased axle load, are handled. When passing such trains, the state of gauge geometry deteriorates. This leads to increased labour costs for track maintenance, which is also caused by climatic features. Under such conditions, it is important to carry out effective gauge maintenance works. To analyse an impact of maintenance on gauge geometry, it is offered to determine the effect of uneven settlement of the under sleeper base in spring on the dynamics of each type of deviation. To evaluate the efficiency of preventive track alignment in the plan and profile, an analysis of graphical charts for the state of gauge geometry depending on track maintenance by gangs of track fitters and preventive track alignment by machine systems was carried out. It is also revealed that the efficiency of preventive alignment is significantly reduced under harsh climatic conditions, especially for the tonnage handled exceeding 600 million tonnes gross, and should therefore be carried out more frequently.

Cost reduction with single axle running gears in metro trains

Energy and maintenance costs constitute a large part of the recurring cost for railway operation. The vehicle weight impacts both energy consumption and the need for tamping of the track, while curving performance impacts the curving resistance and the wear on wheels and rails. In the project RUN2Rail and further in NextGear, both parts of the EU-funded initiative Shift2Rail, a single axle running gear with composite frame and active wheelset guidance was proposed for metro vehicles.The present study is comparing a reference vehicle from Metro Madrid with the proposed vehicle in terms of energy consumption and maintenance cost for simulated service on Metro Madrid Line 10 with curvature, gradients, stops and speed profiles considered. The calculated yearly saving for each vehicle become about 50 k€/(year*trainset), split on 20% on energy and 40% on each of wheel and track maintenance.

Influence of transport and logistics infrastructure of industrial enterprises on the efficiency of marshalling yards

The article considers the problems of interaction between marshalling yards and industrial enterprises under the conditions of uneven operations. The need for modelling of transport processes at marshalling yards and non-public tracks is revealed and grounded. Based on the conducted study, the authors offer recommendations to develop a simulation model for marshalling yard operations. This model being developed with the AnyLogic adaptive programming matches the structure and technological process of marshalling yards and freight point operations and provides the solution of a problem of optimizing the marshalling yard’s handling capacity. The process approach taken will reduce transportation costs emerging in the maintenance of non-public tracks and provide for the freight transportation regularity.

Prediction model for the maintenance of rail infrastructure in Java

Maintenance is the most prolonged phase after constructing a railway track is completed and operated. The initial indication of the need for railway track maintenance can be seen from the track quality index (TQI) value. Maintenance of railway tracks can be based on the TQI data category, which is a track superelevation, leveling, lining, and gauge width with TQI categories ranging from very good, good, fair, and poor. In the existing condition, only one-track measurement train, the EM-120, is owned by PT Kereta Api Indonesia (Persero) operates on the island of Java, so there are still railway tracks that still need to be measured by track measurement trains and require a TQI value. Implementing the TQI categorization is necessary for maintenance; hence a comprehensive study is essential to monitor and track the advancement of the research. This paper will map the literature on railway track maintenance, TQI, and prediction models. The literature database was taken from Google Scholar and analyzed using the VOS viewer tool with a mapping of previous research. The results of this research are highly useful in understanding the current development of railway track maintenance research; however, a study has yet to be identified that predicts the TQI category for railway tracks that have not been surveyed by track measurement trains.

Semi-Direct Multimap SLAM System for Real-Time Sparse 3-D Map Reconstruction

The lack of a sufficient number of reliable corners in low-textured environments is a big challenge for classical visual simultaneous localization and mapping (SLAM), especially for point feature-based methods. Many other features (i.e., line and plane segments) are often combined with points to restore an environmental structure. However, using such features requires much computational time. This work focuses on the reliable and high-performance real-time operation of a SLAM system in low-textured scenarios. It proposes a semi-direct multi-map monocular SLAM system (SM-SLAM) that combines direct tracking and feature-based map maintenance with point features and line segments. The proposed system tracks non-keyframes based on a sparse image alignment method for fast tracking, extracts and matches point features and line segments in keyframes for high-quality environment structure and motion optimization. We present an extensive evaluation on two widely-used public datasets and some challenging real-world scenarios. Experimental results show that SM-SLAM can well reconstruct a sparse 3D map with geometrical structure information in 30-40 Hz. Notably, SM-SLAM shows an accuracy improvement of more than 20% than the Oriented FAST and Rotated BRIEF feature-based SLAM (ORB-SLAM) on some low-speed, small-range camera motion datasets and performs well in low-texture scenarios.

Rail transport research and innovation in Europe: an assessment based on recent European Union projects

Adequate research and innovation is paramount for the seamless development, testing, adoption and integration of new rail concepts and technologies. This study adopts a structured methodology to provide a comprehensive analysis of initiatives in Europe in this field. First, it provides a policy overview in Europe in the past years. After that, it provides quantitative analyses from European Union funded research. Finally, it critically addresses research by six thematic areas: efficient stock design and manufacturing, emissions and noise reduction, track and stock maintenance, efficient rail operations, infrastructure management, information and communications technologies solutions. In doing so, it highlights current developments and identifies future needs. These results can help funding organisations, policy makers and researchers to make better-informed choices to push the field forward.

Modelling of the quasi-static and dynamic mechanisms on ballasted and ballastless tracks

The evaluation of the short and long-term performance of the railways’ infrastructure can have vital importance on the track maintenance operations. This work presents a hybrid methodology capable of simulating the stresses induced in the subgrade and predicting the permanent deformations induced by the passage of the trains. The stresses are obtained using the 2.5D FEM-PML approach, while the permanent deformations and cumulative displacements are obtained through the implementation of an empirical permanent deformation model. This methodology allows obtaining the short and long-term results very expeditiously. This work also evaluates the importance of the dynamic effects on the stresses and permanent deformations and compares the performance of the conventional ballasted track, ballastless track (Rheda system) and an optimised ballastless track constituted only by a concrete slab.

A Feasibility Analysis of the Infrared Thermography Technique in Surface Crack Detection for High‐Speed Rail Slab Track

The surface crack on track slab is the main cause of the service reliability deterioration of the high‐speed rail (HSR) slab track. To realize the rapid and accurate detection of these cracks, infrared thermography (IRT) can be regarded as an effective way in HSR application. However, the performance of IRT can be significantly affected by the testing environment. This study aims to analyze the feasibility of IRT in crack detection for HSR slab track by investigating the key factors affecting the detection accuracy. Firstly, the principle of IRT in detecting surface crack of track slab is given, and the key factors influencing the detection performance are discussed. Considering the difficulty in conducting field tests for slab tracks, this study establishes a finite element (FE) model to simulate the temperature field of track slab with surface cracks. This model is verified by using the field measurement results of track slab temperature. With the FE model, the effects of detection time, ambient temperature, and sensitivity of the thermal imager on the detection accuracy are revealed. The results show that, during the window time for track maintenance (0:00 am–3:00 am), the difference of temperature between crack areas and noncrack areas gradually increases with time, so it is recommended to conduct detection during 2:00–3:00 am; under the condition of no auxiliary heat source, the crack detection is suggested to be arranged in summer with a high incidence of crack; the minimum limit of thermal sensitivity of thermal imager is below 108mk@25°C. The findings in this research can offer guidance for the implementation of IRT‐based crack detection for HSR slab crack.

Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM.

Railway track maintenance plays an important role in enabling safe, reliable, and seamless train operations and passenger comfort. Due to the increasing rail transportation, rolling stocks tend to run faster and the load tends to increase continuously. As a result, the track deteriorates quicker, and maintenance needs to be performed more frequently. However, more frequent maintenance activities do not guarantee a better overall performance of the railway system. It is crucial for rail infrastructure managers to optimize predictive and preventative maintenance. This study is the world's first to develop deep machine learning models using three-dimensional recurrent neural network-based co-simulation models to predict track geometry parameters in the next year. Different recurrent neural network-based techniques are used to develop predictive models. In addition, a building information modeling (BIM) model is developed to integrate and cross-functionally co-simulate the track geometry measurement with the prediction for predictive and preventative maintenance purposes. From the study, the developed BIM models can be used to exchange information for predictive maintenance. Machine learning models provide the average R2 of 0.95 and the average mean absolute error of 0.56 mm. The insightful breakthrough demonstrates the potential of machine learning and BIM for predictive maintenance, which can promote the safety and cost effectiveness of railway maintenance.

Updating the criteria for assessing the state of the railway track according to the data of track measuring equipment

Introduction. One of the consequences of the introduction of the Instructions for Assessing the Condition of the Rail Track (enacted on February 28, 2020 No. 436/r) was an increase in the number of newly detected deviations from the norm of track maintenance and their reassignment as malfunctions. As a result, the number of kilometres with limited train speeds has increased by up to 3 times, which may lead to significant losses in transportation and an increase in the cost of the immediate track maintenance.Materials and methods. Data from a special thoroughfare of a diagnostic complex along the operating lines of Russian Railways were used to analyse the actual distribution of types and magnitudes of malfunctions, the number and causes of the speed limits. The authors used computer simulations to determine the dynamic indicators of cars in accordance with the algorithm of the deterministic method for calculating the movement of cars along the railway track.Results. The consequences for assessing the state of the track from the standards specified in Instruction 436/r are analysed. The authors present the results of mathematical modelling of the rolling stock interaction with the track, confirming the feasibility of the proposed changes in the standards: increasing the permissible speeds of passenger trains, assessing the gauge widening in curved sections, clarifying the estimates of unbalanced acceleration.Discussion and conclusion. It is proposed to change a number of standards for assessing deviations from the norms of track maintenance in order to reduce the number of speed limits.

Dynamic Stray Current Measuring Methods in Urban Areas

In areas where urban tracks are used as public transportation, dynamic stray currents cause high maintenance costs for the tracks and metal structures near the tracks. S tray currents caused by rail vehicles depend on many factors (traffic density, vehicle speed, acceleration and deceleration, soil and track moisture), so it is very difficult to get a clear picture of the harmfulness of the stray current based on the results of a single field measurement. However, there are several measurement methods that can be used to determine the presence of stray currents and predict appropriate track maintenance actions. Some of these methods are described in this article, namely the use of stray current mapper, measurement of rail potential and rail current, measurement at the stray current collection system, and the use of non-destructive sensors. In track construction, measuring the electrical potential between rail and ground is one of the most common methods of detecting the damaging influence of stray current.

Экспериментальные исследования колебаний трамвая «Витязь-М»

Objective: Based on experimental studies of vibrations of Vityaz-M tram car occurring at speeds of 50 km/h or more on track straight sections with P65 rails on reinforced-concrete sleepers, to evaluate the effect of transversal vibrations on traffic safety and passenger comfort. To show the inconsistency of existing regulatory documentation on the structure and maintenance of tram tracks and tram chassis with the development prospects. Methods: Experimental measurements of vibration accelerations of tram body and tram chassis, having different mileage, when moving along track experimental section. Measurement and analysis of geometric parameters of rail track experimental section and of trams. Results: Measurements of vibration accelerations occurring during tram motion with 20 to 50 km/h speeds were performed as well as geometric parameter analysis for rail track experimental section and rolling stock wheels’ profiles, participated in the tests, was pursued. During the races, vibration accelerations that occur during movement were measured in three directions — longitudinal, transversal and vertical. The appearance of transversal vibrations of tram body affecting passenger discomfort and traffic safety has been confirmed. The root mean-square values of the amplitudes of vibration accelerations towards three directions in acceleration gravity (g) fractions have been obtained in two variants — at trolley motion between joints and at joints. Vibration acceleration frequency analysis has confirmed a sharp increase in the amplitude of transversal vibrations at frequencies in the region of 1–2 Hz at a speed of 50 km/h for a tram with worn wheels. The compliance of rail track experimental section geometry with the accepted standards as well as the compliance of rolling stock wheels dimensions with constructive documentation are noted. Practical significance: The need for further comprehensive study of the interaction of track and rolling stock while high-speed tram introduction with the purpose to improve existing standards on the structure and maintenance of tram tracks and tram chassis.

Investigation of a dynamic chord method and its application on track irregularity evaluation and track maintenance

Track irregularity causes dynamic train-track interactions and accelerates the degradation of their components. In this paper, we proposed a dynamic chord method based on dynamic measurements that can be directly used for maintenance planning. Based on the extensive investigations, we found that the results from the dynamic chord method are comparable to the static chord measurement results with appropriate cut-off wavelength and chord length. Specifically, the maximum chord lengths to apply on the dynamic measurements with cut-off wavelengths of 42 m, 70 m, and 120 m are 20 m, 30 m, and 40 m respectively. Track settlement can be identified based on the difference analysis between the static and dynamic chord results when the chord length is larger than the maximum chord length. Further, we proposed a method to evaluate track stiffness based on the peak differences between static and dynamic measurements. A threshold of 2 mm is used to determine whether there are track stiffness anomalies. The validity of the method is shown with experimental tests and field validations. Finally, a more direct method to guide track adjustment based on dynamic measurement is proposed. The application of this method on track adjustment shows that the amplitude of dynamic longitudinal level and the standard deviation reduces by 42 % and 51 % respectively. Periodic longitudinal level at the wavelength of 32 m, due to the bridge span, also sees significant improvements.

Estimation of Vehicle Dynamic Response from Track Irregularity Using Deep Learning Techniques

To improve the quality of track maintenance work, it is a desire to estimate vehicle dynamic behavior from track geometry irregularities. This paper proposes a deep learning model to predict vehicle responses (e.g., vertical wheel-rail forces, wheel unloading rate, and car body vertical acceleration) using deep learning techniques. In the proposed CA-CNN-MUSE model, convolutional neural networks (CNNs) are used to learn features of track irregularities, and multiscale self-attention mechanisms (MUSE) are employed to capture the long-term and short-term trends of sequences. Coordinate attention (CA) is introduced into CNN to focus on important interchannel relationships and important spatial mileage points. The experiments were performed on a multibody simulation model of the vehicle system and the measured data of the actual high-speed line. The results show that the CA-CNN-MUSE has high prediction accuracy for vertical vehicle responses and fast computation speed. The predicted time-domain waveforms and power spectral densities (PSDs) agree well with the actual vehicle responses. The main features of the lateral vehicle responses can also be captured by the proposed method, yet the results are not as good as the vertical ones.

Effect of clay fouling on track drainage capacity of railway Ballast: Experimental and numerical study

Ballast fouling is a key contributor to the instability of ballasted railway tracks due to its detrimental influence on the permeability of the ballast layer. Predicting the track drainage condition in a ballasted track segment frequently affected by fouling is vital for planning track maintenance activities. However, only limited studies have been conducted to assess the hydraulic conductivity and track drainage capacity of fouled railway ballast. Hence, full-scale permeability tests were performed in this study using an in-house custom-built large-scale permeability apparatus at the laboratory to attain hydraulic conductivities for 5 different fouling levels of ballast fouled with sandy lean clay material. Based on the experimental results, a relationship between the Void Contamination Index (VCI) and hydraulic conductivity was obtained. The results elucidated that the fouling material reduced the hydraulic conductivity of ballast by more than 90% when VCI was 25% or higher. In addition, using the hydraulic conductivity values obtained from experimental results, a detailed 2D numerical seepage analysis of a rail track was carried out to represent the track drainage capacity at various fouling levels. Based on the results, the significance of maintaining a clean top ballast layer and shoulder ballast was highlighted and a criterion for estimating the drainage capacity of a clay-fouled railway track was proposed to initiate track maintenance operations.

Correlation Analysis between Rail Track Geometry and Car-Body Vibration Based on Fractal Theory

The effect of track geometry on vehicle vibration is a major concern in high-speed rail (HSR) operation from the perspectives of ride comfort and safety. However, how to quantitatively characterize the relation between them remains a problem to be solved in track quality assessment. By using fractal analysis, this paper studies the detailed correlation between track surface and alignment irregularities and car body vertical and lateral acceleration in various wavelength ranges. The time-frequency features of the track irregularity and car-body acceleration are first analyzed based on empirical mode decomposition (EMD). Then, the fractal features of the inspection data are determined by calculating the Hurst exponent of their intrinsic mode functions (IMFs). Finally, the fractal dimensions of the track irregularity and car-body acceleration are obtained, and the correlation between their fractal dimensions with respect to different IMFs is revealed using regression analysis. The results show that the fractal dimension is only related to the roughness of the IMF waveforms of the track irregularity and car-body vibration and is irrelevant to the amplitude of the time series of the data; the correlation coefficient of the fractal dimension of the track irregularity and car-body acceleration is greater than 0.7 for wavelengths greater than 30 m, indicating that the relationship between track irregularity and car-body vibration acceleration is more obvious for long wavelengths. The findings of this research could be used for optimizing HSR track maintenance work from the viewpoint of the ride quality of high-speed trains.

Review of Data Analytics for Condition Monitoring of Railway Track Geometry

Railway track geometry varies along routes depending on topographical, operational and safety constraints. Tracks are prone to degrade over time due to various factors, with deviations from the original geometry design having potential implications for comfort and safety. Regular inspections are carried out to evaluate track condition and determine whether maintenance interventions should be undertaken to correct track geometry. The dynamic measurement of track geometry parameters generates large volumes of data that must be analysed to evaluate track degradation. This work comprehensively explains how track quality is evaluated, introducing four main categories of factors affecting it. These are track design, loading, environment and maintenance. The most common techniques applied to evaluate track condition and predict degradation and faults, categorised into statistical, Machine Learning, Big Data and other, are also introduced. Specifically, the influence of each factor on track geometry is stated and the common techniques applied to each factor determined from this review. The utility of loading and maintenance data for fault prediction depend on the availability of records, whilst the impact of environmental conditions is expected to become increasingly important due to climate change. Artificial Neural Networks, Bayesian models and regression are the most applied techniques for determining track degradation behaviour and fault prediction, considering several different factors in their models. Increasingly sophisticated algorithms can consider multiple factors in tandem to predict faults based on the unique conditions of specified tracks.

Study on Vibrational Properties of Vehicle and Track System Caused by Deformation of Tunnel Base

In this paper, the vehicle track tunnel coupling dynamic model is established by using finite element software, and the model is verified. Based on this, the vibration characteristics of the vehicle and track system caused by the settlement of the tunnel foundation are studied. The calculation results show that: (1) the short wave in the tunnel settlement wavelength has a great influence on the dynamic characteristics of vehicles and track structures, and the vibration characteristics of vehicle track system decrease with the increase of wavelength, so it is suggested to control the wavelength above 25 m. (2) The dynamic index of the vehicle and track increases significantly with the increase of amplitude, which affects the driving stability and track operation. Through calculation, it is recommended to control the amplitude below 20 mm. (3) The influence of tunnel base settlement on driving is related to speed. The greater the train speed is, the greater the influence on driving will be. So the train speed should be controlled. (4) Higher running speed will endanger the safety of train operation and the stability of the track structure, so the train running speed should be lower than 200 km/h. The above research provides some theoretical guidance for track maintenance and operation.

The Typical Damage Form and Mechanism of a Railway Prestressed Concrete Sleeper.

Prestressed concrete sleepers are an important track component that is widely used in railway ballast track. Prestressed concrete sleepers have high strength, strong stability, and good durability; thus, their operation and use in railways are beneficial. However, in different countries and regions, certain damage to sleepers typically appears. Existing research on concrete sleepers focuses primarily on the structural design method, the application of new materials, theoretical analysis, and bearing strength test research, while ignoring sleeper damage. There are a few sleeper damage studies, but they look at only one type of damage; thus, there is no comprehensive study of prestressed concrete sleeper damage. The damage forms of prestressed concrete sleeper damage are thus summarized in this study, and the theory of the causes of prestressed concrete sleepers is analyzed based on the limit state method for the first time. The findings indicate that sleeper structure design is the primary cause of its operation and use status, and that special measures should be considered based on sleeper use conditions. In addition to meeting design requirements, materials, curing systems, product inspection, and other factors must be considered during manufacturing to improve the sleepers' long-term performance. Keeping the track in good condition, including but not limited to the state of fasteners, ballast bed, and track geometry is also an important aspect of preventing sleeper damage. The outcomes of this study provide better insights into the influences of damage to railway prestressed concrete sleepers and can be used to improve track maintenance and inspection criteria.

An Innovative multi-sensor data fusion method based on thrice deeply-fusion architecture for multi-object tracking

For the purpose of track maintenance in a cluttered environment with some false alarms, false dismissals and measurement disturbances, this paper presents a new multi-object tracking procedure with uncertainty of the source of the measurement data returned from multi-sensor. The tracking method is a thrice deeply-fusion approach constructed by a primary fusion based on an improved probabilistic data association filter (IPDAF), a secondary fusion with historical motion trajectories, and a thrice fusion with road-markings. It incorporates the existence probabilities of the individual tracks and the variable number of objects based on the Bayesian estimation theory, which can improve the tracking performances effectively in an environment with high clutter density. A binary 2-D assignment is adopted for the optimal data association, which is established as a nonlinear optimization problem. In the motion modeling, it introduces multiple measurement models for different sensors into the method. Then, the estimation could be performed with greater reliability. The computational efficiency is satisfying and it can be used for real-time application, which is verified by two real test scenarios.