A framework for dynamic modelling of railway track switches considering the switch blades,actuators and control systems

The main contribution of this paper is the development and demonstration of a novel methodology that can be followed to develop a simulation twin of a railway track switch system to test the functionality in a digital environment.This is important because,globally,railway track switches are used to allow trains to change routes;they are a key part of all railway networks.However,because track switches are single points of failure and safety-critical,their inability to operate correctly can cause significant delays and concomitant costs.In order to better understand the dynamic behaviour of switches during operation,this paper has developed a full simulation twin of a complete track switch system.The approach fuses finite element for the rail bending and motion,with physics-based models of the electromechanical actuator system and the control system.Hence,it provides researchers and engineers the opportunity to explore and understand the design space around the dynamic operation of new switches and switch machines before they are built.This is useful for looking at the modification or monitoring of existing switches,and it becomes even more important when new switch concepts are being considered and evaluated.The simulation is capable of running in real time or faster meaning designs can be iterated and checked interactively.The paper describes the modelling approach,demonstrates the methodology by developing the system model for a novel“REPOINT”switch system,and evaluates the system level performance against the dynamic performance requirements for the switch.In the context of that case study,it is found that the proposed new actuation system as designed can meet(and exceed)the system performance requirements,and that the fault tolerance built into the actuation ensures continued operation after a single actuator failure.

Advancements and Applications of Polyurethane Curing Technology in Railway Track Bed Construction in China

This paper examines the application of polyurethane curing technology in the construction of railway track beds,with a specific focus on its implementation in China’s rapidly developing railway infrastructure.The study begins by identifying the limitations of traditional ballasted track beds,especially under the demands of high-speed and heavyload railways.It then methodically analyzes the advantages of polyurethane-cured track beds,highlighting their improved mechanical properties,including enhanced stability and durability.The paper further explores the benefits of transitioning to prefabricated polyurethane track beds,emphasizing significant cost reductions,better construction quality,and enhanced maintainability.Through a detailed review of experimental data and practical applications,the paper demonstrates the efficacy of polyurethane track beds in various railway settings.A critical part of the research involves optimizing the structural parameters of polyurethane track beds to achieve the best balance of mechanical and damping properties.The conclusion of the paper underscores the potential of polyurethane curing technology as a transformative approach to railway track bed construction,offering a solution to the challenges posed by traditional methods and aligning with the evolving needs of modern railways.

Embedded System Development for Detection of Railway Track Surface Deformation Using Contour Feature Algorithm

Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition monitoring system is essential to avoid accidents and heavy losses.Generally,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment.Therefore,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway track.The proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition process.The contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy regions.It works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black color.We have used the multiprocessing technique to overcome the massive processing and memory issues.This embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/h.The functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faults.An unhealthy frame’s onsite detection processing time was recorded at approximately 25.6ms.The proposed system can synchronize the acceleration data on specific railway track deformation.The proposed novel embedded system may be beneficial for detecting faults to overcome the conventional manual railway track condition monitoring,which is still being practiced in various developing or underdeveloped countries.

Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images

There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.

The influence of reflections from the train body and the ground on the sound radiation from a railway rail

Purpose – The vibration of the rails is a significant source of railway rolling noise, often forming the dominantcomponent of noise in the important frequency region between 400 and 2000 Hz. The purpose of the paper is toinvestigate the influence of the ground profile and the presence of the train body on the sound radiation fromthe rail.Design/methodology/approach – Two-dimensional boundary element calculations are used, in which therail vibration is the source. The ground profile and various different shapes of train body are introduced in themodel, and results are observed in terms of sound power and sound pressure. Comparisons are also made withvibro-acoustic measurements performed with and without a train present.Findings – The sound radiated by the rail in the absence of the train body is strongly attenuated by shieldingdue to the ballast shoulder. When the train body is present, the sound from the vertical rail motion is reflectedback down toward the track where it is partly absorbed by the ballast. Nevertheless, the sound pressure at thetrackside is increased by typically 0–5 dB. For the lateral vibration of the rail, the effects are much smaller. Oncethe sound power is known, the sound pressure with the train present can be approximated reasonably well withsimple line source directivities.Originality/value – Numerical models used to predict the sound radiation from railway rails have generallyneglected the influence of the ground profile and reflections from the underside of the train body on the soundpower and directivity of the rail. These effects are studied in a systematic way including comparisons with measurements.

Assessment of Ballast Flying in the National Railway Network of Ethiopia

One of the major problems in ballasted railroads is ballast flying, which is the projection of ballast particles from the at-rest position as the train passes over the track of a railway structure, mainly due to high speed. In this research, the possibility of railway ballast flying for the double track Addis-Adama section of the new Addis-Djibouti railway line is assessed by determining the major causes of ballast flying and applying Discrete Element Modeling (DEM) with the aid of Particle Flow Code (PFC3D) software. The analysis comprised of an impact load and ballast material behavior which were used to determine the vibrational speed of individual ballast particles. The governing result from the series of discrete element analyses performed by considering fouled ballast gradation with grain-size diameter of 22.4 mm gives rise to a ballast maximum vibrational speed of 0.014 m/s. Since the ballast vibrational speed for Addis Ababa-Adama line is less than 0.02 m/s that is recommended by the literature, no ballast flight is expected under the present traffic and ballast conditions.

Semantic segmentation of track image based on deep neural network

In this paper,deep learning technology was utilited to solve the railway track recognition in intrusion detection problem.The railway track recognition can be viewed as semantic segmentation task which extends image processing to pixel level prediction.An encoder-decoder architecture DeepLabv3+model was applied in this work due to its good performance in semantic segmentation task.Since images of the railway track collected from the video surveillance of the train cab were used as experiment dataset in this work,the following improvements were made to the model.The first aspect deals with over-fitting problem due to the limited amount of training data.Data augmentation and transfer learning are applied consequently to rich the diversity of data and enhance model robustness during the training process.Besides,different gradient descent methods are compared to obtain the optimal optimizer for training model parameters.The third problem relates to data sample imbalance,cross entropy(CE)loss is replaced by focal loss(FL)to address the issue of serious imbalance between positive and negative sample.Effectiveness of the improved DeepLabv3+model with above solutions is demonstrated by experiment results with different system parameters.