Safety Of Railway Systems Research Articles

A Study on the Derivation of Risk Factors in Railway Site for the Improvement of Artificial Intelligence(AI)-Railway Safety System

A Study on the Derivation of Risk Factors in Railway Site for the Improvement of Artificial Intelligence(AI)-Railway Safety System

Adaptive large-neighbourhood search for optimisation in answer-set programming

Answer-set programming (ASP) is a prominent approach to declarative problem solving that is increasingly used to tackle challenging optimisation problems. We present an approach to leverage ASP optimisation by using large-neighbourhood search (LNS), which is a meta-heuristic where parts of a solution are iteratively destroyed and reconstructed in an attempt to improve an overall objective. In our LNS framework, neighbourhoods can be specified either declaratively as part of the ASP encoding or automatically generated by code. Furthermore, our framework is self-adaptive, i.e., it also incorporates portfolios for the LNS operators along with selection strategies to adjust search parameters on the fly. The implementation of our framework, the system ALASPO, currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon that allow for difference and full integer constraints, respectively. It utilises multi-shot solving to efficiently realise the LNS loop and in this way avoids program regrounding. We describe our LNS framework for ASP as well as its implementation, discuss methodological aspects, and demonstrate the effectiveness of the adaptive LNS approach for ASP on different optimisation benchmarks, some of which are notoriously difficult, as well as real-world applications for shift planning, configuration of railway-safety systems, parallel machine scheduling, and test laboratory scheduling.

Dynamic inspection data-based analysis of rail base metal irregularities for engineering failure prevention

With the continual increase in train speeds, the requirement for track smoothness has become increasingly critical. Within the railway system of China, the prevalence of short-wave defects, attributed to the rail base metal irregularity, adversely affects the stability of train movement and constitutes a considerable safety risk. However, research into the rail base metal irregularity remains markedly scant, with a conspicuous absence of effective evaluation and identification methodologies. This deficiency critically restricts the capability to mitigate short-wave disturbances arising from such irregularities, negatively impacting the safety and operational stability of railway systems. Therefore, this paper proposes a method for evaluating and identifying the rail base metal irregularity based on dynamic inspection data. Initially, this paper conducts a comprehensive assessment of rail base metal irregularities by analysing wavelength characteristics, frequency amplitude features, and dynamic responses of vehicles. Subsequently, this paper utilises the Synchrosqueezed Wavelet Transform (SWT) technique, which refines the frequency resolution by compressing frequency components within the frequency domain, eliminating cross-frequency interference, and minimising energy dispersion across scale domains, thus accurately delineating the time–frequency attributes of rail base metal irregularity. Building upon the foundation provided by SWT, this paper introduces an identification method employing the Summed Wavelet Power (SWP) across various frequency bands. This approach evaluates the energy variations of different wavelength components relative to changes in mileage, facilitating the identification of track sections with rail base metal irregularity. The efficacy of the proposed methodology is corroborated through the analysis of actual measurement data. The results show that the SWT and SWP techniques can effectively identify rail base metal irregularity, thereby offering substantial support for enhancing the safety and comfort of railway operations.

WITHDRAWN: Artificial-intelligent-powered safety and efficiency improvement for integrated railway systems

The multi-mode integrated railway system, anchored by the high-speed railway, caters to the diverse travel requirement both within and between cities, offering safe, comfortable, punctual, and eco-friendly transportation services. With the expansion of the railway networks, enhancing the efficiency and safety of the comprehensive system has become a cricual issue in the advanced development of railway transportation. In light of the prevailing application of artificial intelligence technologies within railway systems, this study leverages large model technology characterized by robust learning capabilities, efficient associative abilities, and linkage analysis analysis, to propose an AI-powered railway control and dispatching system. This system is elaborately designed with four core functions, including galobal optimum unattended dispatching, synergetic transportation in multiply modes, high-speed automatic control, and precise maintenance decision and execution. The deployment pathway and essential tasks of the system is further delineated, alongside the challenges and obstacles encountered. The AI-powered system promises a significant enhancement in the operational efficiency and safety of the composite railway system, ensuring a more effective alignment between transportation services and passenger demands.

Present Development of Software for Railway Safety

Railway is Cyber-Physical System (CPS), which is distributed over a large territory. It requires secure communication not only among various parts of system, but also with operation center. Building its own communication networks by the railway system operator is financially demanding, which is why more or less open communication systems are used. This is connected with higher requirements for the security of applications, operated in a CPS. European project COSMSOS has been creating a tool that applies DevOps development technologies from the IT field to the field of embedded systems, to which railway system belong. The article shows that this very complex software must be adapted to real requirements, which are put on railway operation system safety.

Railway Track Faults Detection Using Ensemble Deep Transfer Learning Models

Railway track fault detection is an essential task for ensuring the safety and reliability of railway systems, particularly in the summer and rainy seasons when train wheels may slide due to fractures in the track or corrosion may cause track fractures. In this study, we propose a novel approach for the automated detection of railway track faults using deep transfer learning models. The proposed method combines image processing techniques and the training of three pretrained models: InceptionV3, ResNet50V2, and VGG16, on a dataset of railway track images. We evaluated the performance of our proposed method by measuring its accuracy on a test set of railway track images. The individual training accuracies for InceptionV3, ResNet50V2, and VGG16 were 94.30%, 96.79%, and 94.64%, respectively. We then combined these models using an ensemble approach, which achieved an impressive accuracy of 98.57% on the test set. Our results demonstrate the effectiveness of using deep ensemble transfer learning for railway track fault detection. Moreover, our proposed method can be used as a valuable tool for railway track maintenance and monitoring, which can ultimately lead to the improvement of the safety and reliability of railway systems. our proposed approach for railway track fault detection using ensemble deep transfer learning models shows promising results, indicating that it has great potential for detecting track faults accurately and efficiently. The proposed method can be used in various railway systems worldwide, ultimately leading to improved safety and reliability for passengers and cargo transportation.

Concise Historic Overview of Rail Corrugation Studies: From Formation Mechanisms to Detection Methods

Rail corrugation is a serious problem in a railway transportation system, aggravating the operational risk and shortening the lifetime of train–track system. In order to ensure the safety and reliability of the railway system, the detection of rail corrugation is very important. Thus, this study systematically summarizes the recent research progress of rail corrugation. First, this study introduces the definition of rail corrugation and the classification criteria. Then, the formation mechanism of rail corrugation is analyzed in detail, and its adverse consequences are investigated. Further, this study summarizes several main detection methods, which are corrugation-detection methods based on acceleration measurements, wavelet transform methods for corrugation evaluation, computer-vision-based methods for corrugation automatic detection, digital filtering algorithms for rail corrugation detection, and others. In this study, the formation mechanism and detection methods of rail corrugation are systematically described, and various corrugation-detection methods are also introduced in detail. This study not only provides a scientific basis for railway maintenance, but also lays a solid foundation for future experimental design and data analysis. This study can also guide engineering practice to improve the reliability and safety of railway systems. It also provides useful experience for future railway-engineering design and planning, as well as safer and more reliable operation. In general, this study can provide technical support for the detection of rail corrugation to ensure the safety of the rail–track system.

Review Kerusakan Keausan Roda Pada Kendaraan Kereta Api

Damage due to wheel wear on railway trains has a significant impact on railway safety and comfort. This review examines various aspects related to wheel wear damage on trains. The primary focus of this review encompasses three critical areas: railway track, wheel-rail interaction, and the trains themselves. The first section discusses the structure and modeling of railway tracks, while the second section explores various types of interactions between wheels and rails as well as related mathematical models. The third section reviews the types of railway vehicles, their mathematical models, and their stability on straight and curved tracks. Furthermore, this review also examines the influence of wheel wear on the dynamic response of the system. It is hoped that this review will provide valuable insights for practitioners and researchers in improving and enhancing the reliability and safety of railway systems.

Dynamic safety management model for rail traffic control

In rail transport, increasing emphasis has been placed in recent years on improving safety levels. Therefore, more requirements and legal documents require risk analyses to be carried out at various stages of investment implementation. One of the leading legal documents that introduce the obligation to monitor risk is Directive (EU) 2016/798 of the European Parliament and of the Council of 11 May 2016 on railway safety and Commission Implementing Regulation (EU) No 402/2013 of 30 April 2013 on the common safety method for risk evaluation and assessment and re-pealing Regulation (EC) No 352/2009. Additionally, for traffic control systems, the requirements of CENELEC standards are mandatory. These documents present the subject of safety level and show its relation with the safety targets defined in the railway system, including the different ways of measuring them. Methods are also available to analyse the safety level of railway system components in detail, both at the level of individual components, subsystems, and the whole national railway system. However, after conducting an in-depth analysis of the literature, the authors of the article indicate that these methods are not consistent with each other. There is no method defined to present the direct relation of the safety level of the components of the system on the achievement of safety targets for the national railway system. The research and analysis aimed to define an approach, a method that would meet all legal requirements but at the same time would allow to clearly and reliably determine the safety level of the railway system. To define a unified approach, the authors of the article propose to develop a model of a dynamic object - a railway system safety model, which has also been verified on accurate safety data in rail transport in recent years. This model organises the process of safety management on railways and allows to determine values influencing the achievement of safety targets on an assumed level.

Eddy Currents Assessment of Rail Cracks Using Artificial Neural Networks in a Laboratory Setup

Although flaws associated with rolling contact fatigue (RCF) and the corresponding traffic induced damage, which are a cause of failure in railways, have been of great concern in railway system maintenance and safety strategies in many countries for at least two decades, this serious problem has not been yet adequately tackled in the Argentine railway system. The present upgrading activities undertaken in the Argentine railway system (in infrastructure and in rolling stock) are prompting the need for R&D in non-destructive testing techniques and procedures, to satisfy requirements of the new rolling stock and to ensure safe and economic operation of passengers and cargo. RCF damage appears as surface and near surface defects and grows into cracks which in time will propagate along the running surface and through the cross-section. Eddy current testing (ET) is a very efficient in-service inspection method for this task, the near field technique being especially recommended for ferromagnetic components.
 In the present paper, an artificial neural network (ANN) method for automatic classification of flaws with lift-off compensation is presented and tested. The tests consist on the ET evaluation of right angle artificial cracks on a rail calibration coupon; the depth of the cracks studied ranged from 1 to 7 mm. The technique permitted to compensate the weakening of the signals caused by the lift-off effect, allowing signal cracks classification with lift-off variations of up to 5.4 mm.
 The effect of crack skewness on the ET signals is also studied. Because the RCF cracks penetrate the rail at oblique angles, (10° to 30° to the rolling surface), an additional uncertainty component is added to the experiments if calibration is made with a piece having perpendicular cracks. In order to estimate this additional uncertainty on the ANN method presented here, further tests were made with a second calibration piece with cracks at 25° to the surface. Comparison of results showed that the peak to peak amplitudes for both types of cracks are not equivalent at all the tested depths.

VHXLA: A post-earthquake damage prediction method for high-speed railway track-bridge system using VMD and hybrid neural network

The timely and accurate prediction of post-earthquake damage is critically important for ensuring the safety of high-speed railway track-bridge systems. The study introduces a novel method known as the VHXLA model. This innovative model blends variational mode decomposition (VMD) with a hybrid neural network for predicting non-linear multi-component post-earthquake damage in high-speed railways. The VHXLA model comprises a signal processing module and a deep learning module. The signal processing module utilises VMD decomposition, and the Hilbert transform (HT) to transform two-dimensional seismic signals into four-dimensional complex time–frequency signals, thus simplifying the task of identifying seismic time–frequency characteristics via the neural network. The deep learning module integrates diverse types of neural network components, such as the Xception CNN feature extraction submodule, the LSTM RNN temporal learning submodule, and the multi-head attention mechanism submodule. A Bayesian self-optimisation method is implemented to determine the number of decomposition layers in VMD and select essential hyperparameters. The model's effectiveness is evaluated by predicting the damage of an experimentally validated finite element model, subjecting it to seismic loads, and thus gauging its performance. The results indicated that the signal processing module, based on VMD decomposition, significantly improves the neural network's signal processing capability. In addition, the synergistic integration of different modules in the VHXLA model provides superior prediction accuracy compared to pre-existing damage prediction models. Notably, the prediction accuracy is consistent across different positions of the same predicted component in the high-speed railway track-bridge system.

MOBILIZE – Aufrechterhaltung der Betriebssicherheit und Absicherung großer Eisenbahnsysteme

AbstractIn this paper we describe the concept and ongoing work of the research project MOBILIZE, which addresses the operational safety and security of large railway systems to prevent sabotage and vandalism. Examples of such acts are manipulation of system components, intentional placement of objects on the tracks, theft of copper cables or damage to property such as graffiti on parked trains. The prevention of personal injuries resulting from crossing tracks or climbing on parked wagons and thereby getting too close to or even touching the overhead lines is also an important aspect. A permanent installation of video surveillance systems for the entire railway infrastructure is not feasible and, what is more, state-of-the-art video surveillance alone is currently not up to the challenges of monitoring very large areas completely. Therefore, MOBILIZE focuses on the development of a reliable portable system with multi-sensor modalities. In case of increased incidents in a specific region, the system can be deployed quickly and easily. The development of such a system raises questions that represent the main scientific challenges to be explored within MOBILIZE: which combination of sensor technologies is the most suitable to reduce false alarm rates to a minimum in practical operation, legal issues such as the changing regulations regarding the usage of drones, usability for the operator, integration into the operational procedures of the railway operators as well as future economic exploitation of the MOBILIZE project. The current paper focuses on the work done on ground-based visual sensors as well as their fusion with other sensors employed within MOBILIZE, and an assessment of their social impact.

Fault Diagnosis of High-Speed Railway Turnout System Based on Improved Group Decision-Making

Turnout system is very important in the safety of railway system. However, the traditional maintenance method of turnouts is mainly based on human experience and basic intelligent model. Aiming at the problems existing in the current intelligent fault diagnosis method of turnout equipment, this paper uses the improved group decision-making method to dynamically and iteratively adjust and optimize the authority of each decision-making expert in the group decision-making method, so as to improve the prediction ability of the existing model, reduce the number of cases of prediction errors and further improve the safety of the turnout system.

Modeling system dynamics of interacting cruising trains to reduce the impact of power peaks

Rail operators around the globe are striving to improve the efficiency, automation, safety, and sustainability of railway systems. Despite significant advances in technologies such as artificial intelligence and automated train operations (ATO), achieving these goals is challenging for complex rail networks when accounting for unpredictable factors that alter real-time operations. In this paper, we model railway traffic in a corridor as a string of interacting cruising trains, each subject to random speed variations that are described by a stochastic process. We simulate this dynamic system under assumptions that model human drivers and ATO systems, and compute performance measures focusing on energy consumption and the power peaks arising when multiple trains accelerate simultaneously. Different strategies to smooth these peaks are investigated, including the use of regenerative braking energy, potentially coupled with an electric energy storage, and a rule that uses fixed waiting times before re-acceleration. Our findings shed light on when and why these strategies can be effective at reducing energy consumption and/or shaving the peaks. They also show that employing a well-calibrated ATO controller in which vehicles exchange information about their location improves energy performance compared to a model of a human driven. Finally, a trade-off between energy performance and traffic regularity is exposed, i.e., strategies to reduce power peaks may slow rail traffic down, reducing capacity utilization.

All Aboard : Transforming the Tracks of Travel with an Innovative Railway Reservation System

In all nations across the world, the demand for safe, rapid, and dependable train services continues to be a source of worry. Safety, operational inefficiency and reliability of old railway systems and operations, as well as safety and security concerns that haunt many countries for changing rail infrastructure that exist the worldwide train sector is battling to stay afloat. to accommodate the rising freight and passenger demand because of the inefficient utilization of the rail network and Rail assets are being used inefficiently. This is predicted to cause train congestion. executives to design better and more efficient train systems efficiently. Indian Railways' passenger reservation system is one of the most extensive in the world. Approximately one million passengers travel with Indian Airlines every day in reserved accommodations. Another sixteen million passengers use unreserved flights. Railways in India It are a huge job to navigate this large structure. We have investigated several aspects of implementing smart computing in reservation models for railway networks.

Identification of track irregularities with the multi-sensor acceleration measurements of vehicle dynamic responses

Track irregularities induce potential risks to the safety and stability of railway track systems. This paper proposes a novel methodology to identify vertical and lateral track irregularities. The method involves measuring system-based attitude calculation and a model-based unknown input observer estimator, based on the dynamic responses of distributed multi-sensors on the vehicle and bogie. First, a mechanical model of wheel-rail contacts is built with dynamic methods. The model considers the different directions of motion for a railway vehicle and consists of two bogies and four wheelsets. Based on the multi-sensor acceleration measurement, the vertical and lateral acceleration signals of the vehicle and bogies are integrated into the displacement signal. Then a state-space description of the vehicle suspension model is established for inverse dynamical analysis to extract the input signals. A suitable unknown input observer is constructed to estimate the track irregularities by transforming the state space equations of the vehicle into an augmented system that can monitor the track irregularities in-service. This method provides an opportunity to reduce the costs of the monitoring infrastructure and provide quicker and more reliable information about the status of a track.

Introduction to the Special Section on Reliability, Safety, and Security of Railway Systems

Introduction to the Special Section on Reliability, Safety, and Security of Railway Systems

Developing and Extending Status Prediction Models for Railway Tracks Based on On-Board Monitoring Data

Assigning inspection trains to monitor track quality is a standard procedure for maintaining railway system safety. The main challenges lie in lacking time and resources to perform the inspections because of the increasing traffic nowadays. To overcome these challenges, many consider adopting the on-board monitoring (OBM) technique for performing the inspections. This technique assigns commercial trains, instead of traditional track recording vehicles (TRVs), to monitor the track status, allowing railway operators to perform more inspections without affecting the traffic and using expensive inspection trains as well. However, compared with TRV data, the new OBM data are of lower data quality and have fewer features, although they can be recorded more frequently. Therefore, new methods should be developed for effectively applying the new data. This study develops four models, namely the linear regression model, Markov model, ordinary Kriging model, and Kalman filter model, for predicting the track status based on the OBM data. Data collected from the Switzerland railway network are used for verifying the models. Results show that the proposed models can effectively predict the degradation of the track status in different ways and, therefore, assist railway operators in scheduling maintenance tasks.

A Refinement-based Formal Development of Cyber-physical Railway Signalling Systems

For years, formal methods have been successfully applied in the railway domain to formally demonstrate safety of railway systems. Despite that, little has been done in the field of formal methods to address the cyber-physical nature of modern railway signalling systems. In this article, we present an approach for a formal development of cyber-physical railway signalling systems that is based on a refinement-based modelling and proof-based verification. Our approach utilises the Event-B formal specification language together with a hybrid system and communication modelling patterns to developing a generic hybrid railway signalling system model that can be further refined to capture a specific railway signalling system. The main technical contribution of this article is the refinement of the hybrid train Event-B model with other railway signalling sub-systems. The complete model of the cyber-physical railway signalling system was formally proved to ensure a safe rolling stock separation and prevent their derailment. Furthermore, the article demonstrates the advantage of the refinement-based development approach of cyber-physical systems, which enables a problem decomposition and in turn reduction in the verification and modelling effort.

The concept of improving safety on D-grade railway level crossings

The article discusses the safety issue on railway level crossings, specifically emphasizing D-grade crossings, i.e., not equipped with any safety devices. Such crossings pose the most significant challenge to improving railway system safety. The lack of any warning or safety devices causes the safe passage through these crossings dependent only on the car driver his concentration and observation of legal requirements (especially enforcing stopping before the STOP sign). Due to that, it is suggested to introduce devices allowing for automatic identification and registration of violations by drivers. Recorded material will be handed over to the relevant authorities (e.g., Police, General Inspectorate for Road Transport), which will undertake appropriate actions. The article also attempts to assess the influence of such a solution on the number of accidents on given level crossings and their outcome, among others, on improving railway safety.