Safety Of Railway Transportation Research Articles

Electromagnetic Impact of Overhead High-Voltage Lines during Power Transmission on Buried Signaling Cables of the Traffic Control Systems in Modernized Railway Lines

The railway transport system is a key factor supporting industrialization in all aspects of human activity. However, in order not to lose its importance, it must meet the challenge of modern civilization. The safety, reliability, and efficiency of railway transport, to a large degree, depend on using highly integrated electronics, which are very sensitive to various disturbances generated in the electric traction system and train or coming from the environment. One of the sources of electromagnetic disturbances are high-voltage (HV) power lines running close to the railway infrastructure. The purpose was to assess the electromagnetic impact of overhead HV transmission lines on buried signaling cables of the railway traffic control system crossbreeding with them. The levels of voltage induced in the cable under steady state and the earth fault in the HV line at various soil resistivity were estimated. A software tool based on a hybrid numerical method that combines circuit theory and electromagnetic field theory was used for computations. It was found that very high voltages may be induced in the signaling cables during earth faults in the HV lines, which may lead to serious interference or damage to the equipment. The results provide useful knowledge for implementing modern railway traffic control systems and protection measures.

Railway accident causation prediction with improved transformer model based on lexical information and contextual relationships

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents.

Clustering-Based Classification of Polygonal Wheels in a Railway Freight Vehicle Using a Wayside System

Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors.

High-frequency Resonance Suppression of Railway Traction Power Supply System Based on the Combination of Single-tuned and C-type Filters

Background:: Railroad transportation in the actual operation process, there are also many dangerous accidents caused by resonance, which greatly affect the safety of railroad transportation. A comprehensive examination of the operational dynamics within the power branch of a traction substation is imperative for sustaining system equilibrium. Discrepancies between these facets pose a potential threat to the safety of railway transport. Thus, a meticulous analysis of highfrequency resonance characteristics and the formulation of effective suppression techniques become paramount. Objective:: Harmonics from grid-side locomotive traction braking are scrutinized under different operational scenarios and different train runs. The aim is to develop an effective harmonic management strategy to normalize the THD and thus maintain the traction power supply system to become more stable. Methods:: This study investigates the high-frequency harmonic resonance characteristics using a control variable approach. Harmonics and negative sequences generated during locomotive traction braking under different operating conditions are investigated by means of extensive analysis. These challenges require the implementation of a harmonic management method. This method uses a combination of two monotonic filters and a C filter in parallel. This method applies to different operating conditions and can dynamically adapt harmonics to changes in the number of trains, which is related to the actual dynamics of the traction power system closely. Results:: A comparative evaluation of seven operating conditions shows that the filter in this patent exceeds the efficiency of existing methods. The filter reduces the fluctuations during spectral changes, and the voltage distortion rate decreases from the previous 2.33% to 0.55%, making it more adaptable and promising effective harmonic management in high voltage and high current situations. Conclusion:: Through multiple simulation tests, a synergistic configuration involving the C-type filter and two single-tuned parallel connections under regenerative braking conditions emerges. This refined, patented filter design not only mitigates the impact of negative sequence during the filtration process but also substantially diminishes high harmonics and THD.

Determining Smart Phone Sensing and K-Means Clustering for Accurate and Timely Railway Track Joint Fault Diagnosis

The railway track joint is an important component that connects two sections of the rail and ensures a smooth and safe operation of trains. However, the joint is also a critical point of failure that can lead to train derailments and accidents. Therefore, accurate and timely detection of joint faults is crucial for ensuring the safety and reliability of railway transportation. In this paper, we propose a novel approach for railway track joint fault diagnosis using smart phone sensing and k-means clustering. Our approach utilizes the accelerometer sensor of a smart phone to measure the vibrations and movements of a specifically developed railway shoe stick that is employed on an actual railway track for the condition monitoring of the railway tracks. More than 60000 data values are collected and are then processed and analysed using k-means clustering, a popular unsupervised machine learning technique that groups similar data points together. The K means clustering in this study forms 3 clusters as a result. The 3 clusters after being validated on the track by virtue of visual inspection are determined to be acceleration values of the healthy track, track with higher joint gap than the standardized value and super-elevated railway track joint fault(s), respectively. In addition to its high accuracy and efficiency, our approach has several advantages over traditional methods, such as low cost, easy deployment, and high scalability. Moreover, the smart phone sensing technology can be easily integrated with existing train monitoring systems, making it a useful tool for real-time joint fault diagnosis and maintenance. Overall, this study demonstrates the potential of smart phone sensing and k-means clustering for railway track joint fault diagnosis and highlights the need for further research in this field.

Image segmentation method of rail head defects and area measurement of selected segments

The operation safety of railway transport, which is the most important economic and social factor, is largely determined by the technical condition of the rail track and measures to maintain the quality of its track management system. One of the system elements for ensuring the accident-free operation of the track is the technical diagnosis of rails using a method complex of non-destructive control of rails, such as acoustic (ultrasonic), magnetic, combined, etc., and monitoring of the track using methods of measuring the geometry of the rail track and its disturbances. When the wheel interacts with the rail, especially on high-speed and load-stressed sections, defects and damage inevitably occur in the rails. A rather large share of such defects are on the rolling surface of the rail head. Formed defects develop rapidly, which seriously complicates the safety of train traffic. Therefore, accurate and quick detection of defects on the rolling surface of the rail head is very important. However, it is quite difficult to detect defects on the rolling surface of the rail by the acoustic (ultrasound) method due to the violation of tight contact between the rolling surface of the rail head and the piezoelectric transducer. In this case, it is quite convenient to detect surface defects of the rail head using video control. The article provides a comparative analysis of segmentation methods. There has been presented the method of image segmentation of main rail defects based on general contour preparation and parallel-hierarchical (PH) transformation using their classification. The parallel-hierarchical transformation method allows to increase the segmentation accuracy of individual areas in the original image compared to similar ones. The algorithm of pyramidal generalized-contour preparation and the criterion system allows, by calculating the threshold for each level of the gray scale, to present the study of the image with the corresponding contour preparations at the segmentation level. Modeling of recursive generalized-contour preparation and PH transformation method for image segmentation problem of rail head defects shows that, compared to the segmentation method based on the increase of areas, the accuracy of image segmentation is better. A modified method of calculating the image contour area based on the coding of lines forming the boundaries of the black and white areas of the two-gradation image has been given.

Enhancing Railway Transportation Safety with Proactive Maintenance Strategies Incorporating Machine Learning

Enhancing Railway Transportation Safety with Proactive Maintenance Strategies Incorporating Machine Learning

APPLICATION OF VIBRATION DIAGNOSTICS OF AXLE BOXES OF FREIGHT CARS TO REDUCE THE RISK OF FAILURES

The research presented in this study advocates for the adoption of vibration diagnostics as a method to mitigate the risk of axle box failures in freight cars, emphasizing its significant impact on enhancing the safety of railway transportation. The authors assert that employing vibration diagnostics for axle boxes in freight cars results in a noteworthy 2.5-fold reduction in risks within the realm of railway transport.The practical implications of the developed diagnostic method extend to its capacity for assessing the safety of railway transport, offering a valuable tool for risk evaluation. Experimental studies conducted as part of this research further validate the effectiveness of the method, revealing a substantial decrease in the risk of axle box failures by a factor ranging from 1.125 to an impressive times. This empirical evidence solidifies the credibility and applicability of vibration diagnostics in averting potential failures in axle boxes.From an economic standpoint, the implementation of vibration diagnostics for axle box units demonstrates a tangible impact, with cost savings ranging from 13 to 44 thousand UAH corresponding to the operational mileage of a freight car. This underscores not only the safety benefits but also the economic advantages associated with the utilization of vibration diagnostics in the maintenance and operation of railway transport.The findings of this research contribute to a more comprehensive understanding of the capabilities of vibration diagnostics as a powerful and effective tool in ensuring the safety of railway transportation. By highlighting the practical applicability of the method and supporting its success through empirical data, the authors advocate for its widespread adoption as a means to bolster the safety and efficiency of freight transportation by rail. In essence, this study provides a robust foundation for the integration of vibration diagnostics into routine maintenance practices, offering a proactive approach to minimizing risks and optimizing the performance of axle box units in freight cars.

Fatigue fracture cross-sections after cyclic tests with a combination of cyclic bending and torsion of samples made of aluminum alloy 6060

The paper presents the results of fatigue tests of the 6060 aluminum alloy. The test material was taken from the profiles used for production of side windows and external doors of the passenger trains by the RAWAG company. The tests were carried out for cyclic loads with pure bending, pure torsion, and two combinations of bending and torsion. Fatigue tests were performed at zero mean values. Using scanning electron microscopy, a fractographic analysis was made, which is a supplementary basis for considerations about the mechanism of initiation and development of fatigue cracks. Based on the appearance of individual zones and the characteristics of cracks, a picture of the behavior of the material under specific conditions was obtained. Finally, the plastic property of fatigue cracks was indicated. The work demonstrates the influence of aluminum alloy processing on the fatigue properties of this alloy during operation in order to obtain maximum reliability and safety of railway transport.

Symmetry-Driven Unsupervised Abnormal Object Detection for Railway Inspection

Vision-based abnormal object detection in railway track inspection images is one of the critical tasks to ensure the safety of railway transportation. Even though many machine learning based methods have been developed, these approaches heavily rely on anomaly supervisions and therefore can not detect unknown anomaly classes. In order to tackle the problem, this paper proposes a novel unsupervised method to detect abnormal objects, which does not require abnormal object training data. Specially, we find that a railway track image is almost symmetrical about the track central line, i.e., normal objects appear repeatedly and symmetrically, while abnormal ones are rare and also significantly different from the corresponding symmetric areas. Motivated by this observation, we propose to train a metric-learning based deep model to learn the similarity between normal objects and the corresponding symmetrical areas. Then for each object proposal in one test image, we measure the distance between the proposal and the corresponding symmetrical regions, and determine whether it is an abnormal object based on the symmetrical metric. Extensive experiments on our collected dataset show that our proposed method achieves competitive performance compared with the state-of-the-art methods.

Повышение качества транспортных услуг на основе развития железнодорожной инфраструктуры Сибирского федерального округа

The article describes the degree of influence of the level of transport infrastructure development on the quality of transportation services provided to the population. It analyzes the state of infrastructure in the Siberian Fed-eral District for all types of transport, and considers a risk-oriented approach to traffic safety in railway transport as an internal control of service quality indicators. According to the results of the factor analysis of train traffic safety risks of the activity of the West Siberian Engineering Structures Division, the problematic risk factors af-fecting the quality level of the indicator “Reliability” are determined. The integral assessment of the quality of services provided for the use of transport infrastructure and the significance of risk factors in relation to the achievement of the reference state of the quality criterion “Safety” are determined. Long-term corrective measures of transport infrastructure development aimed at improving the quality of services in general are examined.

An Overview of Current Challenges and Emerging Technologies to Facilitate Increased Energy Efficiency, Safety, and Sustainability of Railway Transport

This article presents a review of cutting-edge technologies poised to shape the future of railway transportation systems, focusing on enhancing their intelligence, safety, and environmental sustainability. It illustrates key aspects of the energy-transport-information/communication system nexus as a framework for future railway systems development. Initially, we provide a review of the existing challenges within the realm of railway transportation. Subsequently, we delve into the realm of emerging propulsion technologies, which are pivotal for ensuring the sustainability of transportation. These include innovative solutions such as alternative fuel-based systems, hydrogen fuel cells, and energy storage technologies geared towards harnessing kinetic energy and facilitating power transfer. In the following section, we turn our attention to emerging information and telecommunication systems, including Long-Term Evolution (LTE) and fifth generation New Radio (5G NR) networks tailored for railway applications. Additionally, we delve into the integral role played by the Industrial Internet of Things (Industrial IoT) in this evolving landscape. Concluding our analysis, we examine the integration of information and communication technologies and remote sensor networks within the context of Industry 4.0. This leveraging of information pertaining to transportation infrastructure promises to bolster energy efficiency, safety, and resilience in the transportation ecosystem. Furthermore, we examine the significance of the smart grid in the realm of railway transport, along with the indispensable resources required to bring forth the vision of energy-smart railways.

Is the safety index of modern safety integrity level(SIL) truly appropriate for the railway?

Safety is paramount in rail transportation. The International Safety Standard IEC 61508, issued by the International Electrotechnical Commission (IEC) in 2000, describes the modern concept of safety and the specifications for safety evaluation in a systematic manner. On this basis, the EN 50129 railway transportation safety standard and safety integrity level(SIL) are established. Through the above analysis, the relationship between safety-related system reliability, acceptable risk, and safety index is summarized. It is clear that the safety index is based on acceptable risks. Nonetheless, as the economy and society advance, the safety index mandated by the SIL falls behind. Consequently, the principles and methodologies for calculating the safety index are presented. The paper proposes a reference for the Chinese railway safety index based on actual data from the Chinese railways over the past few years.

Visual Fault Detection of Multiscale Key Components in Freight Trains

Fault detection for key components in the braking system of freight trains is critical for ensuring railway transportation safety. Despite the frequently employed methods based on deep learning, these fault detectors are extremely reliant on hardware resources and complex to implement. In addition, no train fault detectors consider the drop in accuracy induced by scale variation of fault parts. This paper proposes a lightweight anchor-free framework to solve the above problems. Specifically, to reduce the amount of computation and model size, we introduce a lightweight backbone and adopt an anchor-free method for localization and regression. To improve detection accuracy for multi-scale parts, we design a feature pyramid network to generate rectangular layers of different sizes to map parts with similar aspect ratios. Experiments on four fault datasets show that our framework achieves 98.44% accuracy while the model size is only 22.5 MB, outperforming state-of-the-art detectors.

Mechanism and Control Scheme of Central Defects in Cross Wedge Rolling of Railway Vehicle Axles

Faced with a great demand for railway axles, the cross wedge rolling (CWR) process has the advantages of high efficiency and material saving, and good forming quality of axles is significant for railway transportation safety. The stress inside the railway axle of CWR was analyzed by the finite element method. It was found that the center of the rolled piece is subjected to tensile stress in transverse and axial directions and compressive stress in radial direction, making it more prone to defects. By simulating the evolution of micro voids in the center of the CWR piece, it was found that the presence of voids makes the strain around them significantly large and concentrated and the material between the voids deforms intensely. When voids expand relative to the rolled piece and the internal necking between voids is significant, void coalescence is easy to occur, and central defects are formed. The influence of process parameters on void evolution was analyzed. The scheme of detaching die was proposed to avoid central defects of the CWR piece and the optimal parameter conditions of CWR of railway axles were determined, which proved that the quality of railway axles formed with optimized parameters meets the technical requirements of railway vehicle axles.

On Certain Areas of Improvement of Control and Supervisory Activities in the field of Ensuring Transport Safety in Railway Transport

Various types of control and supervisory measures provide the anti-terrorist protection of transport (railways included) from potential threats. An important role is assigned to the federal state control (supervision) in the field of transport security, despite the fact that Federal Law No. 17‑FZ of 10.01.2003 «On Railway Transport in the Russian Federation» and the Charter of Railway Transport do not mention this type of transport supervision. The analysis of the subject, objects, participants and the content of administrative and legal relations arising in the implementation of transport supervision on railway transport allows us to attribute this activity to the control and supervisory and identify a number of its features. The peculiarity of risk management to ensure the anti-terrorist security of railways is the attribution of certain types of activities of controlled persons to one category of severity and risk probability group. This provision makes it possible to plan and carry out uniform preventive measures against one controlled person both within the framework of federal state control (supervision) in the field of transport security and within the framework of federal state control (supervision) in the field of railway transport. Amendments have been proposed to the Federal Law «On Railway Transport in the Russian Federation» in terms of supplementing it with norms on transport security and to the Regulation on Federal State Control (Supervision) in the field of Transport Security, approved by Decree of the Government of the Russian Federation No. 1051 of 06/29/2021. The main directions for transport supervision improvement include: carrying out control measures aimed at identifying potential threats to the automated transport security system; expanding the list of situations that can be simulated during the experiment; using a permanent raid as a special mode of transport supervision on the railway; conducting an inspection visit with respect to vehicle owners; developing an information resource for conducting self-examination as a preventive measure.

Development of DMPS-EMAT for Long-Distance Monitoring of Broken Rail

The safety of railway transportation is crucial to social and economic development. Therefore, real-time monitoring of the rail is particularly necessary. The current track circuit structure is complex and costly, posing challenges to monitoring broken tracks using alternative methods. As a non-contact detection technology with a lower environmental impact, electromagnetic ultrasonic transducers (EMATs) have become a concern. However, traditional EMATs have problems such as low conversion efficiency and complex modes, which can limit their effectiveness for long-distance monitoring. Therefore, this study introduces a novel dual-magnet phase-stacked EMAT (DMPS-EMAT) design comprising two magnets and a dual-layer winding coil arrangement. The magnets are positioned at a distance equal to the wavelength of the A0 wave from each other, while the center distance between the two sets of coils beneath the transducer is also equal to the wavelength. After analyzing the dispersion curves of the rail waist, it was determined that the optimal frequency for long-distance rail monitoring is 35 kHz. At this frequency, adjusting the relative positions of the two magnets and the coil directly underneath to be one A0 wavelength can effectively excite a constructive interference A0 wave in the rail waist. The simulation and experimental results show that DMPS-EMAT excited a single-mode A0 wave, resulting in a 1.35-times increase in amplitude.

INJURIES IN RAILWAY TRANSPORT AND WAYS OF REDUCING THE INDUSTRIAL DANGERS IMPACT

The article deals with the problem of the labour protection state in railway transport. The transport sector, in particular the railway, is one of the main country's activity spheres, which significantly affects the level of its social and economic development. The analysis of statistical data shows that the transport industry is one of the leaders in the most trauma-hazardous human activity spheres. Thus, the analysis of injury rates and factors determining the professional reliability of locomotive crews, and the scientific substantiation of measures set aimed at preserving the health of railway transport workers and ensuring the safety of railway transport, are urgent and essential tasks. The article's purpose is a comprehensive analysis of the injury's actual state in railway transport. In the work, it is established that during the performance of their duties, railway transport employees are affected by physical, chemical and biological harmful production factors. The work is carried out under the influence of psychophysiological factors caused by the severity and intensity of the labour process in conditions of hydrodynamic and fixed working posture. It was determined that the influence of adverse factors complex leads to disruption of the cardiovascular, central nervous and endocrine systems activity. The authors assessed the actual state of industrial and non-industrial injuries in railway transport. It has been established that the number of industrial accident victims has been gradually increasing in recent years. This indicates the need to develop and implement in practice a set of measures to improve the working conditions of mainline locomotive crews. It is noted that the key areas of measures to preserve the health and professional capacity of railway transport workers are the improvement of regulatory and legal support, socio-economic motivation of workers, and organizational-technological and sanitary-medical measures. Keywords: injury, accident, railway transport, labour protection.

БЕЗОПАСНАЯ ЭКСПЛУАТАЦИЯ ВАГОНОВ ПРИ НАЛИЧИИ НЕИСПРАВНОСТЕЙ

A study of various types of malfunctions in railway transport is conducted. Their critical impact on the safety of operation is evaluated. The research is aimed at studying statistical data on appearing failures and malfunctions both on rolling stock and on railway tracks. At high loads on mechanical components, the probability of various defects is high, which requires the most thorough study of the reliability of structural elements. Any deviation can lead to significant breakdowns and cause huge damage, especially for rolling stock for passenger transportation or transportation of dangerous goods. It is also necessary to predict the indicators of reliability and stability of the system to various impacts. In this regard, the study is aimed at evaluating the failures of railway systems and their critical impact on traffic safety. Despite such a great interest in the problems of evaluating and ensuring the safety of railway transport, there is still no unambiguous evaluation of the parameters of railway carriages and the track for the maximum stability coefficient, if exceeded, the train is highly likely to derail.

Blockchain enabled zero trust based authentication scheme for railway communication networks

With the introduction of emerging technologies such as cloud computing, the railway communication network has the characteristics of complex structure and blurred boundaries, which leads to a series of security threats including information leakage and malicious access. Specifically, the third-party cloud services are difficult to be supervised, and network traffic is untrustworthy. To ensure system security, we propose a zero-trust security model in this paper. Then, we introduce blockchain and Merkle tree to build a distributed identity storage scheme for guaranteeing reliable, confidential and efficient data updates, and improving authentication efficiency. Furthermore, the proxy was introduced for two-way authentication with cloud servers, so that internal and external threats could be counteracted. Moreover, reputation assessment mechanism has been adopted to reduce the possibility of nodes accessing malicious cloud services. Performance analysis demonstrated that the proposed security model is able to enhance the security, efficiency and stability of the system, and consequently can guarantee the safety and reliability of railway transportation.