Train System Research Articles

The tribological behavior of Cu-based brake materials against Cr3C2-NiCr coated steel disks with varying roughness

A high coefficient of friction (COF) and low wear rate are expected in the braking system for high-speed trains. For this purpose, we prepared coated steel disks by spraying Cr3C2-NiCr powders and slid them against Cu-based powder metallurgy pins. Pin-on-disk tests showed a significant increase in the COF with the Cr3C2-NiCr coated disk (roughness of 1.0 µm) reaching 0.514, compared to 0.366 for the uncoated steel disk. Additionally, there was a notable reduction in wear loss of the brake material, with the coated disk losing only 0.0088g, while the uncoated disk lost 0.0760g.The lower adhesion between the coating and the pin material, combined with the abrasive action of the harder coating on the disk surface, helps maintain the friction surface flat and virgin, thus avoiding the formation of thick friction films and fatigue cracks. As a result, a higher and more stable COF with lower wear was achieved when the coated disk slid against the pins.

Investigation of the statistical data on the technical condition of brake equipment components of passenger carriages in operation

In the article, a study was conducted on improving the efficiency of braking systems in railway transport. Based on the information obtained regarding the maintenance of passenger cars, an analysis of brake equipment component failures was performed. Key trends and factors determining brake equipment component failures were identified from the collected statistical data for the reporting period. The main brake equipment components with the highest number of failures were highlighted. It was established that a significant portion of transport incidents due to brake failures in passenger cars occurs during operation, negatively impacting traffic safety. The collected data on the failures of electro-pneumatic distributors in passenger cars were systematized using the specialized software package "STATISTIKA". The research established that seasonal factors influence the total number of failures. A diagnostic system for the braking equipment of passenger trains was proposed to ensure traffic safety. The diagnostic system allows for the construction and analysis of mathematical models that describe the operational state of the braking equipment of passenger rolling stock as a whole. The diagnostic system enhances the reliability of brake operation, monitors air pressure in the brake line, ensures the inter-repair periods of passenger car components, and reduces the time required to detect failures both en route and during train stops. Recommendations for diagnosing the braking system of passenger trains were developed for continuous inspection and timely identification of failures. Their application will enable the prediction of the lifespan of pneumatic and electro-pneumatic system components in passenger cars, ensure the inter-repair operational periods of cars based on their current technical condition, and enhance traffic safety in railway transport.

A proactive opportunistic maintenance decision model based on reliability in train systems

This paper presents an innovative framework for optimizing maintenance strategy in complex electromechanical systems (CEMS), by mitigating over-maintenance and suboptimal resource allocation. This study proposes an optimized maintenance strategy based on opportunistic maintenance with reliability as the core. A topological network model is used to establish a model that forecasts system reliability from function and structure, resulting in the identification of critical components in the system. Subsequently, a maintenance model for mechanical, electrical and information components is proposed and a method for determining maintenance reliability thresholds is suggested. The research imposes constraints in component and system reliability, in constructing an opportunistic maintenance strategies model. The objective of this model was to minimize the maintenance cost. A case study on high-speed railway bogie system supports this strategy, effectively guaranteeing system reliability while reducing maintenance costs. Maintenance resources are allocated reasonably across various stages, thereby reducing wastage of resources.

Using Formal Models, Safety Shields and Certified Control to Validate AI-Based Train Systems

Using Formal Models, Safety Shields and Certified Control to Validate AI-Based Train Systems

Exploring the switch to urban train services: The impact of perceived accessibility and its moderating effects

Perceived accessibility refers to individuals’ subjective perception of how easy it is to reach destinations using a specific mode of transport. As such, understanding the effect of perceived accessibility on travel behavior is essential for creating transport systems and urban environments that are efficient, equitable, and sustainable. This study develops a new integrated model by combining the Norm Activation Model (NAM), the Theory of Planned Behavior (TPB), perceived risk, and perceived accessibility. This enhanced model offers a comprehensive understanding of the factors influencing individuals’ intentions to switch to urban train systems. Additionally, the moderating effects of perceived accessibility on the link between personal, contextual factors and switching intention are also explored. The model developed is tested using data obtained from a sample of 535 individuals in Hanoi, Vietnam. This is particularly important because more research in low- and middle-income countries (LMICs) is needed to address the sustainability challenges these nations face due to their dependency on motorized private transport. The findings show that subjective norms have the strongest effect on the intention, followed by personal norms, attitude, and perceived accessibility. This study also confirms the moderating role of perceived accessibility on the link between attitude and switching intention, while the other hypotheses regarding the moderating effects of perceived accessibility are not supported by the findings. These insights aim to inform policymakers and public transport authorities in developing targeted strategies to promote sustainable urban mobility in Vietnam.

Domestic Accounts of China’s Belt and Road Initiative as an Internally Oriented Soft Power Exercise

China’s Belt and Road Initiative is one of the world’s largest and most significant externally oriented soft power initiatives. Originally envisaged as a series of development projects in countries on historic land and sea trade routes between China and Europe, the initiatives quickly expanded to encompass projects around the globe. The projects, funded using Chinese finances and carried out for the most part by Chinese contractors, provide tangible benefits for recipient countries, such as ports, bridges, train systems, telecommunication systems, medical assistance, and much more. The project outputs provide invaluable soft power benefits for China. Separately, the Chinese government is able to use descriptions of the projects as internally oriented soft power tools that can enhance domestic support for the government and the Communist Party by describing the benefits for China that flow from the Belt and Road projects. This article reviews messaging about the Belt and Road initiative projects posted online for Chinese readers to see how the Chinese government has created internally oriented soft power messages based on its projects abroad.

Effects of Wheel Flats on Vibration and Stress Response of the Wheel-Rail System in High-Speed Trains

With the advancement of high-speed and lightweight vehicles, the occurrence of wheel tread wear, including wheel flats, has escalated. The resulting impact loads on axle-end components and the dynamic response of the axle cannot be overlooked. On-track tests were conducted on wheel flats to obtain wheel-rail forces, axle box vibration accelerations, and axle stresses during service. Time–frequency analysis was employed to uncover patterns of variation with speed. Additionally, a dynamic model of the high-speed train–track system was developed to replicate test conditions, investigating the characteristics of wheel–rail loads and structural elastic vibrations under high-frequency excitation for various flat sizes and higher operating speeds. The findings reveal that under wheel flats, the time-domain response of the wheel–rail system exhibits periodic impacts, while the frequency-domain shows high-frequency energy comprising multiple harmonics of the wheel rotation frequency. Wheel–rail forces, axle box vibration accelerations, and axle stresses exhibit inflection points with increasing speed, particularly within the 120–160 km/h range. In the high-frequency domain, axle stresses display characteristic frequencies that remain unchanged with speed, attributed to the coupled vertical bending vibration modes of the wheel–rail system. These research results offer foundational support for predicting fatigue damage to axle-end components, assessing axle stress, and establishing tolerances for tread defect limits.

Teaching mobility, teaching gender in the ladies’ compartments of Mumbai local trains

The local trains of Mumbai, known for their excessive crowding and cramped conditions, are a popular public transport system due to the vast spread of the network and cheap pricing. These trains are also used extensively by women due to the provision of exclusive segregated ladies’ compartments. However, women still need to negotiate with crowds, and push and jostle as they struggle to board and deboard trains. Through an ethnographic study of women’s experiences of travelling by trains, I examine how women learn to be mobile and teach others, such as their children, to navigate the local train system. Analysing women’s narratives of how they learnt to use trains, I find that teaching mobility and teaching gender go hand-in-hand, as women simultaneously internalise codes of appropriate femininity to protect themselves from physical and sexual harm, but also subvert mainstream femininities, as they embark on their everyday train journeys.

Design of a linear motor-based magnetic levitation train prototype

<span>This study explores the modelling of a magnetic levitation train and its implementation using a microcontroller. Magnetic levitation (maglev) is a technology that enables vehicles to levitate and move without wheels. Maglev research has been conducted globally, but maglev trains haven't received much attention. Due to the sophisticated linear motor technology for contactless transit, building a maglev train requires enormous investments. This paper is crucial for understanding the linear motor technologies necessary for levitation and propulsion. The primary objectives of this study include creating a model of the maglev train using a linear motor circuit, investigating the maglev effect concerning different coil and magnet types, and monitoring the train's propulsion and levitation using a microcontroller. This work constructs a linear motor system for the maglev train, comprising a mechanical structure with a permanent magnet for levitation and electromagnets for propulsion. A microcontroller is employed to sense the magnetic field, produced by the permanent magnet and electromagnets. In summary, this paper successfully designed a maglev train prototype using a linear motor circuit to establish the repulsive mechanism for both levitation and propulsion, with levitation~1 cm from the track and demonstrated the ability to move along a 30 cm track.</span>

Robust Constraint-Following Control for Bio-Inspired Structure Oriented Active Suspension System of High-Speed Trains

Robust Constraint-Following Control for Bio-Inspired Structure Oriented Active Suspension System of High-Speed Trains

Machine Learning based Suggestion Method for Land Suitability Assessment and Production Sustainability

The global population is projected to increase by an additional two billion by 2050, as per the assessment conducted by Food and Agriculture Management. However, the arable land is anticipated to expand by just 5%. Consequently, intelligent and effective agricultural practices are essential to enhancing farming production. Evaluating rural Land Suitability (LS) is a crucial instrument for agricultural growth. Numerous novel methods and concepts are being adopted in agriculture as alternatives for gathering and processing farm data. The swift advancement of wireless Sensor Networks (WSN) has prompted the creation of economical and compact sensor gadgets, with the Internet of Things (IoT) serving as a viable instrument for automation and decision-making in farmers. To evaluate agricultural LS, this study offers an expert system integrating networked sensors with Machine Learning (ML) technologies, including neural networks. The suggested approach would assist farmers in evaluating agricultural land for cultivating across four decision categories: very appropriate, suitable, somewhat suitable, and inappropriate. This evaluation is based on the data gathered from various sensor devices for system training. The findings achieved with the MLP with four concealed layers demonstrate efficacy for the multiclass categorization method compared to other current models. This trained system will assess future evaluations and categorize the land post-cultivation.

Real Time Recognition of Face Covered with Facemask using CNN for Application in Classroom Attendance System

Because each person has a different face identity, facial recognition relies on the distinctness of individual facial traits. However, the recent Covid-19 pandemic has substantially altered daily living, required the usage of facemasks while walking outside, including for educational activities such as attending lectures at schools and universities. Facemasks' widespread use presents a significant problem to facial recognition systems since they obscure a significant area of the face, limiting the availability of important facial clues. In response to this critical issue, this project aims to create a Convolutional Neural Network (CNN) architecture suitable for use in a classroom attendance system. The research process begins with the creation of a large training dataset that includes five different students with four different facial circumstances, including facemask covering. The best CNN design is determined through painstaking testing to enhance accuracy during both the training and validation phases. The empirical findings demonstrated that the finalized CNN architecture has a remarkable validation accuracy of up to 96.2%. The trained system demonstrated outstanding precision in recognizing persons during real-time recognition tests. This experiment highlights the CNN's ability to recognize students with high accuracy even while they are wearing facemasks, indicating its potential in solving the issues provided by the prevailing public health measures.

Dynamic Interaction Analysis of a Coupled System of Permanent Maglev Train and Flexible Track Beam

Vehicle–track beam coupling resonance greatly influences the vibration response of the suspended permanent (SPML) system. Therefore, the study established the coupled dynamic model of the SPML train and the track beam using. The model is used to analyze the dynamic response of SPML trains when passing through the long-span flexible track beam at resonance speed. The accuracy of the model was verified by field tests, which simulated and compared the dynamic responses of maglev train and track beam systems at different running speeds. Furthermore, the coupled vibration response of the train and track beam at resonant speed are investigated to gain insight into the mechanism and vibrational properties of the SPML system in a resonant background. It shows that the dominant frequency of the train, suspension frame, and track beam is 6.74[Formula: see text]Hz. They result in resonance in the SPML system at 30[Formula: see text]km/h. Further analysis shows that adjusting the vertical distance between the mass center of the vehicle and the track beam can effectively alleviate the resonance between the vehicle and the track. Additionally, deformation at the ends of the long-span track beams affects the levitation force and gap of the suspension frame, offering valuable guidance for SPML system design.

Automating global landslide detection with heterogeneous ensemble deep-learning classification

With changing climatic conditions, we are already seeing an increase in extreme weather events and their secondary consequences, including landslides. Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. However, these both rely on data from previous landslide events, which is often scarce. Many deep learning (DL) models have recently been applied for landslide mapping using medium-to high-resolution satellite images as input. However, they often suffer from sensitivity problems, overfitting, and low mapping accuracy. This study addresses some of these limitations by using a diverse global landslide dataset, using different segmentation models, such as Unet, Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an ensemble model. The ensemble model achieved the highest F1-score (0.69) when combining both Sentinel-1 and Sentinel-2 bands, with the highest average improvement of 6.87 % when the ensemble size was 20. On the other hand, Sentinel-2 bands performed well, with an F1 score of 0.61 only when the ensemble size is 20 with an improvement of 14.59 %. This result shows considerable potential in building a robust and reliable monitoring system to minimise landslide hazards by building the ensemble globally trained system based on changes in vegetation index dNDVI only.

A study on load sharing characteristics of the planetary gear train system with cracked gears

Load sharing characteristic is very important for the reliability of the planetary gear train. Cracked gears can make the load sharing performance become worse. Nevertheless, previous studies have not paid enough attention to the influence of cracked gears on the load sharing behavior, when compared with the impact of multiple factors under healthy condition. An 18 degrees of freedom dynamic model for the spur planetary gear train is established based on the centralized parameters theory in this paper. The influences of cracked gears on the load sharing behavior are investigated, which include the cracked sun gear, the planet gear containing cracked side meshing with the sun gear, the planet gear having cracked side meshing with the ring gear and the cracked ring gear. Additionally, the sensitivity analysis of the external and internal load sharing behavior to different cracked components is conducted. The results indicate that the load distribution becomes more uneven with the propagation of crack in the sun gear and ring gear, while the change regulation is different for each cracked planet gear. Moreover, the load sharing characteristic of the external meshing is more sensitive to the cracked sun gear and the planet gear 1, which has the crack side meshing with the ring gear. While the load sharing characteristic of the internal meshing is more sensitive to the cracked ring gear and the planet gear 3, which owns the crack side meshing with the ring gear than others. The output of this paper will be useful for the crack fault diagnose and fault source identification of the planetary gear train system.

Adaptive feature fusion and disturbance correction for accurate remaining useful life prediction of rolling bearings

As a key component in the transmission system of high-speed trains, bearings need to withstand certain loads while rotating at high speeds. Once a failure occurs, it can directly affect the safety of train operations. Therefore, it is of great significance to establish a reliable remaining useful life model to ensure train operation safety. Addressing the problems of redundant features in the existing multi-feature fusion process, which affects diagnostic performance, and the spurious fluctuations in the fusion features, which cause inaccurate determination of the start fault time and lead to low prediction accuracy of remaining useful life, we propose a method based on adaptive feature fusion and the autoregressive integrated moving average model for rolling bearing prediction. Firstly, features from the time domain, frequency domain, and entropy are extracted. A feature selection mechanism with minimum redundancy is constructed to screen the optimal sensitive feature set. Secondly, based on adaptive feature fusion, the optimal sensitive feature set is dynamically fused, and the spurious fluctuations of the health index are corrected using linear regression and the 3σ principle. Next, a bottom-up time series segmentation method is employed to divide the health status of the Improved Health Indicators. Finally, a remaining useful life prediction model based on the autoregressive integrated moving average model is established. This study demonstrates that the proposed method effectively identifies features that are most sensitive to degradation trends, accurately determines the initial fault moment of bearings, and achieves effective prediction of the remaining useful life of bearings.

Efficient Computation of Radiative Heat Recovery from Porous Ceramic Monoliths for Efficient Solar Thermochemical Fuel Production

Solar thermochemical hydrogen (STCH) produced by heat-driven water-splitting is a promising route for producing green hydrogen and other zero-emission synfuels. However, the efficiency of STCH must be dramatically increased for it to make an impact on decarbonization efforts. We have previously presented a novel Reactor Train System (RTS) for significantly increasing the efficiency of STCH by employing heat recovery from the redox material and efficient gas exchange processes. In this paper we present a higher-fidelity model for the RTS that accommodates the slow heat diffusion through the STCH redox material. For this purpose, a novel method is introduced for transient modelling of radiative heat in participating media. This method, called GREENER: Generalized Radiation Exchange Factors and Net Radiation, combines the accuracy of Monte Carlo Ray Tracing with the low computational cost of the P1 or Rosseland diffusion approximations. Along with STCH, GREENER has application for modelling volumetric solar receivers, high temperature heat recovery systems like heat exchangers and regenerators, and packed bed reactors. Using the GREENER method, the RTS counterflow radiative heat exchanger is shown to achieve heat recovery effectiveness greater than 70%. The performance of non-uniform porous redox morphologies is evaluated, and high-performing configurations are identified.

A pilot study of thecardiopulmonary effects in healthy volunteers after exposure to high levels of PM2.5 in a New York City subway station.

Subway systems are becoming increasingly common worldwide transporting large populations in major cities. PM2.5 concentrations have been demonstrated to be exceptionally high when underground, however. Studies on the impact of subway PM exposure on cardiopulmonary health in the United States are limited. Healthy volunteers in New York City were exposed to a 2-h visit on the 9th Street Station platform on the Port Authority Trans-Hudson train system. Blood pressure, heart rate variability (HRV), spirometry, and forced impulse oscillometry were measured, and urine, blood spot, and nasal swab biosamples were collected for cytokine analysis at the end of the 2-h exposure period. These endpoints were compared against individual control measurements collected after 2-h in a "clean" control space. In addition to paired comparisons, mixed effects models with subject as a random effect were employed to investigate the effect of the PM2.5 concentrations and visit type (i.e., subway vs. control). Mean PM2.5 concentrations on the platform and during the control visit were 293.6 ± 65.7 (SD) and 4.6 ± 1.9µg/m3, respectively. There was no change in any of the health metrics, but there was a non-significant trend for SDNN to be lower after subway exposure compared to control exposure. Total symptomatic scores did increase post-subway exposure compared to reported values prior to exposure or after the control visit. No significant changes in cytokine concentrations in any specimen type were observed. Mixed-effects models mostly corroborated these paired comparisons. Acute exposures to PM on a subway platform do not cause measurable cardiopulmonary effects apart from reductions in HRV and increases in symptoms in healthy volunteers. These findings match other studies that found little to no changes in lung function and blood pressure after exposure in underground subway stations. Future work should still target potentially more vulnerable populations, such as individuals with asthma or those who spend increased time underground on the subway such as transit workers.

Public Transport Delay Prediction using Deep Learning

A system of trains, buses, subways, ferries, and other vehicles that are accessible to the public is referred to as public transportation, also known as public transit or mass transit. These networks are usually run by private or public organisations are made to efficiently transport large numbers of people in urban and suburban areas. It usually operates on set schedules and routes and has a listed fare for each journey. Long wait periods and lengthy travel times resulting from delays are two problems that frequently plague public transportation services. There are several things that can cause a delay in public transport, such as an excess of passengers, heavy traffic, accidents, and other unforeseen circumstances. The availability of a more accurate delay prediction for public transportation might increase users’ confidence and their willingness to pay more for transit services. Over the past two decades, a number of studies on prediction algorithms for transportation data have been proposed. Most of the work is on machine learning model development, focusing on delay prediction and taking into account several factors such as weather conditions and infrastructure issues. This paper proposes a deep learning model to predict public transportation delays using data from public transportation and the weather. The results obtained from this research work are compared with several other existing works. Our experiment has demonstrated that the deep neural network (DNN) is the best model to predict transit delay compared to several other machine learning and deep learning models.

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.