Train System Research Articles

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>

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.

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.

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.

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.

Numerical investigation on the heat dissipation of phase change materials used in the high-speed train brake system

The massive heat dissipation demands of the brake system in high-speed trains pose a significant obstacle to achieving higher operation speeds. Phase change material has attracted considerable attention in various fields due to their exceptional heat dissipation capabilities, yet their utilization in the brake system of high-speed trains remains unexplored. This study aims to investigate the feasibility of phase change material application in the brake system of high-speed train. Specifically, in Case A, the introduction of phase change material resulted in a notable 21% decrease in the average temperature and a remarkable 40% reduction in the maximum temperature difference within the brake system. The latent heat of the phase change material plays a crucial role in maintaining a substantial temperature differential between the cooling components and discs, thereby enhancing heat flux in the brake system. Phase change materials exhibit superior cooling performance compared to traditional air cooling methods in the brake system. To expedite the cooling process of phase change material and facilitate its transition from liquid to solid, an optimized brake system structure utilizing phase change material was proposed. This optimized design holds promise in enhancing the overall heat dissipation efficiency of the high-speed train brake system.

Time-delay feedback control on vertical vibration of maglev train under unsteady aerodynamic force

The stability and safety of maglev trains are significantly affected by unsteady aerodynamic forces during high-speed operation. In this paper, we employ a time-delay feedback control strategy to suppress the nonlinear vertical vibrations of maglev train under periodic unsteady aerodynamic force. Firstly, the amplitude-frequency response equation of the maglev vehicle is derived using the multiple scales method, and the stability of the steady-state solutions is determined. Then, the influences of velocity feedback gain coefficient and velocity time delay on the nonlinear vibrations of the maglev vehicle are investigated. The optimal value range of the time delay is identified. The results demonstrate that the time delay feedback control can effectively suppress the nonlinear vibration of maglev vehicles and enhance their operational stability under the premise of reasonable selection of time delay parameters. The theoretical research presented in this paper can serve as a theoretical reference for the design, improvement, and optimization of control systems for high-speed maglev trains.

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.

INFLUENCE OF MOISTURE CONDITION AND SILICA SAND ON FRICTION COEFFICIENT OF WIND TURBINE BRAKE SYSTEM

Major drive train parts for the horizontal wind turbines including the gearbox, generator, and brake system have very high repair and replacement costs and take a long time to complete. This paper investigates the impact of environmental air pollution on drivetrain tribology behaviors. Experimental research of the wind turbine's drive train system is simulated and investigated. The mechanical brake system of wind turbines is the subject of one case study chosen for this research paper. This research paper is examined how impurities affect the friction coefficient of the brake on wind turbines. According to the findings, the friction coefficient rises with each increase in wind turbine shaft speed. Also, it has been discovered that the size of the contaminants' particles significantly affects the friction coefficient. Furthermore, the findings may offer helpful insights of the drive train system that as humidity increased from 30 mm3 to 90 mm3, the coefficient of friction significantly decreased.

On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains

AbstractPartial discharge (PD) detection of cable terminals is crucial for the safe operation of the traction power system in trains. However, similar PD signals in complex train‐operating environments cause difficulty to recognise the insulation defects. Therefore, a PD signal image transformation recognition method is proposed for PD detection of cable terminal defects to identify defects in cable terminals with similar PD characteristics accurately. In the proposed method, the raw PD signals are firstly transformed to images via the Gramian angular field (GAF) representation. This can reveal the discriminative characteristics embedded in the original PD signals and subsequently facilitate differentiating the PD sources, which exhibit similar characteristic in the time domain. The obtained GAF representation of PD signals (named as PD GAF images) is extracted from local and global features to train an efficient MobileVIT model, which is then utilised to identify similar types of PD sources in cable terminals. The results show that the proposed method achieves 97.5% recognition accuracy in the field experiment, which is superior to other methods.

Remote control of the technical condition of metro trains using seismological tools

As urban road congestion increases and people shift to using the subway, the safety of under-ground transportation becomes a priority. Therefore, it is necessary to establish a monitoring sys-tem for subway trains to detect malfunctions that occur during operation. The article proposes an engineering seismology method for remote monitoring of the technical condition of metro trains based on the analysis of micro seismic vibrations. This method allows remote control of the dynamic parameters of each train and their deviations from the norm. The authors demonstrate how a seismic recorder placed in a building adjacent to a metro station can be used to monitor changes in the amplitude-frequency spectrum of vibro seismic recordings. It is used to determine the arrival and departure times of trains, and it is shown that observations are best made for departures due to their more pronounced signal amplitude. The recorded signal of a metro train should fall within a certain frequency range, which is determined based on its average values, and any deviation of the observed signal spectrum from this norm indicates changes in the train's condition. The proposed approach not only helps improve passenger safety, but also reduces the risk of emergencies associated with technical malfunctions of rolling stock. In the future, it is possible to develop software equipped with a database of statistical data, which will allow even more efficient control of metro operations and prompt response to any deviations, thus ensuring a high level of safety and comfort for passengers.

Similarity design method and multi-objective optimization of train energy-absorbing tube velocity distortion under axial impact

Thin-walled tubes are commonly used as crucial structures for energy absorption. It is an efficient and convenient research method to establish a small-scale model of the energy-absorbing thin-walled tube for experiments. However, due to the inevitable difference between the design velocity of the test and the actual velocity of the test, it cannot meet the traditional similarity theory. Because of this limitation, this article constructs a similarity design method for the velocity distortion of the multi-cell energy-absorbing tube at the front end of the train under axial impact. Firstly, according to the motion equation of the train system, the design criterion of the similar model under the velocity distortion is derived. Then, the collision mass correction factor is derived based on the elastic-plastic structural energy equation, aiming at the error of the collision peak force and the error of the collision time, the correction factor of the wall thickness of the energy absorbing tube is obtained based on the simulation analysis results. Finally, it is verified that the velocity distortion similarity model can accurately predict the dynamic response of the prototype, and reveals the influence of the mass and wall thickness correction factors on the error.

Combining machine learning (ML) and participatory rural appraisal (PRA) for disaster risk preparedness (DRP): Evidence from the poorest region of Luzon, Philippines

In the field of social science, disaster risk preparedness (DRP) is considered immeasurable due to its multidimensional nature, making it infamously difficult to quantify. The current measurements are costly, labor-intensive, and time-consuming. Consequently, policymakers struggle to target policies effectively when implementing disaster risk reduction management initiatives. By combining Participatory Rural Appraisal (PRA) and Machine Learning (ML) to train and test community-based system datasets, this work proposes novel approaches to DRP in the poorest region of Luzon, Philippines. We utilized sophisticated econometrics models along with ML categorization methods. Through the analysis of 34 locales and 4 sectors within a disaggregation system over 429 ensemble runs using cross-validation techniques, we then combined the results. The Support Vector Machine (SVM) classifier achieved the highest accuracy of 91.55 % randomly and 94.53 % within the pipeline, surpassing all other models. It also confirms the current relationship between DRP and multidimensional attributes (a total of 21 factors) in terms of correlation and causation. Our work showcases the potential of ML for disaster risk prediction, potentially reducing costs, saving labor, and optimizing time, especially in the most impoverished areas of the Philippines. Ultimately, through extensive PRA, the outcomes have provided different localities with tools for targeting policies in disaster risk management.