Search Results (157)

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

For a given timetable in urban rail transit systems, this paper presents a practical energy efficiency optimization problem that carries out adjustments to the timetable, with the goal of energy saving. We propose two strategies to address this challenge, including adjusting the section running time by selecting a speed profile and improving the utilization of regenerative braking energy by adjusting the trains’ departure time. Constraints on the range of adjustment for energy-efficient time elements are constructed for maintaining the stability of elements of the given timetable. An energy efficiency optimization model is then established to minimize the total net energy consumption of the timetable, and a solution algorithm based on a genetic algorithm is proposed. We make small-scale adjustments to trains’ running trajectories to optimize the overlap time of braking and traction conditions among multiple trains. The case of the Guangzhou Metro Line 8 in China is presented to verify the effectiveness and practicality of our method. The results show that the consumption of traction energy is reduced by 0.95% and the use of regenerative braking energy is increased by 8.18%, with an improvement in energy efficiency of 6.78%. This method can achieve relatively significant energy efficiency results while ensuring the stable service quality of the train timetable and can provide support for an energy-efficient train timetable for urban rail transit operation enterprises. Full article

Friction behaviour at the wheel–rail interface is of critical importance for railway operations and maintenance and is generally characterised by creep curves. The V-Track test rig was used in this study to measure both the lateral and longitudinal creep curves with uncontaminated dry interface conditions, utilising contact pressures representative of operational railway wheel–rail systems. The novelties of this study are threefold. 1. With proper representations of train/track components, the V-Track tests revealed the effects of structural dynamics on measuring wheel–rail creep curves in real life. 2. Pure lateral and longitudinal creepage conditions were produced with two distinct experimental principles—displacement- and force-controlled—on the V-Track, i.e., by carefully controlling the angle of attack and the traction/braking torque, respectively, and thus the coefficient of friction from lateral and longitudinal creep curves measured on the same platform could be cross-checked. 3. The uncertainties in the measured creep curves were analysed, which was rarely addressed in previous studies on creep curve measurements. In addition, the measured creep curves were compared against the theoretical creep curves obtained from Kalker’s CONTACT. The influence of wheel rolling speed and torque direction on the creep curve characteristics was then investigated. The measurement results and findings demonstrate the reliability of the V-Track to measure wheel–rail creep curves and study the wheel–rail frictional rolling contact. Full article

A machine learning framework is developed to interpret vehicle subsystem status from sensor data, providing actionable insights for adaptive control systems. Using the vehicle’s suspension as a case study, inertial data are collected from driving maneuvers, including braking and cornering, to seed a prototype XGBoost classifier. The classifier then pseudo-labels a larger exemplar dataset acquired from street and racetrack sessions, which is used to train an inference model capable of robust generalization across both regular and performance driving. An overlapping sliding-window grading approach with reverse exponential weighting smooths transient fluctuations while preserving responsiveness. The resulting real-time semantic mode predictions accurately describe the vehicle’s current dynamics and can inform a model predictive control system that can adjust suspension parameters and update internal constraints for improved performance, ride comfort, and component longevity. The methodology extends to other components, such as braking systems, offering a scalable path toward fully self-optimizing vehicle control in both conventional and autonomous platforms. Full article

Earthquakes can trigger emergency braking in urban rail systems, yet the combined effect of braking and ground motion on train–bridge safety remains poorly quantified. Using the Wuxi Metro Line S1 (160 km/h initial speed) on a ten-span simply supported bridge as a case study, we build a multi-body dynamic subway model coupled to a finite element track–bridge model with non-linear Hertz wheel–rail contact. Under the design-basis earthquake (PGA ≈ 0.10 g), the train’s derailment coefficient and lateral car body acceleration rise by 37% and 45%, while the bridge’s lateral and vertical accelerations increase by 62% and 30%, respectively. Introducing a constant emergency brake deceleration of 1.2 m/s² cuts those train-side peaks by 20–25% and lowers the bridge’s lateral acceleration by 18%. The results show that timely braking not only protects passengers but also mitigates seismic demand on the structure, offering quantitative guidance for urban rail emergency protocols in earthquake-prone regions. Full article

To improve the transient performance of speed tracking control while ensuring stability and considering actuator constraints in heavy-haul train systems, this paper proposes a novel cruise control method based on a nonparallel distributed compensation (non-PDC) fuzzy-based composite nonlinear feedback (CNF) technique. First, a low-dimensional nonlinear multi-particle error dynamics model is established based on the fencing concept, simplifying the model significantly. To facilitate controller design, a Takagi–Sugeno (T-S) fuzzy model is derived from the nonlinear model. Subsequently, sufficient conditions for the non-PDC fuzzy-based CNF controller are provided in terms of linear matrix inequalities (LMIs), with the controller design addressing asymmetric constraints on control inputs due to differing maximums of traction and braking forces. Simulations based on MATLAB/Simulink are conducted under different maneuvers to validate the effectiveness and superiority of the proposed method. The simulation results demonstrate a notable enhancement in transient performance (over 22.3% improvement in settling time) and steady-state cruise control performance for heavy-haul trains using the proposed strategy. Full article

Accurately simulating the thermal behavior of wheel–brake shoe friction on long downhill ramps is challenging due to the complexity of modeling appropriate heat source models. This study investigates heat generation during the frictional braking process of freight train wheels and brake shoes under long-slope conditions. Four heat source models—constant, modified Gaussian, sinusoidal, and parabolic distributions—were developed based on energy conservation principles and validated through experimental data. A thermomechanical coupled finite element model was established, incorporating a moving heat source to analyze the effects of different models on wheel tread temperature distribution and its evolution over time. The results show that all four models effectively simulate frictional heat generation, with computed temperatures, deviating by only 6.0–8.2% from experimental measurements, confirming their accuracy and reliability. Among the models, the modified Gaussian distribution heat source, with its significantly higher peak local heat flux (2.82 times that of the constant model) and rapid attenuation, offers the most precise simulation of the non-uniform temperature distribution in the contact region. This leads to a 40% increase in the temperature gradient variation rate and effectively reproduces the “hot spot” effect. The new non-uniform heat source model accurately captures local temperature dynamics and predicts frictional heat transfer and thermal damage trends. The modified Gaussian distribution model outperforms others in simulating local temperature peaks, offering support for optimizing braking system models and improving thermal damage prediction. Future research will refine this model by incorporating factors like material wear, environmental conditions, and dynamic contact characteristics. Full article

As a key component of the train braking system, the comprehensive performance of brake discs plays a vital role in ensuring the operational safety of trains. With the advent of high-speed and heavy-haul trains, the thermal energy generated by braking systems has significantly increased. The resulting rapid temperature rise can easily exceed the material limits of brake discs. Consequently, research focused on enhancing brake disc performance in high-temperature environments, improving thermal fatigue resistance, and optimizing tribological properties has become increasingly critical. Brake disc materials have undergone substantial evolution, transitioning from traditional iron and steel to lightweight aluminum matrix composites and carbon matrix composites. While iron and steel benefit from mature manufacturing processes and proven reliability, their high mass density poses challenges in meeting the demands for lightweight and high-speed development in modern rail transit. Although aluminum matrix composites and carbon matrix composites offer advantages like low density and high heat capacity, they still face several technical challenges in practical applications. This paper outlines the key characteristics of train brake disc materials, emphasizing the application status and research progress of iron and steel, aluminum matrix composites, and carbon matrix composites. Additionally, it briefly introduces surface modification technologies for iron and steel brake discs, with the goal of providing insights and references to guide the innovation and development of train brake disc materials. Full article

For reliable autonomous train operation, detecting and classifying obstacles on or near rail tracks, and accurately estimating the distance to these obstacles, is essential. This task is more challenging in low-light conditions, common for freight trains that operate primarily at night. This paper proposes a novel method, FuzzyH, for estimating the distance between a thermal camera and detected obstacles using image-plane homography. By leveraging the homography between the image and rail track planes, and incorporating a fuzzy logic system, the method improves distance estimation accuracy and eliminates the need for complex calibration. This paper also explores the symmetry and asymmetry of fuzzy membership functions and rules. The system was validated on Serbian railways under simulated real-world conditions, demonstrating reliable performance. A key contribution of this method is the use of fuzzy membership functions tailored to specific distance ranges, based on experimental data and domain knowledge, such as regulatory braking distances. This approach improves over traditional methods by offering reliable distance estimates in low-light environments and simplifying the calibration process, ultimately enhancing system accuracy and robustness. Full article

The brake system is a key system for the safe operation and stopping of trains. As a core component of brake systems, the brake actuation unit (BAU) is essential for slowing down or stopping trains, and faults in the BAU will affect the safety and efficiency of train operation. In order to detect and locate faults in the BAU in time, a fault detection and isolation (FDI) strategy, based on mutual residuals (MRs), principal component analysis (PCA) and improved reconstruction-based contribution plots (IRBCP), was proposed. Firstly, the structural composition and working principle of the BAU were introduced, and its typical failure modes and effects were analyzed. Secondly, considering that the fault detection threshold is not easy to determine due to the variable operating conditions of the BAU, the steady-state fault feature based on MR was extracted. Thirdly, fault detection and isolation were realized based on PCA and the IRBCP algorithm. Finally, by using the fault injection method, case studies on test-rig experiment data of brake systems were conducted; the fault detection rate was 99% and the effectiveness of the proposed strategy was validated by the test data. The proposed strategy shows fast computing ability, and is suitable for systems with dynamic time-varying and nonlinear characteristics. Full article

The majority of freight trains are characterized by a braking system that does not guarantee synchronous braking between different wagons. This results in the generation of considerable in-train forces during emergency braking operations, which are sometimes imposed by the railway infrastructure due to certain running speeds being exceeded. The magnitude of in-train forces is contingent upon a number of factors, the most significant ones being the length, mass and load composition of the trainset, in addition to the specific braking imposed. The application of excessive compressive in-train forces has the potential to cause the wagon to derail, particularly if the wagon is lightweight and traversing a small radius curve. Similarly, excessive tensile in-train forces applied to the screw couplers can cause them to fail, typically through fatigue, resulting in train disruption and necessitating the recovery of both portions of the trainset. The objective of this study is to perform a preliminary analysis of the UIC (International Union of Railways) unified screw couplers fatigue phenomenon, employing load spectra computed by the UIC 1.4.6 software TrainDy. A possible future development is developing a maintenance model functional to predict the extent of damage in freight wagon screw couplers during their service life. Full article

When a railway train runs along a curved track with braking, the dynamic behaviors of the vehicle are extremely complex and difficult to accurately reveal due to the coupling effects between the wheel–rail interactions and the disc–pad frictions. Therefore, a rigid–flexible coupled trailer car dynamics model of a railway train is established. In this model, the brake systems and vehicle system are dynamically coupled via the frictions within the braking interface, wheel–rail relationships and suspension systems. Furthermore, the effectiveness of the established model is validated by a comparison with the field test data. Based on this, the dynamic response characteristics of vehicle under curve and straight braking conditions are analyzed and compared, and the influence of the curve geometric parameters on vehicle vibration and operation safety is explored. The results show that braking on a curve track directly affects the vibration characteristics of the vehicle and reduces its operation safety. When the vehicle is braking on a curve track, the lateral vibration of the bogie frame significantly increases compared to the vehicle braking on a straight track, and the vibration intensifies as the curve radius decreases. When the curved track maintains equilibrium superelevation, the differences in primary suspension force, wheel–rail vertical force, and wheel axle lateral force between the inner and outer sides of the first and second wheelsets are relatively minor under both straight and curved braking conditions. Additionally, under these circumstances, the derailment coefficient is minimized. However, when the curve radius is 7000 m, with a superelevation of 40 mm, the maximum dynamic wheel load reduction rate of the inner wheel of the second wheelset is 0.54, which reaches 90% of the allowable limit value of 0.6 for the safety index, and impacts the vehicle running safety. Therefore, it is necessary to focus on the operation safety of railway trains when braking on curved tracks. Full article

The reliability of the bidirectional converter plays an important role in the energy storage system. However, the power devices that make up the converter are prone to failure under complex operating conditions. Therefore, how to extend the service life of devices in this case becomes a tricky problem. Due to the typical intermittent, random, and fluctuating nature of train regenerative braking energy, only relying on capacity allocation to extend device life is easily limited by actual operating conditions. Meanwhile, most energy management strategies tend to focus on only two factors: economic cost and energy savings. Therefore, this paper proposes an energy management strategy that considers the lifetime of the energy storage converter device. The objective function of the energy management strategy including device life and systematic evaluation indicators is then established. Taking the actual line conditions as a case, the bald eagle search (BES) is used to optimize the objective. This method can lengthen the IGBT device lifetime and reduce the economic cost while achieving a good energy saving rate and voltage stabilization rate. Its effectiveness and feasibility are verified by simulations under different arithmetic cases. Full article

The study aims to introduce a novel system that powers a passenger train using supercapacitor energy storage that is charged by a solar carport system located at each train stop station. The system’s detailed design and its techno-economic analysis have been carried out and applied to a case study of a supercapacitor-based train (SC-Train) that connects an international airport with five major cities in the eastern region of Saudi Arabia. The objective is to reduce CO₂ emissions from Saudi Arabia’s transportation sector utilizing an electric-operated train energized by a solar carport system. The solar carport system is designed to have a capacity equal to the train’s energy consumption. Additionally, the supercapacitor has been selected as a storage device to utilize the regenerative braking system feature to enhance the train’s energy efficiency, which results in energy savings equivalent to 44.9% of total energy consumption. Finally, the project’s feasibility has been determined via the benefit–cost analysis approach, which yields a positive net present value of USD 367 million over 30 years. Full article