Urban Rail Train Research Articles

The influence of forward leaning postures on passenger injury in urban rail train collision

The influence of forward leaning postures on passenger injury in urban rail train collision

Research on Multi-Objective Optimization Methods of Urban Rail Train Automatic Driving Based on NSGA-II

In order to improve the control performance of automatic train operation (ATO) in urban rail trains, five typical operating sequences of urban rail trains were studied. Under the condition of meeting the safety and comfort principles of train operation, a train dynamics model was established to achieve the goals of low energy consumption, short running time, and high stopping accuracy in urban rail transit trains. In the process of finding a multi-objective solution to this problem, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used with an elite retention strategy, and the optimal Pareto multi-objective solution set was sought. In the process of optimal solution weight assignment, the hierarchical analysis Mahalanobis distance method, which combines subjective and objective analysis, was used. Finally, taking the Beijing Yizhuang subway line as the background design example, the simulation verified the effectiveness and feasibility of the algorithm and obtained high-quality automatic train driving curves under various working conditions. This research has important reference significance for the actual operation of automatic driving in urban rail trains.

LiDAR-Based Urban Three-Dimensional Rail Area Extraction for Improved Train Collision Warnings.

The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms.

Coordinated Control of the Onboard and Wayside Energy Storage System of an Urban Rail Train Based on Rule Mining

There are three major challenges to the broad implementation of energy storage systems (ESSs) in urban rail transit: maximizing the absorption of regenerative braking power, enabling online global optimal control, and ensuring algorithm portability. To address these problems, a coordinated control framework between onboard and wayside ESSs is proposed in this study, and the related control strategy is obtained by transforming the global optimization problem into a combination of rule-based and local optimization problems. The design process of the strategy is divided into three parts: offline optimization, rule extraction, and local optimization. First, a genetic algorithm is used to optimize the charge/discharge threshold curves under typical operational conditions. Then, the rules of offline optimization results are obtained from an expert system, and the nonlinear rules are mined with local optimization methods. Finally, a coordinated strategy is presented based on the outcomes of the three parts. A power hardware-in-the-loop (PHIL) experimental platform based on RTLAB is built, and the above coordination strategy is verified based on data for the Beijing Metro Batong Line. The experimental results show that the strategy can effectively improve the energy-saving rate and reduce the regeneration failure rate substantially, with the effect being close to the offline global optimal solution. The algorithm proposed in this paper achieves near global optimal energy-saving optimization results with lower computational costs, and has strong portability, providing a good solution for the large-scale application of onboard and wayside energy storage coordination control.

A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions

Owing to the rapidly varying working conditions of urban rail trains, the rotational speed conditions constantly shift in a short time span. As a key component of the running gear, the gearbox generates non-stationary vibration signals, making it challenging to monitor its health status. To address this challenge, a tacholess order tracking method (TLOT) based on ridge extraction method is proposed in this paper. This method determines the optimal search starting point using the time-frequency Gini coefficient and extracts the time-varying gearbox meshing frequency components from the time-frequency representation results. Furthermore, the TLOT method is utilized to process the strongly time-varying gearbox signal. In the order domain, multiple frequency components are extracted to reconstruct the signal. Simulation and experimental results demonstrate that the proposed method achieves a superior ridge extraction effect on gearbox experimental signals. It accurately converts unstable signals into angular domain stable signals, enhancing the energy aggregation of time-varying unstable signals in the order domain. This approach addresses the problem of weak harmonic component extraction from gearbox signals and effectively reduces interference in the resonance band, realizing fault diagnosis of the gearbox under unstable working conditions.

Energy management strategy of urban rail hybrid energy storage system based on fuzzy logic system

Energy management is an important link in the effective functioning of hybrid energy storage systems (HESS) within urban rail trains. This factor significantly impacts the operational stability and economic efficiency of urban rail systems. Safety issues arise from DC bus voltage fluctuations due to varying train conditions. To address these issues, this paper proposes an energy management strategy for the urban rail HESS, which builds upon a traditional double closed-loop control strategy. This strategy incorporates a fuzzy logic system to regulate power distribution parameters and discharge thresholds dynamically. Real-time monitoring of the state of charge (SOC) of the energy storage device informs the decision-making process, utilizing a fuzzy inference system to effectively regulate power and energy distribution in response to the distinct characteristics of batteries and super-capacitors. This approach aims to mitigate power fluctuations within the traction network. To validate the effectiveness of the proposed energy management strategy, the study conducts a comprehensive simulation experiment using MATLAB/Simulink. The simulation results confirm the efficacy of the proposed strategy, highlighting its potential to enhance the stability and overall performance of urban rail train operations.

Investigation and Analysis of Electromagnetic Interference for PWM Encoder of Urban Rail Train

Electromagnetic compatibility (EMC) is an important factor in ensuring the safe operation of the sensitive electronic equipment on urban rail trains. The pulse width modulation (PWM) encoder of an urban rail train exported from China to Brazil is sometimes affected by electromagnetic interference (EMI), which causes the train to fail to run properly. To solve this problem, the EMC test is performed on the PWM encoder to identify the coupling path and the type of interference source. The EMI coupling model and the vehicle-catenary-rail model are established by using an electromagnetic transients program (EMTP) to analyze the mechanism of interference coupling. It is shown that the unbalanced voltage of the train body caused by the backflow of the grounding current is the root cause of the interference of the PWM encoder. The maximum voltage coupled to the internal port of the PWM encoder is about 1894 V, which is sufficient to burn out the encoder. A measure to suppress the interference by installing thyristor surge suppressors (TSS) P0300SC is proposed, which effectively solves the EMI problem of the PWM encoder.

A holistic solution to virtual coupling based urban rail train control system

Virtual coupling (VC) is an emerging concept that is gaining popularity in railway control systems. It has the potential to significantly enhance railway capacity and flexibility, and has attracted considerable research attention. This study explores several key aspects of VC research in the context of urban rail transit systems, including system structure, train platoon control method, scenario definition, and train scheduling. The paper presents three main contributions: Firstly, it proposes a potential system structure for VC and discusses infrastructure occupancy procedures within typical operational scenarios. Secondly, a VC platoon controller based on the adaptive back-stepping method is designed to realize the function of the operation layer. Thirdly, combining operational scenario definition and control methods, we propose a VC train scheduling method based on mixed integer linear programming (MILP). A case study demonstrated that the proposed solution could effectively achieve the primary functions of VC.

Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA

Circulant Singular Spectrum Analysis (CiSSA) performs well in the decomposition and extraction of the periodic components of nonstationary signals. However, the decomposition of signals in complex environments suffers from spectral aliasing and difficulties in extracting feature information. Therefore, based on CiSSA, an improved CiSSA and multipoint optimal minimum entropy deconvolution adjustment (ICiSSA-MOMEDA) is proposed and applied to the early faint fault diagnosis of axlebox bearings of urban rail train wheelsets. First, the optimal embedding dimension was computed adaptively and accurately using an improved Cao’s method. Then, the initial components obtained from the decomposition were reorganized using the K-ARs method. ICiSSA effectively solves the problems of spectrum confusion and fault-information dispersion. Finally, ICiSSA was combined with MOMEDA to improve its ability to detect weak fault information. The superiority of ICiSSA-MOMEDA was verified based on the analysis of the actual bearing data and comparison with other methods.

Design and Optimization of Flywheel Energy Storage System for Rail Transit

At present, the urban rail transit system has problems such as energy waste in the braking process and unstable grid voltage in the start-stop state. Aiming at the problems caused by the start-stop state of rail transit, considering the energy saving and voltage stability requirements of system energy management, a flywheel energy storage system (FESS) specially used for rail transit is designed. The energy system (FESS) can feed back the braking energy stored by the flywheel to the urban rail train power system when the rail train starts to cause the voltage and frequency of the traction microgrid to change. This paper proposes a flywheel energy management system based on a permanent magnet synchronous motor (PMSM), which can realize efficient energy management through efficient control of the flywheel side motor. The flywheel side permanent magnet synchronous motor adopts an improved flywheel speed expansion energy storage control strategy based on current feedforward control to improve the fast response capability of the flywheel, ensuring the output power of the flywheel at high speed. In addition, a specific multi-threshold voltage single FESS control strategy is suggested, which improves the energy-saving and voltage-stabilizing performance of the system.

Research on Tracking Control of Urban Rail Trains Based on Improved Disturbance Observer

Urban rail transit trains operate in a complex and changing environment susceptible to uncertainties such as unknown disturbances or actuator faults. In order to ensure that the automatic train control (ATC) system can operate safely and control accurately even when the train is affected by uncertainties, the train is first subjected to force analysis, and a multiple point-mass model for the urban rail trains (URTs) is established. Secondly, an improved disturbance observer is proposed to estimate and attenuate the unknown disturbance online. The stability of the URTs system under the improved disturbance observer-based control (IDOBC) is demonstrated based on the linear matrix inequality and Lyapunov theorem. Finally, the Simulink platform is used to verify the target curves obtained after data optimization of the certain Nanchang Metro line for case verification. Compared to the traditional disturbance observer-based control (TDOBC) method, the train tracking error converges to near the zero region faster after being subjected to an external disturbance, and the mean absolute error (MAE) metric is smaller throughout the whole operation. The control strategy proposed in this paper has a more desirable control effect and can ensure the safer and more stable operation of the URTs.

Simulation of Comfort Algorithm for Automatic Driving of Urban Rail Train

Abstract The automation and intelligence of transportation vehicles is the focus and hotspot of the future research and development of the entire transportation industry, which can effectively improve a series of social problems such as traffic accidents, exhaust pollution, and traffic jams caused by the current increase in the number of transportation vehicles. With the rapid development of rail transit in our country, more and more people choose urban rail transit for travel, which is greatly convenient for travel. The comfort of Automatic Train Operation (ATO) system is an inevitable consideration when people choose it. Aiming at the comfort of ATO system, this paper designs a train operation target curve that can meet the comfort index of train. At the same time, two simulation models are established by using the SIMULINK module of MATLAB software to compare the experiments. One model is the train simulation model based on PID control, and the other model is the train simulation model based on fuzzy PID. The final simulation results show that fuzzy PID control has stronger superiority in train comfort in the process of train motion simulation, and the traditional PID control is not as good as fuzzy PID control in train comfort.

Power Allocation Strategy for Urban Rail HESS Based on Deep Reinforcement Learning Sequential Decision Optimization

A hybrid energy storage system (HESS) is adopted to tackle the traction network voltage fluctuation problem caused by high power and large energy demand during the starting and braking of urban rail trains. The system is composed of on-board ultracapacitors and ground lithium batteries, aiming to smooth out the power fluctuation to realize “peak-shaving and valley-filling”. Based on deep reinforcement learning (DRL) online sequence decision, a dynamic power allocation strategy is proposed to improve the energy-saving and voltage stabilization of DC traction networks as well as HESS life protection. Furthermore, to enhance the DRL’s efficiency under time-varying operating conditions, an annealing bias - priority experience replay twin delayed deep deterministic policy gradient algorithm (A-TD3) is proposed to train the replay buffer in DRL. The online learning and optimization strategy is implemented via the mechanism of “trial and error” and “feedback” of the agent. RT-LAB semi-physical real-time simulation systems are adopted to verify the effectiveness of the proposed strategy. Compared with the traditional filtering algorithms and DRL algorithms, the results show that the proposed method converges faster and is more energy saving and stable while effectively protecting the energy storage components.

An optimal selection method for exterior design schemes of subway trains based on multi-level gray relational analysis

To make appropriate decisions in the evaluation phase of the exterior design of subway trains, an optimal selection method was proposed based on multi-level gray relational analysis. The exterior design factors of subway trains were analyzed to construct an index system for design evaluation. The significance of each index was compared through an analytic hierarchy process. The correlation coefficient of each index in the plan was calculated through gray relational analysis to obtain the weighted correlation degree of each design scheme. The optimal selection of the exterior design of Guangzhou Metro Line 6 in China was considered as an example. Four types of subjects were recruited: professional designers, students majoring in design, subway train design experts, and subway passengers in Guangzhou. The weight of each index in the evaluation system was calculated using questionnaire scoring. Virtual simulation software was applied to evaluate the human factors related to each scheme. The indices in each plan were then scored to calculate the correlation coefficient and the overall correlation degree; and finally, the optimal selection was obtained. The results showed that it was practical to evaluate and optimize the exterior design of subway trains based on multi-level gray relational analysis. In the evaluation index system, the weights of technology, human factors, aesthetics, and culture were 0.517, 0.297, 0.099, and 0.087, respectively, which showed that technology had the greatest impact on the system, while human factors, aesthetics, and culture were useful complements. Our results showed that Design Scheme 1 was unsuitable as an optimization scheme due to the high escape window. Meanwhile, Design Scheme 2 was optimal overall, from a technical perspective. Design Scheme 3 was the best in terms of the escape window index (a human factor). Design Schemes 3 and 4 were optimally assessed from aesthetic and cultural perspectives. This study is conducive to the optimization of the exterior design of subway trains, can be used to inform design iteration, and provides a reference for the optimal selection of design schemes for other urban rail trains.

Research on Wheel Out-of-round Fault Diagnosis Based on Vibration Data Images

Background: The wheel out-of-round fault of urban rail vehicles has a very important impact on the safe operation of urban rail trains. Therefore, it is of great significance to achieve an accurate diagnosis of the wheel out-of-round fault of trains. Objective: The purpose of this paper is to summarize the diagnosis methods of the wheel out-of-round fault, and propose a new diagnosis method based on vibration data images, which can effectively identify the wheel out-of-round fault. Methods: The one-dimensional vibration signal is converted into a two-dimensional texture matrix. The Statistical Geometrical Features (SGF) method extracts the feature information of the twodimensional gray image and combines it with a support vector machine for training and recognition to achieve the fault diagnosis of the wheel out-of-roundness. Results: The feasibility and accuracy of the method are verified by simulation and experimental signal analysis, respectively. The experimental results show that the overall recognition accuracy of the model simulation data and the two-wheel experimental bench data exceeds 91%, exhibiting significantly high fault identification accuracy. Conclusion: In this paper, a wheel out-of-round fault diagnosis model based on vibration data images has been established by analyzing the vertical dynamic signal of the axle box, which has the advantages of fast recognition in combination with two-dimensional grey-scale images, no signal preprocessing, and high recognition accuracy. It provides a new method for monitoring and diagnosing wheel out-of-round faults in urban rail vehicles.

Dynamic Characteristics of Urban Rail Train in Multivehicle Marshaling under Traction Conditions

In recent years, urban rail transportation has rapidly developed in China and become one of the most important modes of travel. Most existing studies on the dynamic characteristics of urban rail trains have been based on single-section trains, and there have been fewer studies on marshaling urban rail trains that incorporate traction transmission systems. The dynamic performance of each carriage directly affects the operational reliability and even the running safety of urban rail trains. For this reason, in this paper, a marshaling urban rail train model with a traction transmission system was established and its accuracy was validated by field tests. This dynamics model enables the consideration of the coupling interactions between the gear transmission motion, the vertical, the lateral and the longitudinal motions of the vehicle. First, the model accuracy was validated by field tests. Then, the relationship between the motor torque and the running time of the urban rail train under traction conditions was calculated. Finally, the dynamic performance of each car of the marshaling train was studied. The research results show that there is a clear difference between the dynamics of the motor car and the trailer, and that the motor car is significantly inferior to the trailer. Among the four motor cars, the dynamic performances of the first and last moving cars were worse than those of the other motor cars. Among the two trailers, the trailer at the back was worse than the trailer at the front. The traction transmission system has a greater impact on the vertical and lateral vibration of the train bogie frame and wheelset, but the impact on the vibration of the car body is negligible. This paper provides theoretical support for the research one train dynamic performance optimization and operation safety.

Effect of tool tilt angle on microstructure, mechanical properties and fracture behavior of dissimilar friction stir lap welding joint of SiCp/ZL101 and ZL101

In order to achieve the lightweight goal of urban rail train braking system, friction stir lap welding (FSLW) was used to weld SiCp/ZL101 and ZL101 sheets to prepare a new type of brake disc material. The effects of different tool tilt angles (TTAs) on microstructure and mechanical properties of the welding joints were systematically studied. The TTA had a significant effect on the microstructure and defect formation of the dissimilar FSLW joints. There were tunnel defects under the advancing side at 1° and 2°. Internal defect-free weld structures were formed at 3° and 3.5°. A large number of dislocations caused by plastic deformation remained in the microstructure of the weld region at 1° and 2°, and the transformation of the microstructure of the weld region at 3° and 3.5° was dominated by dynamic recovery and dynamic recrystallization. The distribution of SiC particles in the nugget area was uniform, and the grains were obviously refined. With the increase of the TTA, the average grain size of the nugget area increased, the average size of the SiC particles decreased, and the average hardness of the nugget area increased. The four samples all formed a transition zone with good metallurgical bonding at the welding interface. The shear strength of the welding joints increased first and then decreased with the increase of the TTA. In the absence of welding defects, the shear strength reached the best level of 130.71 MPa at 3°. Both shear and tensile fractures were ductile fractures.

Energy-Efficient Optimization Method of Urban Rail Train Based on Following Consistency

Because of the short distance between stations in urban rail transit, frequent braking of urban rail trains during operation will generate a large amount of regenerative braking energy. Urban rail trains can reduce their actual traction energy consumption using regenerative braking energy. Therefore, an energy-efficient optimization method for urban rail trains is proposed. By taking the punctuality of trains as the premise, the weighted acceleration of trains is taken as the synergetic variable, the synergetic coefficient is introduced to construct the following consistency model, and its convergence is proved. By analyzing the influencing factors of the following consistency coordination time, an adaptive parameter adjustment strategy is designed to solve the latest secondary traction time and the corresponding maximum speed of the primary traction. In order to save communication resources, the event trigger function is used to construct trigger conditions, and the consistency algorithm is used to update the cooperative controller. The simulation results show that the weighted acceleration of the follower train achieves the following consistency on the premise of ensuring punctuality, and the actual traction energy consumption of the follower train is reduced by 5.73%. The proposed method provides a new strategy for the energy-efficient operation of urban rail trains.

Condition Monitoring Method for Heatsink of Traction Inverter Using ANN and OWD

This article proposes a novel condition monitoring method for heatsink of traction inverter of urban rail train by detecting the change of temperature dynamic curves. Artificial neural network is used to predict the reference curves of healthy state under various working conditions, and the differences between temperature dynamic curves are extracted by one-way distance algorithm. This method is suitable for the condition monitoring of heatsink of urban rail train, because first it meets the diversity of operation conditions and nonthermal equilibrium state of traction inverter. Moreover, its implementation does not cause intrusion to the original system, and can realize online monitoring. Finally, the effectiveness is experimentally verified.

Automatic Obstacle Detection Method for the Train Based on Deep Learning

Automatic obstacle detection is of great significance for improving the safety of train operation. However, the existing autonomous operation of trains mainly depends on the signaling control system and lacks the extra equipment to perceive the environment. To further enhance the efficiency and safety of the widely deployed fully automatic operation (FAO) systems of the train, this study proposes an intelligent obstacle detection system based on deep learning. It collects perceptual information from industrial cameras and light detection and ranging (LiDAR), and mainly implements the functionality including rail region detection, obstacle detection, and visual–LiDAR fusion. Specifically, the first two parts adopt deep convolutional neural network (CNN) algorithms for semantic segmentation and object detection to pixel-wisely identify the rail track area ahead and detect the potential obstacles on the rail track, respectively. The visual–LiDAR fusion part integrates the visual data with the LiDAR data to achieve environmental perception for all weather conditions. It can also determine the geometric relationship between the rail track and obstacles to decide whether to trigger a warning alarm. Experimental results show that the system proposed in this study has strong performance and robustness. The system perception rate (precision) is 99.994% and the recall rate reaches 100%. The system, applied to the metro Hong Kong Tsuen Wan line, effectively improves the safety of urban rail train operation.