Subway Vehicle Research Articles

Study on radial deviation amplitude of out-of-roundness of resilient wheel installed on a subway vehicle based on vehicle operation safety regulation

Following the extensive use of resilient wheels on trams, several subways in China have also started to pre-launch testing of resilient wheels, while it remains to be at a beginning stage, the wheel maintenance during its long-term service has become a great concern by subway operators and wheel suppliers. An effort is presented in this paper, trying to provide an invaluable reference. Firstly, a metro vehicle-track dynamic system model fitted with the resilient wheels is built. Then, based on the operation safety index and current wheel re-profiling regulations, the influence of wheel axial and radial stiffness combination on radial deviation maximum of resilient wheel polygonization wear is analyzed. The equivalent stiffnesses of the resilient wheel involve the radial stiffness within 600 kN/mm and the axial stiffness within 100 kN/mm. The amplitude of polygonization below the 10th order on the resilient wheel is within 1 mm. A contour chart is drawn to present how the wheel weight reduction rate varies with wheel polygonization order and amplitude under different combinations of resilient wheel equivalent axial and radial stiffnesses. A series of contours is abstracted to query the amplitude limits of the polygonization of different orders on resilient wheels with different equivalent stiffnesses.

Modal analysis and frequency matching study of subway bogie frame under ambient excitation

A wealth of practical cases indicates that the fatigue failure of subway bogies primarily stems from the modal resonance of the structure. If the modal characteristics of the entire vehicle, including equipment and bogies, are mismatched, rail vehicles may experience abnormal vibrations and noise. Therefore, it is imperative to conduct modal analysis and matching design for subway vehicle bogies to ensure smooth operation, reduce structural vibrations and noise, and enhance vehicle safety and ride comfort. Modal identification methods under the ambient l excitations during vehicle operation were employed to identify the modal parameters of the bogie structure before and after wheel reprofiling and under different load conditions. According to the test results, wheel reprofiling has minimal impact on the modal parameters of the structure, but with an increase in load, the modal frequencies of each order generally increase. This is associated with boundary constraint states, such as the increased stiffness of the bogie air spring with an increase in vehicle load. By comparing the test results with simulation analysis results of the bogie structure under free and constrained states, it is evident that simulating realistic boundary constraint conditions is crucial to ensure the accuracy of the finite element model. Based on frequency isolation criteria and vibration isolation theory, a frequency planning basis for the bogie structure was established. The study found that as the vehicle load increases, changes in the boundary conditions of the bogie affecting the elastic modal frequencies of the structure may have a certain impact on matching design, and may even better comply with the requirements of frequency management equations. This provides a new direction for subsequent scholars researching modal matching design.

Compilation of wheel-rail comprehensive irregularity spectrum for subway vehicle

The irregularity excitation experienced by subway vehicles is mainly the result of the interaction between the track and wheel. However, in the early system design and simulation analysis of subway vehicles, most only used the traditional standard track irregularity spectrum as the input excitation, ignoring or underestimating the contribution of the wheel irregularity. Based on our statistical analysis of 200,000 kilometers of tracking test data of subway vehicle wheel irregularities, we found that the short-wave irregularity caused by the wheels far exceeds the traditional standard track irregularity. The service condition of the vehicle is seriously affected, especially in the final stage of a wheel re-profile period. To address the above issues: Firstly, the sensitive wavelength range (16. 67∼2500 mm) of subway vehicles was derived based on the axle box acceleration spectrum of IEC61373: 2010, which was very close to the wavelength range (50∼2627 mm) of the wheel irregularity spectrum proposed later, demonstrating the importance of compiling a wheel irregularity spectrum; Secondly, based on the large number of tracking test data of wheel out-of-roundness, a calculation method of the wheel irregularity quantile spectrum under the Johnson non-normal transformation system was proposed; Thirdly, according to the different stages of the wheel re-profile period, the wheel irregularity spectrum is introduced to correct the short-wave segments of the traditional standard track irregularity spectrum to compile a wheel-rail comprehensive irregularity spectrum.

Energy Absorption Characteristics and Parameter Optimization of Anti-Climb Energy-Absorbing Devices for Subway Vehicles under Impact Loads

To further enhance the crashworthiness of subway vehicle anti-climb energy-absorbing devices, this paper proposes a novel collapsible structure, which is embedded with honeycomb aluminum blocks and consists of an inner and outer double-layer square tube. An impact finite element model of the subway vehicle’s anti-climb energy-absorbing device was established using LS-DYNA (v.17.2). The effects of the connecting diaphragm thickness, the inner and outer tube thicknesses of the thin-walled tubes, and the yield strength of the honeycomb aluminum on the energy absorption and impact force of the anti-climb energy-absorbing device were investigated. The results indicate that changes in the inner and outer tube thicknesses of the thin-walled tubes significantly affect the energy absorption and impact force of the anti-climb energy-absorbing device. However, changes in the connecting diaphragm thickness only increase the maximum energy absorption by up to 7.5% but have a significant impact on the maximum peak force. It was also found that the difference in energy absorption due to changes in the yield strength of the honeycomb aluminum is only 3.6%, suggesting that the yield strength of the honeycomb aluminum and the connecting diaphragm thickness have a limited influence on the crashworthiness design of the anti-climb energy-absorbing device. Furthermore, a multi-objective surrogate model characterizing the specific energy absorption and maximum peak force was established for the aforementioned four influential parameters using the response surface method. The model was then optimized using a genetic algorithm, resulting in optimal parameters that increased the energy absorption and specific energy absorption by 27.3% and 13.8%, respectively, compared to the original values, leading to a significant improvement in crashworthiness. The findings provide valuable references for and insights into the crashworthiness design and optimization of subway vehicles’ anti-climb energy-absorbing devices.

Improved YOLOv7-Based Algorithm for Detecting Foreign Objects on the Roof of a Subway Vehicle

Subway vehicle roofs must be inspected when entering and exiting the depot to ensure safe subway vehicle operations. This paper presents an improved method for detecting foreign objects on subway vehicle roofs based on the YOLOv7 algorithm. First, we capture images of foreign objects using a line-scan camera at the depot entrance and exit, creating a dataset of foreign roof objects. Subsequently, we address the shortcomings of the YOLOv7 algorithm by introducing the Ghost module, an improved weighted bidirectional feature pyramid network (WBiFPN), and the Wise intersection over union (WIoU) bounding-box regression loss function. These enhancements are incorporated to build the subway vehicle roof foreign object detection model based on the improved YOLOv7, which we refer to as YOLOv7-GBW. The experimental results demonstrate the practicality and usability of the proposed method. The analysis of the experimental results indicates that the YOLOv7-GBW algorithm achieves a detection accuracy of 90.29% at a speed of 54.3 frames per second (fps) with a parameter count of 15.51 million. The improved YOLOv7 model outperforms mainstream detection algorithms in terms of detection accuracy, speed, and parameter count. This finding confirms that the proposed method meets the requirements for detecting foreign objects on subway vehicle roofs.

Research and practice on a new flexible quasi-static test platform for railway vehicle anti-collision posts

ABSTRACT Aming at the shortcomings of the existing crushing test platform for rail vehicles, a design strategy was proposed and a new flexible quasi-static test platform was developed. The elastic-plastic performance of the anti-collision posts of a typical subway vehicle was experimentally investigated based on the developed test platform. The obtained results show that the proposed design strategy is correct and feasible for guiding the design of the flexible quasi-static test platform. The developed new test platform equips with the functions of elastic-plastic tests for anti-collision posts and has good stability with maximum relative displacement of 1.52 mm and 2.22 mm in central collision/corner post elastic-plastic test. The central collision/corner post of the typical subway vehicle has good elastic-plastic performance and could meet the requirements of ASME RT-2-2014. This study provides practical references for the development of a quasi-static crushing test platform for rail vehicle-end structures.

Machine learning-based crashworthiness optimization for the square cone energy-absorbing structure of the subway vehicle

Machine learning-based crashworthiness optimization for the square cone energy-absorbing structure of the subway vehicle

Experimental Research on Vibration Dynamics Performance of Type B Metro Vehicles

In recent years, due to the acceleration of urban rail transit construction, the cumulative length of urban rail transit lines in China has also continued to grow. As one of the typical representatives of urban rail transit, the subway is an excellent choice for the general public due to its energy saving, environmental friendliness, large transportation capacity, and high punctuality rate. However, this also imposes stricter requirements on the operational safety and smoothness of subway vehicles. The vibration generated by subway vehicles in operation will affect the riding experience of passengers and subway vehicles to varying degrees. Vibration research is an important part of the research on rail transit vehicles. In this paper, a dynamic performance test of a B-type subway vehicle is carried out. According to GB/T5599-2019 and other standards, the operation safety and stability of the vehicle under no-load and overloaded loads and the vehicle vibration characteristics are studied, which provides reliable experimental data for the subsequent research of the vehicle.

Transfer learning-based crashworthiness prediction for the composite structure of a subway vehicle

Due to the lack of load/displacement sensors in a complex and uncertain crash test/accident of rail vehicles (e.g., vehicle-to-vehicle or train-to-train collision), only structural deformation images can be obtained while the crashworthiness indicators (e.g., force, displacement, energy absorption) cannot be measured directly. This paper aims to propose a transfer learning-based inverse method for extracting the structural parameters and crashworthiness characteristics by the deformed pictures of energy-absorbing structures. A finite element model of an energy-absorbing structure was firstly established and calibrated by experiments. Then, a number of deformation images were captured from the numerical design of experiment (DOE) through coding languages, which were saved as TFRecord format to reduce the computational time during the training of transfer learning models (i.e., VGG16, LetNet, AlexNet and ResNet50). The result showed that the transfer learning model, ResNet50, exhibited the best performance with R2 of 0.736 and 0.981, respectively, for predicting the structural parameters and crashworthiness characteristics. In addition, the number of full connection layers should be reasonably selected on the premise of maintaining accuracy and efficiency. A group of deformation pictures were randomly used as samples to validate the prediction of structural parameters and crashworthiness through the trained transfer learning model, where good consistence was observed. The proposed method is expected to bring the image recognition and big data prediction into the design and test of composite energy-absorbing structures, thus, auxiliary improve the crashworthiness of rail vehicles.

Study of Wheel Wear Characteristic of Subway Vehicle Based on the Effect of Friction Temperature Rising

Study of Wheel Wear Characteristic of Subway Vehicle Based on the Effect of Friction Temperature Rising

Parametric beam-based crashworthiness optimisation for the crush zone of a subway vehicle

This study proposed a method to optimise the crashworthiness for the crush zone structure of a subway vehicle based on the beam element model. In the preliminary conceptual design, a simplified beam element model of the crush zone was established to verify the feasibility of the model. Then, a parameterised modelling method for the mechanical characteristic curves of the energy-absorbing structures and the front-end beam was proposed, and a hybrid-weight prediction model of the mechanical curves was built. Based on the mapping relationship, a database of mechanical and geometric parameters of the crush zone was established. Finally, beam element models of the crush zone with different arrangement schemes were obtained through optimisation based on genetic algorithm. Multiple sets of feasible geometric structure parameters were obtained and the best scheme was determined. The research results showed that the calculation speed for the simplified beam element model of the crush zone structure was 461 times faster than that of the finite element model. The prediction model of the mechanical characteristic curve of the crush zone structure built in this study exhibited sufficient accuracy. The mapping relationship between the geometric structure and the mechanical properties enabled the optimised structure by the discrete beam model to be quickly obtained. The mechanical parameters of the optimised crush zone were obtained with energy absorption of 597.51 kJ, mean crushing force of 2398.38 kN, and crushing displacement of 249.72 mm. The proposed method is expected to provide a convenient guideline for fast optimisation and upgrade of energy-absorbing structures.

Dynamic Preventive Maintenance Optimization of Subway Vehicle Traction System Considering Stages

The current maintenance mode of the subway vehicle traction system does not consider the change of failure rate in the operation of components, and there is an unreasonable maintenance interval and high maintenance cost. To solve this problem, a dynamic preventive maintenance strategy for subway vehicle traction systems considering stages is proposed. Service age decreasing factor and failure rate increasing factor are introduced to build a new dynamic reliability model considering the effect of preventive maintenance on component failure rate. Non-preventive failure maintenance, preventive maintenance, preventive update maintenance and opportunistic maintenance are adopted. For the components in degradation or random failure stages, a preventive maintenance optimization model with the least maintenance cost, constrained by reliability, is established. The maintenance strategy is optimized and solved based on the enumeration method to obtain single-component irregular preventive maintenance intervals and multi-component group preventive maintenance times. The results show that the proposed maintenance models are reasonable and feasible. On the premise of ensuring the system reliability, the number of vehicle outages for maintenance and the maintenance cost are reduced. Feasible and effective maintenance models and methods are provided. They can ensure the reliability of subway vehicles and reduce maintenance costs. The effectiveness and economy of maintenance work is increased.

Influence of Fastener Failure on Dynamic Performance of Subway Vehicle

The track fasteners may be damaged by fatigue and impact load during long-term subway operation, resulting in the failure of the connecting components between the rail and the track plate, and the spacing of rail support becomes larger, resulting in an increase in its dynamic deformation, affecting the subway vehicle’s running performance, and, in severe cases, endangering the vehicle’s running safety. A vehicle-subway track system model was created to study the running performance of subway vehicles when fasteners failed. A multi-rigid, body spring damping system is used to describe the vehicle system. The model for the track system is created using the finite element method (FEM), and the vehicle dynamic performances under various fastener failure scenarios are calculated, as well as the vehicle’s running comfort and safety in various scenarios. The findings show that fastener failure has little impact on the vehicle’s running comfort but it has a significant impact on the vehicle’s wheel unloading ratio.

An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML

Real-time identification of the running state is one of the key technologies for a smart rail vehicle. However, it is a challenge to accurately real-time sense the complex running states of the rail vehicle on an Internet-of-Things (IoT) edge device. Traditional systems usually upload a large amount of real-time data from the vehicle to the cloud for identification, which is laborious and inefficient. In this paper, an intelligent identification method for rail vehicle running state is proposed based on Tiny Machine Learning (TinyML) technology, and an IoT system is developed with small size and low energy consumption. The system uses a Micro-Electro-Mechanical System (MEMS) sensor to collect acceleration data for machine learning training. A neural network model for recognizing the running state of rail vehicles is built and trained by defining a machine learning running state classification model. The trained recognition model is deployed to the IoT edge device at the vehicle side, and an offset time window method is utilized for real-time state sensing. In addition, the sensing results are uploaded to the IoT server for visualization. The experiments on the subway vehicle showed that the system could identify six complex running states in real-time with over 99% accuracy using only one IoT microcontroller. The model with three axes converges faster than the model with one. The model recognition accuracy remained above 98% and 95%, under different installation positions on the rail vehicle and the zero-drift phenomenon of the MEMS acceleration sensor, respectively. The presented method and system can also be extended to edge-aware applications of equipment such as automobiles and ships.

Numerical simulation of the frictional heat problem of subway brake discs considering variable friction coefficient and slope track

Friction heating during braking may causes premature wear, thermal cracks and other damages which could weak the braking performance of subway vehicles. It is therefore important to determine the temperature field of the brake disc. The purpose of this paper is to propose a one-dimensional brake disc model and apply a new solution algorithm to obtain the temperature field of the brake disc, which is validated by the Renk Dynamometer test bench. The advantage of this numerical model is that the variable friction coefficient and slope are taken into account. The experimental dependency of the coefficient of friction on the velocity is approximated by interpolation and applied to the numerical model. The calculation is based on two cases of constant and velocity-dependent friction coefficient (COF) by using the method of lines (MOL), which has the advantage of shorter computation time and improved accuracy. The numerical results agree well with the experimental data. It was proved that disregarding the velocity dependence of the coefficient of friction, the maximum temperature during the entire braking process was underestimated by 15%. In addition, the subway vehicle braking on slope should decrease the speed limit appropriately to reduce the maximum temperature of the brake disc. This study can provide a theoretical basis for further fatigue study of brake discs.

Actual-time modeling of a subway vehicle and Optimal driving management with GA and ABC algorithms

The optimization of operations of subway systems has critical importance in terms of energy efficiency and costs. Therefore, driving management of subway vehicles has been gaining more importance day by day. Optimal Driving Management (ODM) is the optimization of the velocity trajectory of a subway vehicle by considering operating conditions and travel time. In this study, the driving of a subway vehicle has been modeled dynamically with all parameters that affect driving. So, a realistic model has been prepared. Then, a new objective function has been proposed to reduce energy consumption by using the subway vehicle’s acceleration and braking forces parameters for ODM. The Artificial Bee Colony algorithm (ABC) and Genetic algorithm (GA) have been used on the prepared model to determine the driving dynamics of the subway vehicle. The performance of the algorithms has been evaluated in the real line network, which has multiple stations with different characteristics. The energy consumption has been reduced by 10.47% in GA and 8.92% in ABC compared to the actual driving values. Moreover, the results of the study has been analyzed in terms of passenger comfort, cost, and emission values.

Nonlinear seismic analysis of a train-tunnel-soil system and running safety assessment of metro vehicles

Since earthquakes would pose a serious threat to the safety of underground railway tunnels and subway trains, the seismic analysis of the subway tunnel and the metro train is essential for earthquake-resistant design and construction. This presented study aims to investigate the transient response of a train-tunnel-soil coupled system under various load conditions, and assess running safety of the moving train. Firstly, a simulation model of the train-tunnel-soil system was established, considering interactions between soil and tunnel, rail and train. Secondly, the numerical approaches employed in this paper were verified, and the tunnel-soil model was validated with the available test data. Finally, the nonlinear seismic response of the train-tunnel-soil system is investigated, and running safety indices of the subway vehicle are evaluated. Moreover, the effect of train speed and earthquake intensity on the running safety of the metro train is assessed. The numerical results reveal that compared to the moving-train load, the effect of earthquake action on the dynamic response of the subway tunnel is more prominent, resulting in a significant increase of the wheel-rail force and acceleration of the railway vehicle. Both earthquake loads and train speed have an impact on running safety indices, while the earthquake intensity has a more significant effect on the safety index of the metro vehicle. Moreover, it is worth noticing that with respect to the designed operational speed of 90 km/h for Nanjing Metro Line 10, the ability of the metro train to withstand earthquake excitation is no more than a maximum acceleration of 0.2 g.

An Improved Load Spectrum Extrapolation Method of Railway Vehicle Bogie Frame

This article is aimed to seek for a precise extrapolation method on railway vehicle bogie frame load spectrum based on real engineering problem in China Standard EMU and Beijing Subway vehicle. The most common used methods, exceeding probability and standard load spectrum with cumulative frequency, are discussed. An improved method that excludes trivial load amplitude while calculating cumulative frequency is proposed. The three methods are deployed with real measured data and then compared with the ground truth. The comparison result shows that the improved method introduced in this article has the minimum error of the estimated load maxima and least deviation from ground truth load spectrum.

A Dual-Band Low-Power-Consumption Active RFID Tag Based on a Meander FPCB Antenna for Subway Vehicle Management

A meander microstrip antenna was designed to operate in the UHF and microwave bands. Laser direct structuring (LDS) and the flexible printed circuit board (FPCB) method were used to fabricate the antenna, and their results were compared. A dual-band low-power-consumption active radio frequency identification (RFID) tag based on the present antenna was developed and fabricated for an automated subway vehicle maintenance system. The recognition distance of the active tag is more than 20 m at 900 MHz and more than 170 m at 2.4 GHz, while the recognition rate is >95% at 900 MHz and 100% at 2.4 GHz. The 100% recognition rate was obtained when the RFID readers were placed in a line parallel to the subway track. Therefore, we have shown that our active RFID tag can be used in a subway vehicle management system.

Influence of Wheel Profile Wear Coupled with Wheel Diameter Difference on the Dynamic Performance of Subway Vehicles

In view of the coexistence of wheel profile wear (WPW) and wheel diameter difference (WDD) on an actual subway line, a dynamic analysis method based on coupling between WPW and equivalent in‐phase WDD was proposed. Based on the measurements from a subway vehicle in operation on this line, dynamics modeling and calculations were performed for a single carriage of this vehicle. Later, the interaction between the effects of WPW and equivalent in‐phase WDD on the vehicle dynamic performance was analyzed, and the dynamic response in the presence of coupled damage was compared between the outer and inner wheels. Furthermore, the difference in the dynamic response caused by different positions of the larger‐diameter wheels (i.e., on the inner track or outer track) was analyzed for the case where equivalent in‐phase WDD occurred between the front and rear bogies. The results show that when the vehicle ran on a straight line, the coupling between WPW and WDD reduced the vehicle’s stability but improved its ride comfort. When the vehicle traveled on a curved line, it showed reductions in the lateral wheel/rail contact force, derailment coefficient, axle lateral force, and wear index if the outer wheels had a larger diameter. As a result, the deterioration of the vehicle’s dynamic performance due to the increasing degree of WPW slowed down, and its curve negotiation performance improved. Meanwhile, the outer wheels had significantly greater lateral wheel/rail contact force, derailment coefficient, and wear index compared to the inner wheels. When a −1 mm WDD was coupled with the worn wheel profile for 14 × 104 kilometers traveled, the dynamic performance indexes of the vehicle were close to or even exceeded the corresponding safety limits. The findings can provide technical support for subway vehicle maintenance.