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In this paper, the noise reduction performance of the active steering bogie, developed to address wheel and rail wear and reduce noise during train operation in curved sections, was evaluated. A prototype of the active steering bogie was installed on the test train, and noise reduction performance by active steering control was measured in curved sections of a commercial line with a radius of curve of 300 m or less. The results of the test run confirmed that both the wheel and car interior noise were reduced when active steering control was applied, compared with the case where it was not applied. According to the test results in the curved section with the most noise reduction effect, the equivalent noise level was reduced by 6.6 dB for wheels and 4.7 dB for the car body by active steering control. In addition, analyzing the frequency characteristics by wheel steering control has confirmed that the squeal noise mode generated in the 300 to 2000 Hz band during curve running was significantly reduced. Therefore, the noise reduction performance of the active steering bogie, developed to improve steering performance in curved sections, has been sufficiently verified through the test results. [ABSTRACT FROM AUTHOR]

This article investigates a 27-degree-of-freedom dynamic model of a full-scale railway vehicle. The controlling action is performed by developing a decentralized control structure with five independent Fraction Order Proportional Integral Derivative (FOPID) controllers three types of random track irregularities, vertical profile, lateral alignment, and cross-level. A novel metaheuristic optimization technique named hybrid Particle Swarm Optimization-Gray Wolf Optimization (hybrid PSO-GWO) is proposed to optimize active force for an active suspension system. The simulated results show that the hybrid metaheuristic algorithm outperforms with a significant reduction in vehicle vibration ensuring enhanced ride comfort of the car body. [ABSTRACT FROM AUTHOR]

The air spring for railway vehicles uses the air pressure inside the bellows to absorb vibration and shock to improve ride comfort and adjust the height of the underframe with a leveling valve to control stable driving of the train. This study developed augmented reality content that proposes a novel visual technology to effectively support the training of air spring maintenance tasks. In this study, a special effect algorithm that displays the dispersion and diffusion of fluid, and an algorithm that allows objects to be rotated at various angles, were proposed to increase the visual learning effect of fluid flow for maintenance. The FDG algorithm can increase the training effect by visualizing the leakage of air at a specific location when the air spring is damaged. In addition, the OAR algorithm allows an axisymmetric model, which is difficult to rotate by gestures, to be rotated at various angles, using a touch cube. Using these algorithms, maintenance personnel can effectively learn complex maintenance tasks. The UMUX and CSUQ surveys were conducted with 40 railway maintenance workers to evaluate the effectiveness of the developed educational content. The results showed that the UMUX, across 4 items, averaged as score of 81.56. Likewise, the CSUQ survey score, consisting of 19 questions in 4 categories, was very high, at 80.83. These results show that this AR content is usable for air spring maintenance and field training support. [ABSTRACT FROM AUTHOR]

Comfort has traditionally been assessed as a single deterministic index that can be assigned to each railway vehicle type (at a given speed on a given track quality level). However, it is now well established that passengers influence vibration and, as a consequence, occupancy level and seating arrangement influence comfort, thus turning it into a stochastic variable. The concept of Compound Comfort, presented in Palomares et al. [Is the standard ride comfort index an actual estimation of railway passenger comfort? Veh Syst Dyn. 2022;1–14. doi: ], is used in this paper to evaluate several train types. Simplified models are used as a first approximation for vibration analysis. Despite model simplicity, since the number of possible seating arrangements is intractable, a Monte Carlo procedure is used to obtain compound comfort probability density distributions from a randomised subset of cases. The results from the work presented here will show that the information provided by stochastic distributions is much richer than a single deterministic index. Nevertheless, obtaining distributions from Monte Carlo tests (numerical or experimental) is impractical for any commercial assessment of comfort. Therefore, an inference procedure is called for and has been devised in order to obtain probability density function parameters from just one tare test, along with ten runs of a half laden test. This recipe has been adapted from one presented previously to be able to accommodate the skewed distributions that emerge when a wide range of train configurations is considered. [ABSTRACT FROM AUTHOR]

Recently, research on condition-based maintenance (CBM) using artificial intelligence has attracted attention to reduce the maintenance costs of railway vehicles. The air compressor of a railway vehicle that adopts an air brake system is a target for CBM, and in order to reduce maintenance costs and ensure driving stability, technology to detect anomalies in the air compressor is necessary. The long short-term memory (LSTM) autoencoder is used to process sequence data. However, LSTM has limitations in that it cannot perform parallel processing due to its recurrent neural network characteristics, and it is difficult to learn the dependency between data as the sequence data lengthens. In this paper, we propose a novel transformer architecture as an anomaly detection model to learn dependency between air compressor data of a railway vehicle and we demonstrate superior data reconstruction and generalization ability of proposed transformer. We conduct simulation tests based on actual railway vehicle air compressor data and confirm improved anomaly detection performance. We propose an anomaly score definition method using mean squared error to perform reconstruction error-based anomaly detection. With the successful anomaly detection results in the air compressor of a railway vehicle, we demonstrate the effectiveness of a proposed anomaly detection algorithm that applies the moving average of anomaly scores and defines anomaly criteria using three-sigma. We test the proposed anomaly detection method using sensor data received from actual urban railway air compressors and prove its usefulness. [ABSTRACT FROM AUTHOR]

In recent years, railway vehicles have become lighter since it corresponds not only to the improvement of the running speed but also to the reduction of running cost and environmental noise. On the other hand, this causes an increase in the vertical elastic vibration of the car body and the riding comfort has deteriorated. Therefore, various strategies to reduce vertical vibration have been studied. In this study, vertical vibration reduction, by controlling the internal pressure of the air spring using µ-synthesis control, is proposed. The merit of µ-synthesis control is that it can guarantee robust performance. A controller by µ-synthesis was designed for vibration reduction of a 1/6 scale railway model and compared nominal performance and robust performance with H∞ control theory. Moreover, a feasibility study of this strategy was investigated for full-scale railway vehicles through simulation. [ABSTRACT FROM AUTHOR]

Objective As a braking mode,electromechanical brake adopts the motor controlled screw rod drive to implement the pushing action, making the brake shoe to contact with the wheel and complete the transmission of brake force and friction brake. It is necessary to study the influence of the new brake mode on the vibration performance of rail transit vehicle. Method The basic structure and the composition of the electromechanical brake and air brake are introduced. The push-out stiffness test and track vibration test of the above two brakes are carried out and the influence of the brake push-out stiffness on vehicle vibration is analyzed. Result & Conclusion The stiffness test shows that the push-out stiffness of the electromechanical brake is much higher than that of the air brake in the normal working area and shows nonlinear characteristics, because it is driven by the motor and not equipped with stomatal piston as the air brake does. In line testing when the electromechanical brake is installed on the vehicle, it is found that compared with the air brake, the electromechanical brake will aggravate the vehicle brake vibration due to the large push-out stiffness, and the vibration acceleration of key components of the vehicle will also increase. Based on the research results, it is suggested that the push-out stiffness of the electromechanical brake shall be reduced to ensure smooth and safe brake processes for vehicles.

Modifying the car body structure from the existing train to another car body type requires an analysis of several factors. The strength of the design due to overload and the durability of the structures due to operational loads need to be re-evaluated. Furthermore, stiffness analysis also needs to determine the characteristics of the structure when it is not loaded. This study numerically analyzed the considerations in selecting the structural steel profile for modification of the existing car body into a panoramic type using the ANSYS Workbench R19. The underframe structure can still be used, and other structures are modified with the UNP profile. The side wall, and roof are changed in shape and size following the glass design of the panoramic train. The solid 3D model is rebuilt into a surface model to simplify the analysis. Static structural analysis is used to clarify the strength of the design under overload, a combination of static and transient structural analysis is applied to calculate the operating life, and modal analysis is chosen to figure out the stiffness. The simulation results showed that the modified design had met the needs and requirements based on the PM 175 standard of 2015 by the Indonesian Ministry of Transportation and the international standard EN-12663.

Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel–rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We developed a driving energy simulator to study fuel cell output control of a hybrid test train. The simulator takes into account that the powering and regenerating performance of the test train depends on the SOC of the battery. The simulator was validated by confirming that it matched the driving test data. And the running simulation was carried out on a 15 km virtual route to show the efficiency and fuel consumption of the fuel cell. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the implementation of the dual extended Kalman filter (DEKF) to estimate wheelset equivalent conicity, an accurate understanding of which can facilitate the implementation of an effective model-based estimator. The estimator is developed to identify the wheelset equivalent conicity of high-speed railway vehicles while negotiating a curve. The designed DEKF estimator employs two discrete-time extended Kalman filters combining state and parameter estimators in parallel. This estimator uses easily available measurements from acceleration sensors measuring at axle boxes and a rate gyroscope measuring bogie frame yaw velocity. Two tests, including linearized and actual wheel-rail geometry, are carried out at a speed of 250 km/h with stochastic and deterministic track features using multibody simulations, SIMPACK. The results with acceptable estimation errors for both track conditions indicate adequate performance and reliability of the designed DEKF estimator. They demonstrate the feasibility of utilizing this DEKF method in rail vehicle applications as the knowledge of time-varying parameters is not only important in achieving an effective estimator for vehicle control but also useful for vehicle condition monitoring. [ABSTRACT FROM AUTHOR]

This study investigates a new method for the effective application of active secondary suspension for reducing vertical vibration of the carbody to improve ride comfort of railway vehicles. So far, most previous control methods have contributed to reducing the vibration of the rigid body modes of the carbody at approximately 1 Hz or the first bending mode at approximately 10 Hz. However , in modern high-speed vehicles, higher frequency vibrations generally have a relatively greater influence on vertical ride comfort than vibrations of around 1–2 Hz. Focusing on this fact, the authors propose active secondary suspension with feedback control of the vertical vibration velocity of the bogie frame as a method for reducing carbody vibrations in a wide frequency band above 2 Hz to improve ride comfort. Numerical simulations were carried out to simulate the actual running of a Shinkansen using a vehicle model corresponding to a Shinkansen vehicle with 14 degrees of freedom, considering the vertical and longitudinal dynamics of the vehicle. The results show that this method is mainly effective in reducing vertical carbody vibration above 3 Hz and that the frequency band for vibration reduction can be extended to 1 Hz or lower by combining this method with feedback control of the vertical displacement of the bogie.

The gear transmission system is one of the essential subsystems of railway vehicles. The wheel polygonalization can affect the gear transmission system's condition monitoring and fault diagnosis, causing fatigue failure of the gear system and influencing driving safety. Therefore, to explore the mechanism of the wheel polygonalization in the gear system, this paper develops a Lagrangian form dynamic model of a railway vehicle with a gear transmission system. The model validation uses the data from the field test. The analysis explores the influence of the wheel polygonal fault on the gear transmission system under the traction condition. The results indicate that the wheel polygonal fault has amplitude/frequency modulation effects on the time/frequency response of the gear system. Also, the wheel polygonal fault causes the envelope of the speed curve to fluctuate periodically at a low frequency under traction conditions. The waveform asymmetry is gradually apparent as the traction continues. In the non-resonant region, the statistical analysis of the rotational speed fluctuation illustrates that the order of the wheel polygonal fault has more impact on the rotational speed fluctuation than the amplitude. Besides, in the resonance region, the eigenvalues of the system determine the magnitude of the amplitude. [ABSTRACT FROM AUTHOR]

It is a challenging task to accurately diagnose a railway bearing fault since bearing vibration signals are under strong interferences from wheel–rail excitations. The commonly used Kurtogram-based methods are often trapped in components induced by the wheel–rail excitations while adaptive mode decomposition methods are sensitive to input control parameters. To address these issues, based on a recently developed powerful signal decomposition method, that is, adaptive chirp mode decomposition (ACMD), a novel method called bandwidth-aware ACMD (BA-ACMD) is proposed in this article. First, the filter bank property of ACMD is thoroughly analyzed based on Monte-Carlo simulation and then a bandwidth expression with respect to the penalty parameter is first obtained by fitting a power law model. Then, a weighted spectrum trend (WST) method is proposed to partition frequency bands and then guide the parameter determination of ACMD through the integration of the obtained bandwidth expression. In addition, according to the order of magnitude of the WST in each band, the BA-ACMD adopts a recursive framework to extract signal modes one by one. In this way, dominating signal modes related to wheel–rail excitations can be extracted and then subtracted from the vibration signal in advance so that the bearing faults induced signal modes can be successfully identified. Both simulation and experimental validations are conducted showing that BA-ACMD can effectively detect single and compound faults of railway bearings under strong wheel–rail excitations. [ABSTRACT FROM AUTHOR]

The most general purpose of the current paper is to trace and discuss the history and state of the art of studies on vehicle motion (dynamics) in a transition curve above the critical velocity, with the aim of potentially increasing the circle of researchers involved in studying this issue and strengthening the will of the authors to continue their studies. This general goal is achieved in two ways: first, through a profiled literature analysis, showing the historical progress and current state of the research; and second, through reference to the history of stability studies as an example of selected studies' development. In addition, this work has two more specific goals. Together, they consist of collecting the literature in a related field in one place and analyzing it on site to accomplish the review. Both specific goals are attained by dividing the literature into two corresponding parts. In the first part, the current issues of rail vehicle stability are analyzed and divided into four problems. The second part includes works that deal with the subject of the motion and dynamics of a rail vehicle on a transition curve section. Here, the works are divided into five groups and discussed. They are put in order from the closest to the furthest from this paper's main subject; however, the last group includes the most recent references. In addition, information on the authors' approach to the problem is provided, including the methods and models used, as well as example results. Based on the analysis of the literature and the state of the art, a summary of the analysis is presented at this paper's end. It highlights the small number of works on the subject of interest, and based on the review of stability studies, it seeks to encourage present and potential authors to study this field and share their results with society. [ABSTRACT FROM AUTHOR]

To effectively implement condition-based maintenance for the air compressor in a railway vehicle, a thorough understanding of its fault characteristics and the collection of abnormal data is crucial. However, obtaining sufficient abnormal data from actual railway vehicles is challenging due to the rarity of air compressor failures in commercial railway services. This paper presents a detailed mathematical model of a two-stage piston air compressor that takes into account heat transfer effects. The mathematical model and the fault characteristics of the air compressor are analyzed through MATLAB simulation studies. The accuracy of the proposed model and analysis is verified by comparing it to real pressure data collected from a commercial Seoul subway train. The paper also generates abnormal data through simulations for specific fault conditions, including discharge valve leakages, air cooler malfunctions and pressure switch malfunctions. [ABSTRACT FROM AUTHOR]

The effects of structural nonlinearity (including rubber material and contact boundary nonlinearities) and variable wheel/rail contact point on the dynamic characteristics of resilient wheels are studied to investigate the mechanical properties of these wheels. Primarily, static and dynamic tests are designed to determine the nonlinear constitutive relationship of rubber materials in resilient wheels, and the viscoelastic properties of rubber are discussed. On this basis, the mapping relationship between the elastic modulus and stiffness of rubber in a resilient wheel system is deduced, and the stiffness characteristics of viscoelastic rubber materials are determined. The dynamic models of four types of wheels namely, a solid wheel (SW), a resilient wheel that considers linear rubber (RWL), a resilient wheel that considers nonlinear rubber (RWNL), and a resilient wheel that considers nonlinear rubber and contact boundary (RWNC), are established on the basis of the Yeoh constitutive model for hyper-elastic materials. The changes in wheel/rail contact point and wheel/rail force during train running are obtained under long/short wave irregularity excitation by adopting an established vehicle–track coupled dynamic model. Then the nonlinear dynamic behavior of resilient wheels subjected to varying wheel/rail contact point and wheel/rail force is studied. Finally, the influences of rubber material parameters on the dynamic characteristics of resilient wheels are explored. Results show that the acceleration of a resilient wheel is effectively reduced compared with that of SW. Resilient wheel acceleration that considers variable wheel/rail contact point is larger than that without considering the change in wheel/rail contact point. The deformation rates of rubber subjected to variable and constant wheel/rail contact behavior are 7 and 10%, respectively, and the midpoint deformation of rubber is less than its endpoint deformation. Compared with that of SW, the acceleration of RWL is reduced by 10 and 17% respectively under variable and constant wheel/rail contact points, respectively. Meanwhile, the acceleration of RWNL is reduced by 9 and 7% compared with that of RWL. The influences of nonlinear material characteristics and contact boundary on the dynamic characteristics of resilient wheels are not evident. The major vibration frequencies of the four types of wheels are 3–5, 10, and 22 Hz. The vibration and deformation of resilient wheels increase with an increase in the hardness of rubber. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Mechanics is the property of Chinese Journal of Applied Mechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A comprehensive modeling strategy for studying the thermomechanical tribological behaviors is proposed in this work. The wear degradation considering the influence of temperature (T) is predicted by Archard wear model with the help of the UMESHMOTION subroutine and arbitrary Lagrangian–Eulerian (ALE) remeshing technique. Adopting the proposed method, the thermomechanical tribological behaviors of railway vehicle disc brake system composed of forged steel brake disc and Cu-based powder metallurgy (PM) friction block are studied systematically. The effectiveness of the proposed methodology is validated by experimental test on a self-designed scaled brake test bench from the perspectives of interface temperature, wear degradation, friction noise and vibration, and contact status evolution. This work can provide an effective way for the investigation of thermomechanical tribological behaviors in the engineering field. [ABSTRACT FROM AUTHOR]

This paper describes a practical method for estimating the contact condition between wheel and rail using an instrumented wheelset in a railway vehicle. In a previous study, it was confirmed that an observer based on Kalman filter can estimate wheelset motion variables from measurement data of the instrumented wheelset using a single-wheel creep-force model. However, in the previous method, there is still a problem in that a precise value of friction coefficient between wheel and rail is required as a model parameter to estimate the wheelset motion variables with high accuracy. The accuracy of the value of friction coefficient affects the accuracy of estimation values since a single-wheel creep-force model refers to the creep force and the creepage. Moreover, the decision of the creep force heavily depends on the friction coefficient. To solve the problem, this study proposes a method to estimate the wheelset motion variables and the friction coefficient simultaneously by modifying the plant model. The estimation performance of the proposed method is verified by vehicle dynamics simulations and running test data. The verification results show that the proposed method is effective in estimating the friction coefficient when the track curve radius is smaller than or equal to 600m. In addition, the proposed method can improve the estimation accuracy of the wheelset motion variables compared to the previous method.

The air spring for railway vehicles uses the air pressure inside the bellows to absorb vibration and shock to improve ride comfort and adjust the height of the underframe with a leveling valve to control stable driving of the train. This study developed augmented reality content that proposes a novel visual technology to effectively support the training of air spring maintenance tasks. In this study, a special effect algorithm that displays the dispersion and diffusion of fluid, and an algorithm that allows objects to be rotated at various angles, were proposed to increase the visual learning effect of fluid flow for maintenance. The FDG algorithm can increase the training effect by visualizing the leakage of air at a specific location when the air spring is damaged. In addition, the OAR algorithm allows an axisymmetric model, which is difficult to rotate by gestures, to be rotated at various angles, using a touch cube. Using these algorithms, maintenance personnel can effectively learn complex maintenance tasks. The UMUX and CSUQ surveys were conducted with 40 railway maintenance workers to evaluate the effectiveness of the developed educational content. The results showed that the UMUX, across 4 items, averaged as score of 81.56. Likewise, the CSUQ survey score, consisting of 19 questions in 4 categories, was very high, at 80.83. These results show that this AR content is usable for air spring maintenance and field training support.

The interior noise characteristics of railway vehicles vary depending on the position of the cabin. In general, interior noises are louder in the area directly above bogies (ends of the cabin) than in the center of the cabin. It has been known that structure-borne sounds from bogies contribute greatly to the interior noises directly above bogies. This paper describes, using measured data obtained with the rolling stock test plant at Railway Technical Research Institute (RTRI), the application of the operational transfer path analysis (OTPA) to determine the contribution ratio of each transmission path of vibration from bogies to cabin floors. It is considered that structure-borne sounds from bogies propagate mainly through the parts connecting the bogie and car body such as single-link-type traction devices (traction links), yaw dampers, and air springs. As a result of applying the OTPA and determining the contribution ratios, it has been confirmed that the contribution ratios varied depending on the position of the floor and the frequency band. In addition, the validity of the contribution has been verified by examining the linearity of input/output data and the correlations of input data using multiple coherence functions and partial coherence functions. Although a bogie vibrates rigidly in the low frequency range with the very high correlation between the vibrations of the bogie and the parts connecting the bogie and car body, the correlation becomes low in the high frequency region. Therefore, it is expected that the contribution of each transfer path can be well estimated by applying the OTPA in the high frequency range for structure-borne sounds from bogies.

Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel–rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities.

The most general purpose of the current paper is to trace and discuss the history and state of the art of studies on vehicle motion (dynamics) in a transition curve above the critical velocity, with the aim of potentially increasing the circle of researchers involved in studying this issue and strengthening the will of the authors to continue their studies. This general goal is achieved in two ways: first, through a profiled literature analysis, showing the historical progress and current state of the research; and second, through reference to the history of stability studies as an example of selected studies’ development. In addition, this work has two more specific goals. Together, they consist of collecting the literature in a related field in one place and analyzing it on site to accomplish the review. Both specific goals are attained by dividing the literature into two corresponding parts. In the first part, the current issues of rail vehicle stability are analyzed and divided into four problems. The second part includes works that deal with the subject of the motion and dynamics of a rail vehicle on a transition curve section. Here, the works are divided into five groups and discussed. They are put in order from the closest to the furthest from this paper’s main subject; however, the last group includes the most recent references. In addition, information on the authors’ approach to the problem is provided, including the methods and models used, as well as example results. Based on the analysis of the literature and the state of the art, a summary of the analysis is presented at this paper’s end. It highlights the small number of works on the subject of interest, and based on the review of stability studies, it seeks to encourage present and potential authors to study this field and share their results with society.

• This paper determines the RWRD of railway vehicles through computer vision technology. • A VPT method for predicting RWRD is presented. • The VPT method and the original UNet model are experimentally compared. • The feasibility, advantages, drawbacks and future directions for improvement of the VPT method are discussed. Relative wheel-rail displacement (RWRD) is an important physical quantity that responds to the hunting stability and running safety of railway vehicles but detecting this physical quantity is challenging during train operation due to the complex service environment and the nonlinearity of vehicle-rail system. To address this problem, this paper proposes a virtual point tracking (VPT) method to detect RWRD. First, a series of regions of interest (ROIs) in the preprocessed wheel-rail contact images are extracted using a simplified YOLO (SYOLO) model. Then, some key positions of the wheel and rail on the extracted ROIs are identified using an improved UNet (IUNet) model and characterized by virtual points. Finally, the global coordinates of the virtual points in the whole wheel-rail contact image are calculated to obtain the final RWRD. To demonstrate the feasibility of the VPT method, it is used to detect the RWRD of a vehicle operating on a full-scale roller rig. For comparison, the original UNet model is directly used to detect the RWRD. Two quantities, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are applied for quantitative analysis and the results show that the VPT method can identify the RWRD more reliably and accurately. [ABSTRACT FROM AUTHOR]

The motivation of this paper follows strictly from industrial needs to improve competitiveness in railway companies by the efficient rolling stock utilization affected by the obligatory preventive maintenances of vehicles, which cause their temporary unavailability. Nevertheless, it is economically essential to minimize the total time on maintenances of vehicles, which can be achieved in a certain range by the scheduling of transport tasks taking into account maintenance policies of the vehicles, their mileage and date since the last maintenance, and the workload related to the tasks. Therefore, to face the problem, we model the consider issue as the job scheduling problem to maximize the availability of railway vehicles under preventive maintenances. We prove that the problem is strongly NP-hard and we propose efficient heuristic and metaheuristic algorithms that are characterized by a low computational complexity and they are able to find solutions satisfying from the perspective of industrial practice. The simulations based on real-life data reveal that our approach can provide schedules, which significantly improve availability of locomotives resulting in high potential financial benefits for railway companies. • The total time of maintenance can be minimized by the scheduling of transport tasks • The railway vehicle availability problem under preventive maintenance is formulated • The considered problem is proved to be strongly NP-hard • The proposed scheduling algorithms improve availability of railway vehicles • The results obtained on real-life data are satisfying for the industrial practice. [ABSTRACT FROM AUTHOR]

The OOR (out-of-roundness) wheel is one of the main excitation sources causing vehicle vibration. However, the OOR wheel occurs randomly, indicating that the vibration behavior of a vehicle cannot be comprehensively evaluated using a deterministic approach. Thus, a probability analysis framework is proposed to obtain vehicle vibration characteristics while considering the randomness of the OOR wheel. The probability model of the random OOR wheel is derived by reducing the high-dimensional variables into a few independent variables of the radius, amplitude, and phase. Then, the vertical vehicle-track coupled system with OOR wheels is modelled. A DPIM (direct probability integral method) is further developed to analyze the evolution of excitation to response probabilities. Finally, the statistics of the random vibration of the vehicle are calculated. The proposed framework is verified using a numerical case. Results show that the PDF (probability density function) shape of the vehicle random vibration, induced by the Gaussian-distributed OOR wheel, deviates from the Gaussian distribution due to the nonlinear wheel/rail contact force. Instead, it exhibits a right-skewed shape, significantly impacting the dynamic performance. As the mean or coefficient of variation of the OOR wheel amplitude increases linearly, the reliability of the vehicle Sperling index experiences a quadratic or double-sloping decrease. Consequently, a maintenance threshold for OOR wheel amplitudes is given based on reliability considerations. Compared to Monte Carlo simulation, the proposed framework offers a computational efficiency improvement of at least one order of magnitude. [ABSTRACT FROM AUTHOR]