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To study the braking performance of linear eddy current brake (LECB) for high-speed maglev trains, experimental studies are usually carried out using a track wheel with a finite radius and a track wheel eddy current brake (TWECB) with a certain radius of curvature. However, the geometrical differences between the two braking methods due to the track radius will lead to differences in their effective excitation air gap during operation. Therefore, the excitation air gap model and eddy current braking force model of TWECB and LECB are developed in this study. Through the excitation air gap model and eddy current braking force model proposed in this paper, the difference in braking performance between TWECB and LECB in the velocity domain and operating air gap domain can be obtained for different track radii. The correctness of the finite element analysis (FEA) method is verified by experimental data, and the eddy current density, braking force and air gap flux density formed by eddy current braking at different track radii are analysed using the finite element method. The results show that an appropriate test track radius can significantly improve the accuracy of TWECB in estimating the braking performance of LECB. [ABSTRACT FROM AUTHOR]

The hunting motion of the vehicle increases the wear of the wheel tracks and affects the lateral stability of the vehicle system and safety, so the hunting motion of the vehicle needs to be recognized. However, the existing recognition methods ignore the small-amplitude hunting motion that precedes the onset of the running motion. In addition, due to the scarcity of real hunting data of high-speed trains, there is a problem of underfitting when training with traditional deep learning methods. In this paper, first, the dynamics model of a high-speed train is established, and the normal, small-amplitude hunting and hunting motions of high-speed trains in the process are simulated. Second, this paper proposes a transfer learning-based method for high-speed train hunting motion recognition. The method uses easily collected normal data samples in the training process, does not use real data samples of small-amplitude hunting and hunting motions, and completes the high-speed train running motions recognition task by transferring from simulation data (source domain) to real data (target domain). Finally, the validation is carried out using the real data, which proves that the method-related approach has some engineering application value in the intelligent monitoring of high-speed trains. [ABSTRACT FROM AUTHOR]

Traditional control algorithm of shutting down the air ducts for a fixed period is not applicable to take both the riding comfort and the air quality inside high-speed train carriages into account in long tunnels. Inspired by the morphological similarity of the tunnel pressure waves generated by the same train passes through the same tunnel, an upgraded iterative learning control algorithm for suppressing the air pressure variation excited by the quasi-periodic varying-amplitude tunnel pressure wave is developed. Firstly, the mathematical model of the control system is established, in which the air ducts, gaps and random interferences are considered. Then, the methodology of determining the goal in each iteration is formed, and the implementation of the iterative learning control algorithm is discussed. Finally, simulations of the algorithm are carried out. The simulation results show that in the upgraded iterative learning control algorithm, both the goal and the output of the air pressure inside the carriage will converge into a range determined by the amplitude and random interferences. By comparing with the traditional control algorithm, the upgraded iterative learning control algorithm is more adaptable to meet the needs of riding comfort. [ABSTRACT FROM AUTHOR]

Based on the characteristics that the tunnel pressure wave has a fixed-morphologic form when the same train passes through the same tunnel, an applicational approach based on the iterative learning control (ILC) is developed, aiming at overcoming the drawbacks of the traditional strategy for controlling the air pressure variation inside a high-speed train carriage. To achieve the goal, the control system is mathematically modelled. Then, the problem is formulated. The task of suppressing the influence of the tunnel pressure wave on the air pressure inside the carriages is shifted as an ILC problem of tracking the comfort index with varying trial length. The algorithm of refreshing the control signal from trial to trial is determined and the process of ILC control is designed. Next, the convergence of the newly-developed applicational ILC algorithm is discussed and the algorithm is simulated by the simulation signal and field-test signal. Results show that the applicational ILC algorithm be more adaptable in handling the control of the air pressure inside carriage under the excitation of varying-amplitude, varying-scale and varying-initial-states tunnel pressure wave. Meanwhile, the matching with tunnel pressure wave makes the applicational ILC algorithm will take both the riding comfort and fresh air into consideration, which upgrades the performances when the high-speed train passing through long tunnels. [ABSTRACT FROM AUTHOR]

To break through the limitation of conventional iterative learning control algorithm (ILC) that requires a fixed target, a new ILC algorithm is designed for tracking the varying-amplitude but morphologically similar targets. First, the problem is formulated, in which the tracking target is selected as varying-amplitude and biased-measured. Then, the process of the ILC algorithm based on a variable-gain-proportional-integral scheme is discussed, in which the magnitude coefficient is defined and calculated by orthogonal projection and utilised to redefine the error and build the refreshment algorithm of the input. Next, the convergence of the algorithm is analysed and the applicational simulation is conducted. Results show that the new ILC algorithm has the ability in dealing with the trajectory of the varying but morphologically similar target and the applicational scenario of the ILC algorithm is expanded. [ABSTRACT FROM AUTHOR]

To solve some problems caused by the wake of a high-speed train, the control process, especially the mechanism of the non-smooth surface for controlling the wake of a high-speed train, is studied by using the detached eddy simulation. The numerical simulation method is validated by using the data from a wind tunnel experiment reported in literature. To study the passive control of the wake, the non-smooth surface is arranged on the front surface and the side surface of the train, respectively. The changes of the vortex structure, velocity, and aerodynamic drag before and after the control are presented, and then the flow control mechanism of the non-smooth surface is explored in detail. The results show that by arranging the non-smooth surface on the front and side surface of the tail car, the vortex motion in the tail of the train can be slowed down and the influence region of the wake can be shortened. At the same time, the velocity fluctuation amplitude in the wake region of the train apparently decreases and the aerodynamic drag of the train decreases after the control. [ABSTRACT FROM AUTHOR]

Hunting monitoring is very important for high-speed trains to achieve safe operation. But all the monitoring systems are designed to detect hunting only after hunting has developed sufficiently. Under these circumstances, some damage may be caused to the railway track and train wheels. The work reported in this paper aims to solve the detection problem of small amplitude hunting before the lateral instability of high-speed trains occurs. But the information from a single sensor can only reflect the local operation state of a train. So, to improve the accuracy and robustness of the monitoring system, a multi-sensor fusion framework for detecting small amplitude hunting of high-speed trains based on an improved Dempster–Shafer (DS) theory is proposed. The framework consists of a series of steps. Firstly, the method of combining empirical mode decomposition and sample entropy is used to extract features of each operation condition. Secondly, the posterior probability support vector machine is used to get the basic probability assignment. Finally, the DS theory improved by the authors is proposed to get a more accurate detection result. This framework developed by the authors is used on high-speed trains with success and experimental findings are provided. This multi-sensor fusion framework can also be used in other condition monitoring systems on high-speed trains, such as the gearbox monitoring system, from which nonstationary signals are acquired too. [ABSTRACT FROM AUTHOR]