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The lateral vibration of the car body affects the lateral stability of high-speed trains (HSTs), and the aim of this study is to design a good control strategy to decrease the lateral vibration of the car body. First, the Maxwell model of the anti-yaw damper was transformed into a Voigt model by the equivalence method, and a modified 17-degree-of-freedom (DOF) lateral vibration model that includes the anti-yaw damper was established. After the design difficulty and control accuracy of the control strategy were weighed, the lateral and yaw motions of the wheelsets were regarded as disturbances, and a disturbance observer of wheelset motions was designed to simplify the 17-DOF model to a 9-DOF lateral vibration model. Second, a model predictive control (MPC) strategy for HST lateral vibration was designed, and the effects of the key parameters on the lateral vibration acceleration of the HST car body and on the iterative computation time of the MPC strategy were studied. In addition, a conditionally triggered model predictive control (CMPC) strategy was proposed. Last, the control effects of the CMPC strategy under the working conditions of speed change, turnout passage, and aerodynamic load were studied by simulation, which verified the validity of the proposed model and control strategy. [ABSTRACT FROM AUTHOR]

The setting curve and control method of internal temperature in trains are studied in this paper to achieve energy saving control for air conditioning systems. First, a model of interior temperature that considers the heat transmission from infiltration air is established according to the relationship between the equivalent leakage area for the train body and the pressure difference between the internal and external carriage. Second, the interior temperature setting curve, which changes adaptively with the ambient temperature, is established considering the energy consumption of air conditioning systems and passengers' thermal comfort. Finally, an adaptive iterative learning control (AILC) algorithm is proposed and applied to control the interior temperature according to the periodicity of train operation. In this paper, meteorological data from a typical meteorological year are used for simulation analysis. Simulation results show that the AILC algorithm can effectively track the interior temperature setting value and has a better control effect when the train runs on the same line repeatedly. Under the action of the AILC algorithm, the root-mean-square error is reduced to 0.069 °C in the third iteration. Furthermore, the energy saving for a single carriage in a single process reaches up to 1.534 kW·h, thus achieving energy saving control. [ABSTRACT FROM AUTHOR]

Because of the complexity of high-speed train operation, the measured wall pressure of high-speed train will be affected by many factors, such as train vibration and environmental changes, which make the test signal inaccurate. The main interferences are the electromagnetic interference caused by the complicated electromagnetic environment and the sensor output interference caused by the train body vibration. In this paper, ensemble empirical mode decomposition (EEMD) combined with singular value decomposition (SVD) and the local projection de-noise algorithm with better effect on non-linear time series noise reduction are used to eliminate electromagnetic interference in test signal. Because of the frequency band aliasing of test signal and interference cause by vibration, it is difficult to eliminate vibration interference by the signal processing method. In this paper, a vibration interference model is established by model-train experiment and computational fluid dynamics (CFD) software to eliminate the vibration interference in the test signal. Moreover, the theoretical guidance for accurately extracting the wall pressure of high-speed train has been put forward. [ABSTRACT FROM AUTHOR]

• It exploits the vertical axle-box vibration acceleration signal that it is helpful to simplify the hardware of monitoring system of high-speed train. • The proposed angular synchronous average technique perfectly enhances the fault-related signal of wheel polygon wear by mitigating the asynchronous coherent and random background noise. • The proposed method can detect the order and the rough degree of railway wheel polygonization fault in real-time. The polygon wear of railway wheel (PWRW) is a wear fault that is ubiquitous in railway vehicles. PWRW can induce a strong periodic excitation to both vehicle and track, which not only decreases passenger comfort but also is detrimental to the operational reliability and safety. Both the degree and the order of PWRW are important parameters used to quantify the fault. Because the fault-related components distribute at a wide range in the frequency domain, it is easy to alias with some radiated vibrations from vehicle and track components, which makes the on-board detection for both parameters of PWRW very difficult. To address the practical engineering problem, this paper proposes a detection framework based on the angle domain synchronous averaging technique (ADSAT). The detection method employs the vertical axle-box vibration acceleration (ABVA), which is easy to obtain and can also be used to monitor the conditions of axle-box bearings. The paper compares the proposed and traditional methods. The results reveal that the proposed method not only achieves the order detection which the traditional method cannot, but also mitigates the influence of background noise. The feasibility and effectiveness of the proposed method to improve the detection accuracy of PWRW is demonstrated through simulation and real field investigations. [ABSTRACT FROM AUTHOR]

• Test result of pressure sensor for high speed train will be affected by vibration. • Simulation of actual vibration condition of train body with large mass method. • Simulation of pressure sensor voltage output by electrical structure simulation. • Vibration with same amplitude and different frequency has different influence. • Linear simplified model can accurately calculate vibration effect. This paper devotes to investigate the influence of vibration acceleration on measurement of piezoresistive pressure. For the sake of definiteness, sensor operation conditions in high-speed train are considered. Where, the sensor chip is loaded with dynamic accelerations with frequency within 20 Hz. The real state of the pressure sensor under different frequency vibration load is simulated by using large mass method (LMM). And the voltage output of the pressure sensor under different vibration loads was obtained by the joint simulation of electrical- structure. By analysis of different vibration loads and the voltage output of sensors, their internal relations are obtained. These outcomes provide a guidance for elimination of vibration influence in high-speed train surface fluctuation pressure measurement. It is also useful in other fields of pressure sensor operation in acceleration-loaded environments. [ABSTRACT FROM AUTHOR]

Detached Eddy Simulation (DES) has been used to investigate the effect of the Reynolds number on the wake of a high-speed train in the range from 5.2 ​× ​105 to 2.2 ​× ​107. Then, the characteristics of vortex structure, streamlines distribution, velocity characteristics, and pressure characteristics in the wake region of the high-speed train at five Reynolds numbers are studied. The results show that with the increase of the Reynolds number, some small-scale vortices emerge in the wake region of the high-speed train, which increases the complexity of the wake vortex structure, but the large-scale vortex structure has not changed significantly. The separation lines and vortices distributed on the train surface, and the characteristics of the pressure distribution in the wake region do not change considerably at different Reynolds numbers. The frequency of the vortex shedding from the train based on Strouhal number is approximately a constant at five Reynolds numbers, and the phenomenon and mechanism of the flow in the wake region are almost the same within the range of Reynolds number selected in this study. With the increase of the Reynolds number, the fluctuation degree of the velocity and pressure, and the vorticity in the wake region increase substantially. • DES has been used to investigate the wake characteristics of a high-speed train at five Reynolds numbers. • With the increase of Re , small-scale vortices emerge, but the large-scale vortex structure has not changed significantly. • The separation lines and vortices distributed on the train surface do not change considerably at different Re. • The phenomenon and mechanism of the flow in wake region is almost same within the range of Re from 5.2 × 105 to 2.2 × 107. • With the increase of Re , the fluctuation degree of velocity and pressure, and the vorticity in the wake region increase. [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]

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]

Joint Approximate Diagonalization of Eigen-matrices (JADE) cannot deal with non-stationary data. Therefore, in this paper, a method called Non-stationary Kernel JADE (NKJADE) is proposed, which can extract non-stationary features and fuse multi-sensor features precisely and rapidly. In this method, the non-stationarity of the data is considered and the data from multi-sensor are used to fuse the features efficiently. The method is compared with EEMD-SVD-LTSA and EEMD-JADE using the bearing fault data of CWRU, and the validity of the method is verified. Considering that the vibration signals of high-speed trains are typically non-stationary, it is necessary to utilize a rapid feature fusion method to identify the evolutionary trends of hunting motions quickly before the phenomenon is fully manifested. In this paper, the proposed method is applied to identify the evolutionary trend of hunting motions quickly and accurately. Results verify that the accuracy of this method is much higher than that of the EEMD-JADE and EEMD-SVD-LTSA methods. This method can also be used to fuse multi-sensor features of non-stationary data rapidly. [ABSTRACT FROM AUTHOR]