High-Speed EMU TCMS Design and LCC Technology Research

本文介绍了高速动车组的生命周期——设计、制造、测试、运营和维护阶段,以及各种平台,包括列车控制与监视系统(TCMS)开发平台、测试和验证平台、动车组(EMU)驾驶仿真平台及远程数据传输和维护平台。所有平台构成了EMU全寿命周期系统并绑定在一起。每个平台都有利于EMU全寿命周期管理且成为EMU全寿命周期管理的重要组成部分。

Research on motor rotation anomaly detection based on improved VMD algorithm

Purpose–The electromechanical brake system is leading the latest development trend in railway braking technology.The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder.The tolerance leads to imprecise brake control,so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system.This paper aims to present improved variational mode decomposition(VMD)algorithm,which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approach–The VMD algorithm plays a pivotal role in the preliminary phase,employing mode decomposition techniques to decompose the motor speed signals.Afterward,the error energy algorithm precision is utilized to extract abnormal features,leveraging the practical intrinsic mode functions,eliminating extraneous noise and enhancing the signal’s fidelity.This refined signal then becomes the basis for fault analysis.In the analytical step,the cepstrum is employed to calculate the formant and envelope of the reconstructed signal.By scrutinizing the formant and envelope,the fault point within the electromechanical brake system is precisely identified,contributing to a sophisticated and accurate fault diagnosis.Findings–This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake(EMB)motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction.The signal is reconstructed according to the effective intrinsic mode functions(IMFS)component of removing noise,and the formant and envelope are calculated by cepstrum to locate the fault point.Experiments show that the empirical mode decomposition(EMD)algorithm can effectively decompose the original speed signal.After feature extraction,signal enhancement and fault identification,the motor mechanical fault point can be accurately located.This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.Originality/value–By using this improved VMD algorithm,the electromechanical brake system can precisely identify the rotational anomaly of the motor.This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled.Compared with the conventional motor diagnosis method,this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs.Moreover,the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

Typical faults and safety analysis of brake controller for AC locomotive

Purpose–The brake controller is a key component of the locomotive brake system.It is essential to study its safety.Design/methodology/approach–This paper summarizes and analyzes typical faults of the brake controller,and proposes four categories of faults:position sensor faults,microswitch faults,mechanical faults and communication faults.Suggestions and methods for improving the safety of the brake controller are also presented.Findings–In this paper,a self-judgment and self-learning dynamic calibration method is proposed,which integrates the linear error of the sensor and the manufacturing and assembly errors of the brake controller to solve the output drift.This paper also proposes a logic for diagnosing and handling microswitch faults.Suggestions are proposed for other faults of brake controller.Originality/value–The methods proposed in this paper can greatly improve the usability of the brake controller and reduce the failure rate.

应用元启发式优化和高斯过程回归预测受电弓-接触网系统性能的可行性研究

受电弓接触网系统为高速列车提供电能,正确评估受电弓与接触网之间的接触力(CF)对于稳定供电至关重要.CF的大小和变化范围决定了列车受流质量和安全运行.因此,快速、准确地预测CF具有重要意义.然而,通过实验收集CF数据具有挑战性,并且通过数值模拟获得及时结果并不总是可行的.在本研究中,我们提出了一种结合元启发式优化和高斯过程回归的高效的代理模型方法,来预测受电弓接触网系统接触力统计量。首先,使用有限元法(FEM)建立并验证受电弓接触网模型,用于生成训练和测试数据集.其次,利用高斯过程回归(GPR)进行对接触力的预测.将一种新开发的元启发式优化,即二元饥饿游戏搜索(HGS),应用于特征选择.为了增强BHGS的性能,嵌入了混沌机制,产生了ChaOs-HGSGPR(CHGS-GPR).最后,对CHGS-GPR的预测结果进行了评估.结果发现,所提出的CHGS-GPR对CF的平均值提供了相当准确的预测,并且可以扩展到铁路线路的初步设计、列车运行的实时评估和控制.