作者:艾永军, 陈春俊, 熊仕勇, 张振
作者单位:西南交通大学机械工程学院, 四川 成都 610031
关键词:高速列车;安全性;脱轨监测;卡尔曼滤波;小波变换
摘要:
基于轨道不平顺检测技术的发展,根据轮轨几何关系脱轨理论,设计一种能够工程实施的脱轨监测方案,采用轮轨横向、垂向相对位移近似替代轮对接触点位移和车轮抬升量来判断列车脱轨。针对测控方案中关键参数轴箱振动位移测量困难、测量误差大的问题,使用卡尔曼滤波设计一种估计算法,实时计算轴箱横向、垂向位移;观测噪声方差变化会导致卡尔曼滤波发散,采用小波变换在线估计噪声方差,提高卡尔曼滤波算法鲁棒性。仿真表明:该算法能够通过加速度信号精确计算轴箱振动位移,误差在10%以内,为高速列车脱轨在线监测提供一种思路。
Research on key algorithms for derailment monitoring of high speed trains
AI Yongjun, CHEN Chunjun, XIONG Shiyong, ZHANG Zhen
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Abstract: Based on the development of track irregularity detection technology and the derailment theory of wheel-rail geometric relationship, a derailment monitoring scheme is designed, which can be implemented in engineering. The lateral and vertical relative displacement of wheel-rail is used to approximately replace the contact point displacement and lift of wheel-pair to judge train derailment. Aiming at the difficulty of measuring the vibration displacement of the axle box, an estimation algorithm based on Kalman filter is designed to calculate the lateral and vertical displacement of the axle box in real time.The variance of the observed noise will lead to the divergence of Kalman filter. Wavelet transform is used to estimate the variance of the noise on-line to improve the robustness of Kalman filter algorithm.The simulation results show that the algorithm can accurately calculate the vibration displacement of axle box, and the error is less than 10%. It provides an idea for on-line monitoring of high-speed train derailment.
Keywords: high speed train;safety;derailment detection;Kalman filtering;wavelet transform
2019, 45(10):109-113 收稿日期: 2019-02-17;收到修改稿日期: 2019-03-18
基金项目: 国家自然科学基金资助项目(51475387)
作者简介: 艾永军(1992-),男,安徽芜湖市人,硕士研究生,专业方向为高速列车自动化控制与检测
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