摘要
利用车辆振动响应反演轨道不平顺是轨道状态检测的重要手段,是实现轨道车辆智能运维的关键环节。为此,以某运行速度160km/h的轨道车辆为例,建立了车辆系统的横向、垂向和横垂耦合3种动力学模型,并在车辆系统状态空间下推导了轨道不平顺反演方程,给出了基于经典卡尔曼滤波(KF)/自适应卡尔曼滤波(AKF)算法的轨道不平顺反演流程,最后详细探究了卡尔曼滤波算法、车辆动力学模型和观测方案对轨道横向和垂向不平顺反演结果的影响规律。结果显示:相比于单一横向和垂向模型,横垂耦合模型在KF算法中的轨道不平顺反演效果最佳,表明横垂耦合模型能够更好地模拟车辆横向和垂向运动行为;AKF算法在单一横向和垂向模型表现更优,但在横垂耦合模型中并未发挥出自适应调参优势,表明对于复杂高维耦合模型自适应策略不能保证一定收敛到最优解,反而对于更简单低维模型的自适应反演效果更好;观测方案对轨道不平顺反演结果影响较大,特别是单一的振动加速度观测量难以有效反演轨道不平顺,应结合实际补充有效振动响应信息。
The inversion of track irregularity using vehicle vibration responses serves as a vital method for track condition detection and a key part in achieving intelligent operation and maintenance of railway vehicles.To this end,this paper takes a railway vehicle operating at 160 km/h as an example,establishing three dynamic models for the vehicle system:lateral,vertical,and lateral-vertical coupled.The track irregularity inversion equation is derived within the state-space framework of the vehicle system.An inversion process for track irregularity based on the classical Kalman filter(KF)and adaptive Kalman filter(AKF)algorithms is presented.Finally,a detailed investigation is conducted into the influence patterns of the Kalman filter algorithms,vehicle dynamics models,and observation schemes on the inversion results of lateral and vertical track irregularities.The results indicate:Compared to single lateral or vertical models,the lateral-vertical coupled model yields the best inversion results under the KF algorithm,demonstrating its superior capability in simulating the lateral and vertical motion behaviors of the vehicle.The AKF algorithm performs better in single lateral and vertical models but fails to leverage its adaptive parameter-tuning advantages in the lateral-vertical coupled model.This suggests that adaptive strategies may not guarantee convergence to optimal solutions for complex high-dimensional coupled models,whereas simpler low-dimensional models benefit more from adaptive inversion.The observation scheme significantly impacts track irregularity inversion results.Specifically,relying solely on vibration acceleration measurements proves inadequate for effective inversion.Therefore,effective vibration responses shall be supplemented in combination with the actual situation.
作者
周生通
刘怡辰
肖乾
罗志翔
程玉琦
顾祎昊
Zhou Shengtong;Liu Yichen;Xiao Qian;Luo Zhixiang;Cheng Yuqi;Gu Yihao(School of Mechatronics&Vehicle Engineering,East China Jiaotong University,Nanchang 330013,China;The State Key Laboratory of Heavy-Duty and Express High-Power Electric Locomotive,CRRC Zhuzhou Locomotive Co.,Ltd.,Zhuzhou 412001,China)
出处
《华东交通大学学报》
2025年第4期37-47,共11页
Journal of East China Jiaotong University
基金
国家自然科学基金项目(52462054,52372327,52065022)
江西省自然科学基金项目(20242BAB204122)
江西省教育厅科学技术研究项目(GJJ210638)。
关键词
轨道车辆
振动响应
轨道不平顺
反演方程
卡尔曼滤波
railway vehicles
vibration responses
track irregularity
inversion equation
Kalman filter
