基于增秩Kalman滤波的移动车辆荷载在线识别  

Augmented Kalman filter based moving vehicle loads online identification

作　　者：张超东 黎剑安 张浩[2] ZHANG Chaodong;LI Jian’an;ZHANG Hao(Institute of Urban Smart Transportation&Safety Maintenance,College of Civil and Transportation Engineering,Shenzhen University,Shenzhen 518060,China;State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)

机构地区：[1]深圳大学土木与交通工程学院城市智慧交通与安全运维研究院,广东深圳518060 [2]石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室,石家庄050043

出　　处：《振动与冲击》2022年第2期87-95,共9页Journal of Vibration and Shock

基　　金：国家重点研发计划(2019YFB2102700);国家自然科学基金(52008260);深圳市海上基础设施安全与监测重点实验室(ZDSYS20201020162400001);省部共建交通工程结构力学行为与系统安全国家重点实验室2020年度开放课题(KF2020-16);深圳大学新引进教师科研启动项目(860-000002110356)。

摘　　要：提出了一种基于增秩Kalman滤波(augmented Kalman filter,AKF)的移动车辆荷载在线识别方法。将车辆荷载向量与桥梁结构状态向量联立构成增秩状态向量,基于AKF算法,利用桥梁状态空间方程和少量振动响应获得增秩状态向量的无偏最小方差估计,进而实时识别车辆荷载。以简支梁-弹簧质量车桥耦合系统为数值分析对象,研究了基于AKF算法的移动车辆荷载识别方法的可行性和准确性,详细讨论了路面不平度、车速、噪声、传感器组合和采样频率对识别误差的影响。结果表明,所提方法能准确识别荷载,且对噪声和车速不敏感。A novel moving vehicle dynamic load online identification method based on the augmented Kalman filter(AKF)was proposed.The vehicle load vector and the bridge structure state vector were batched together to form an augmented state vector,and the AKF algorithm was employed to yield the unbiased minimum variance estimate by using only a small amount of response measurement so as to make the vehicle load be identified in real time.Taking a simply supported beam-sprung mass vehicle-bridge coupling system as the object of numerical analysis,the feasibility and accuracy of the proposed method were examined,and the effects of road unevenness,vehicle speed,noise,sensor combination and sampling frequency on identification errors were investigated detailedly.The proposed method can accurately identify dynamic loads and is insensitive to measurement noises and vehicle speed.

关 键 词：移动荷载识别 车桥耦合系统 增秩Kalman滤波(AKF) 不适定性问题 

分 类 号：TU311.3[建筑科学—结构工程]