基于增秩Kalman滤波的移动车辆荷载在线识别

张超东1,黎剑安1,张浩2

振动与冲击 ›› 2022, Vol. 41 ›› Issue (2) : 87-95.

PDF(1373 KB)
PDF(1373 KB)
振动与冲击 ›› 2022, Vol. 41 ›› Issue (2) : 87-95.
论文

基于增秩Kalman滤波的移动车辆荷载在线识别

  • 张超东1,黎剑安1,张浩2
作者信息 +

Augmented Kalman filter based moving vehicle loads online identification

  • ZHANG Chaodong1,LI Jian’an1,ZHANG Hao2
Author information +
文章历史 +

摘要

提出了一种基于增秩卡尔曼滤波(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,所提的方法能准确识别荷载,且对噪声和车速不敏感。

Abstract

A novel moving vehicle dynamic load online identification method based on Augmented Kalman filter (AKF) is proposed. The vehicle load vector and the bridge structure state vector are batched together to form the augmented state vector, and AKF algorithm is employed to yields the unbiased minimum variance estimate using a small amount of response measurement, and thus the vehicle load can be identified in real time. Taking the simply supported beam-sprung mass vehicle-bridge coupling system as the object of numerical analysis, in which the feasibility and accuracy of the proposed method are examined, and the effect of road unevenness, vehicle speed, noise, sensor combination and sampling frequency to identification errors are 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 / 增秩卡尔曼滤波 / 不适定性问题

Key words

moving force identification / vehicle-bridge interaction / augmented Kalman filter / ill-posed problem

引用本文

导出引用
张超东1,黎剑安1,张浩2. 基于增秩Kalman滤波的移动车辆荷载在线识别[J]. 振动与冲击, 2022, 41(2): 87-95
ZHANG Chaodong1,LI Jian’an1,ZHANG Hao2. Augmented Kalman filter based moving vehicle loads online identification[J]. Journal of Vibration and Shock, 2022, 41(2): 87-95

参考文献

[1] Deng L, Cai C S. Identification of dynamic vehicular axle loads: theory and simulations[J]. Journal of Vibration and Control, 2010, 16(14): 2167-2194.
[2] Sun Y, Luo L, Chen K, et al. A time-domain method for load identification using moving weighted least square technique[J]. Computers & Structures, 2020, 234: 106254.
[3] Moses F, Weigh-in-motion system using instrumented bridges [J]. Transportation Engineering Journal, 1979, 105233-249.
[4] Liu J, Meng X, Jiang C, et al. Time‐domain Galerkin method for dynamic load identification[J]. International Journal for Numerical Methods in Engineering, 2016, 105(8): 620-640.
[5] Uhl T. The inverse identification problem and its technical application[J]. Archive of Applied Mechanics, 2007, 77(5): 325-337.
[6] Liu G R, Ma W B, Han X. An inverse procedure for identification of loads on composite laminates[J]. Composites Part B: Engineering, 2002, 33(6): 425-432.
[7] Chan T H T, Law S S, Yung T H, et al. An interpretive method for moving force identification[J]. Journal of sound and vibration, 1999, 219(3): 503-524.
[8] Qiao B, Chen X, Luo X, et al. A novel method for force identification based on the discrete cosine transform [J]. Journal of Vibration and Acoustics, 2015, 137(5): 051012.1-051012.15.
[9] Law SS, Chan THT, Zhu QX, et al. Regularization in moving force identification[J]. Journal of engineering mechanics, 2001, 127(2): 136-148.
[10] Chen Z, Chan THT, A truncated generalized singular value decomposition algorithm for moving force identification with ill-posed problems[J], Journal of Sound & Vibration, 2017, 401: 297–310.
[11] 潘楚东,余岭,刘焕林,等.考虑初始条件影响的移动荷载识别稀疏正则化方法[J].振动工程学报,2018,31(05):734-743.
PAN Chu-dong, YU Ling, LIU Huan-lin, et al. Sparse regularization based moving force identification under unknown initial conditions[J]. Journal of Vibration Engineering, 2018, 31(5): 734-743.
[12] Asnachinda P, Pinkaew T, Laman J A. Multiple vehicle axle load identification from continuous bridge bending moment response[J]. Engineering Structures, 2008, 30(10): 2800-2817.
[13] Zhu X Q, Law S S, Bu J Q. A state space formulation for moving loads identification[J]. Journal of Vibration and Acoustics, 2006, 128(4): 509-520.
[14] Qiao B, Zhang X, Luo X, et al. A force identification method using cubic B-spline scaling functions[J]. Journal of Sound and Vibration, 2015, 337: 28-44.
[15] Busby H R, Trujillo D M. Optimal regularization of an inverse dynamics problem[J]. Computers & structures, 1997, 63(2): 243-248.
[16] Zhong J, Liu H, Yu L. Sparse regularization for traffic load monitoring using bridge response measurements[J]. Measurement, 2019, 131: 173-182.
[17] 陈震,王震,余岭,邵文达.预处理最小二乘QR分解法识别桥梁移动荷载的优化分析及试验研究[J].振动工程学报,2018,31(04):545-552.
CHEN Zhen, WANG Zhen, YU Ling, et al. Optimization analysis and experimental study of preconditioned least square QR-factorization for moving force identification[J]. Journal of Vibration Engineering, 2018, 31(04): 545-552.
[18] Jeffcott H H. VI. On the vibration of beams under the action of moving loads[J]. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1929, 8(48): 66-97.
[19] Hillerborg A. Dynamic influences of smoothly running loads on simply supported girders[M]. Stockholm: Tekniska högskolan, 1951.
[20] 周玉民,谈至明,刘伯莹.1/4车-路耦合动力学模型研究[J].同济大学学报(自然科学版),2012,40(03):408-413.
ZHOU Yu-min, TAN Zhi-ming, LIU Bo-ying. Quarter Vehicle-road Coupling Dynamics Models[J], Journal of Tongji University (natural science), 2012, 40(03): 408-413.
[21] 王宁波,任伟新,李苗.基于影响线的桥梁移动荷载识别[J].振动与冲击,2013,32(03):129-133.
WANG Ning-bo, Ren Wei-xin, LI Miao. Moving load identification of a bridge based on influence line[J]. Journal of Vibration and Shock, 2013, 32(03): 129-133.
[22] 乔东钦,李昭,吴汉立,赵华.基于LS-DYNA与耦合方法的简支板桥车桥振动分析[J].公路工程,2017,42(02):116-121+144.
QIAO Dong-qin, LI Zhao, WU Han-li, et al. Vehicle-Bridge Vibration Analysis of Simply Supported Slab Bridge Using Coupling Method with LS-DYNA[J]. Highway Engineering, 2017, 42(02): 116-121+144.
[23] ISO 8608: 2016. Mechanical Vibration–Road Surface Profiles–Reporting of Measured Data[S]. BSI Standards Publication: London, 2016.
[24] Law S S, Bu J Q, Zhu X Q, et al. Vehicle axle loads identification using finite element method[J]. Engineering Structures, 2004, 26(8): 1143-1153.
[25] Zhang C, Gao Y W, Huang J P, et al. Damage identification in bridge structures subject to moving vehicle based on extended Kalman filter with l1-norm regularization[J]. Inverse Problems in Science and Engineering, 2020, 28(2): 144-174.
[26] Feng D, Sun H, Feng M Q. Simultaneous identification of bridge structural parameters and vehicle loads[J]. Computers & Structures, 2015, 157: 76-88.
[27] Zhu X Q, Law S S. Recent developments in inverse problems of vehicle–bridge interaction dynamics[J]. Journal of Civil Structural Health Monitoring, 2016, 6: 107–128.
[28] Zhang C D, Xu Y L. Structural damage identification via response reconstruction under unknown excitation[J]. Structural Control and Health Monitoring, 2016, 24(8): 1-11.
[29] Lourens E, Reynders E, Roeck G D, et al. An augmented Kalman filter for force identification in structural dynamics[J]. Mechanical Systems and Signal Processing, 2012, 27: 446-460.
[30] 陈震,余岭.基于截断GSVD方法的桥梁移动荷载识别[J].振动与冲击,2014,33(10):97-100+130.
CHEN Zhen, YU Ling. Identification of dynamic axle loads on a bridge based on truncated generalized singular value decomposition[J]. Journal of Vibration and Shock, 2014, 33(10): 97-100+130.
[31] Yu L, Chan T H T, Zhu J. A MOM-based algorithm for moving force identification: Part II-Experiment and comparative studies[J]. Structural Engineering and Mechanics, 2008, 29(2): 155-169.
[32] Pan C D, Yu L, Liu H L, et al. Moving force identification based on redundant concatenated dictionary and weighted l1-norm regularization[J]. Mechanical Systems and Signal Processing, 2018, 98: 32-49.

PDF(1373 KB)

Accesses

Citation

Detail

段落导航
相关文章

/