基于自适应无迹卡尔曼滤波的结构损伤在线识别算法

PDF(2748 KB)

振动与冲击 ›› 2024, Vol. 43 ›› Issue (23) : 203-210.

论文

基于自适应无迹卡尔曼滤波的结构损伤在线识别算法

黄可1,2，孙展1，黄杜康1，王磊1

作者信息 +

Structural damage online identification algorithm based on AUKF

HUANG Ke1,2, SUN Zhan1, HUANG Dukang1, WANG Lei1

Author information +

文章历史 +

摘要

在运用无迹卡尔曼滤波(UKF)对结构进行损伤识别和健康监测的过程中，噪声往往未知时变，而传统UKF方法在噪声参数选择不当时易出现性能退化及发散的问题。为此，提出一种基于自适应无迹卡尔曼滤波(AUKF)的结构损伤在线识别算法。该算法利用协方差匹配法和遗忘因子法，通过残差和新息序列实时识别并更新测量噪声和过程噪声，在保证噪声矩阵正定性的同时，提高了UKF对结构未知参数和损伤的识别精度。采用桥梁模型和非线性模型的数值算例验证本文方法的有效性。结果表明，本文方法可以有效识别大型土木工程结构和非线性结构的损伤位置和程度，且对时变的噪声具有自适应能力和鲁棒性。

Abstract

In the process of structural damage identification and health monitoring using unscented Kalman filtering, the noise is often unknown and time-varying, and the traditional unscented Kalman filtering is prone to performance degradation and dispersion when the noise is not selected properly. Therefore, an online structural damage identification algorithm based on an adaptive unscented Kalman filter is proposed. The proposed method uses covariance matching and forgetting factor method to identify and update the measurement noise and process noise in real time by residuals and innovations, which improves the accuracy of the unscented Kalman filtering for the identification of unknown parameters and damages of the structure while guaranteeing the positive characterization of the noise matrix. Numerical examples of the bridge model and the nonlinear model are used to verify the effectiveness of the proposed method. The results show that the method in this paper can effectively recognize the location and severity of damage in large civil engineering structures and nonlinear structures and is adaptive and robust to time-varying noise.

导出引用

黄可1, 2, 孙展1, 黄杜康1, 王磊1. 基于自适应无迹卡尔曼滤波的结构损伤在线识别算法[J]. 振动与冲击, 2024, 43(23): 203-210

HUANG Ke1, 2, SUN Zhan1, HUANG Dukang1, WANG Lei1. Structural damage online identification algorithm based on AUKF[J]. Journal of Vibration and Shock, 2024, 43(23): 203-210

参考文献

[1] 王凌波, 王秋玲, 朱钊, 等. 桥梁健康监测技术研究现状及展望[J]. 中国公路学报, 2021, 34(12): 25-45.
WANG Ling-bo, WANG Qiu-ling, ZHU Zhao, et al. Current status and prospects of research on bridge health monitoring technology[J]. China Journal of Highway and Transport, 2021, 34(12): 25-45.
[2] Tong Y Z, Zheng Z S, Fan W L, et al. An improved unscented Kalman filter for nonlinear systems with one-step randomly delayed measurement and unknown latency probability[J]. Digital Signal Processing: A Review Journal, 2022, 121: 103324.
[3] Xiong K, Zhang H Y, Chan C W. Performance evaluation of UKF-based nonlinear filtering[J]. Automatica, 2006, 42(2): 261-270.
[4] Mariani S, Ghisi A. Unscented Kalman filtering for nonlinear structural dynamics[J]. Nonlinear Dynamics, 2007, 49: 131-150.
[5] Al-Hussein A, Haldar A. Unscented Kalman filter with unknown input and weighted global iteration for health assessment of large structural systems[J]. Structural Control and Health Monitoring, 2016, 23(1): 156-175.
[6] Li W, Sun S, Jia Y, et al. Robust unscented Kalman filter with adaptation of process and measurement noise covariances[J]. Digital Signal Processing, 2016, 48: 93-103.
[7] Mehra R. Approaches to adaptive filtering[J]. IEEE Transactions on Automatic Control, 1972, 17(5): 693-698.
[8] Yuen K V, Kuok S C. Online updating and uncertainty quantification using nonstationary output-only measurement[J]. Mechanical Systems and Signal Processing, 2016, 66: 62-77.
[9] 杨纪鹏, 夏烨, 闫业祥, 等. 改进的强追踪平方根无迹卡尔曼滤波时变结构参数识别[J]. 振动与冲击, 2021, 40(23): 74-82, 126.
YANG Ji-peng, XIA Ye, YAN Ye-xiang, et al. Time-varying structural parametric identification with MSTSRUKF[J]. Journal of Vibration and Shock, 2021, 40(23): 74-82, 126.
[10] Gao B, Gao S, Hu G, et al. Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter[J]. Aerospace Science and Technology, 2018, 73: 184-196.
[11] Amini Tehrani H, Bakhshi A, Yang T T Y. Online probabilistic model class selection and joint estimation of structures for post-disaster monitoring[J]. Journal of Vibration and Control, 2021, 27(15-16): 1860-1878.
[12] Sage A P, Husa G W. Adaptive filtering with unknown prior statistics[C]//Joint Automatic Control Conference. 1969(7): 760-769.
[13] 魏伟, 秦永元, 张晓冬, 等. 对 Sage-Husa 算法的改进[J]. 中国惯性技术学报, 2012, 20(6): 678-686.
WEI Wei, QIN Yong-yuan, ZHANG Xiao-dong, et al. Amelioration of the Sage-Husa algorithm[J]. Journal of Chinese Inertial Technology, 2012, 20(6): 678-686.
[14] 叶泽浩, 宋亚伟, 陈传生, 等. 基于噪声估计的自适应无迹卡尔曼滤波算法[J]. 空天预警研究学报，2022, 36(4): 242-246.
YE Ze-hao, SONG Ya-wei, CHEN Chuan-sheng, et al. Adaptive untraced Kalman filtering algorithm based on noise estimation[J]. Journal of Air & Space Early Warning Research, 2022, 36(4): 242-246.
[15] Kuleli M, Nagayama T. A robust structural parameter estimation method using seismic response measurements[J]. Structural Control and Health Monitoring, 2020, 27(3): e2475.
[16] 刘宇, 刘琼, 周帆, 等. 基于自适应 UKF 微型航姿系统噪声在线估计[J]. 重庆邮电大学学报(自然科学版), 2016, 28(3): 285-290, 311.
LIU Yu, LIU Qiong, ZHOU Fan, et al. Online noise estimation of mini-AHRS based on adaptive UKF algorithm[J]. Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition), 2016, 28(3): 285-290, 311.
[17] 胡高歌, 高社生, 赵岩. 一种新的自适应 UKF 算法及其在组合导航中的应用[J]. 中国惯性技术学报, 2014, 12(3): 357-361, 367.
HU Gao-ge, GAO She-sheng, ZHAO Yan. Novel adaptive UKF and its application in integrated navigation[J]. Journal of Chinese Inertial Technology, 2014, 12(3): 357-361, 367.
[18] Song M, Astroza R, Ebrahimian H, et al. Adaptive Kalman filters for nonlinear finite element model updating[J]. Mechanical Systems and Signal Processing, 2020, 143: 106837.
[19] Haykin S. Kalman Filtering and Neural Networks[M]. John Wiley & Sons, 2004.
[20] Zhang Q, Yang Y, Xiang Q, et al. Noise adaptive Kalman filter for joint polarization tracking and channel equalization using cascaded covariance matching[J]. IEEE Photonics Journal, 2018, 10(1): 1-11.
[21] Meng Y, Gao S, Zhong Y, et al. Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration[J]. Acta Astronautica, 2016, 120: 171-181.
[22] 孙铭芳, 吕旭, 赵仁杰, 等. 基于新息自适应的扩展卡尔曼滤波雷达目标跟踪算法[J]. 科学技术与工程, 2023, 23(9): 3738-3743.
SUN Ming-fang, LU Xu, ZHAO Ren-jie, et al. Adaptive extended Kalman filter algorithm based oninnovation sequence for radar target tracking[J]. Science, Technology and Engineering, 2023, 23(9): 3738-3743.
[23] Robbins H, Monro S. A stochastic approximation method[J]. The Annals of Mathematical Statistics, 1951: 400-407.