[1]黄兴淮,徐赵东,Dyke Shirley.基于能量原理和Kalman滤波器的实时模型修正策略[J].东南大学学报(自然科学版),2015,45(3):539-543.[doi:10.3969/j.issn.1001-0505.2015.03.022]
 Huang Xinghuai,Xu Zhaodong,Dyke Shirley.In-time model updating strategy based on energy theory and Kalman filter[J].Journal of Southeast University (Natural Science Edition),2015,45(3):539-543.[doi:10.3969/j.issn.1001-0505.2015.03.022]
点击复制

基于能量原理和Kalman滤波器的实时模型修正策略()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
45
期数:
2015年第3期
页码:
539-543
栏目:
建筑学
出版日期:
2015-05-20

文章信息/Info

Title:
In-time model updating strategy based on energy theory and Kalman filter
作者:
黄兴淮1徐赵东1Dyke Shirley2
1东南大学混凝土及预应力混凝土结构教育部重点实验室, 南京 210096; 2Mechanical Engineering and Civil Engineering, Purdue University, West Lafayette 47907, USA
Author(s):
Huang Xinghuai1 Xu Zhaodong1 Dyke Shirley2
1Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
2Mechanical Engineering and Civil Engineering, Purdue University, West Lafayette 47907, USA
关键词:
实时模型修正 能量原理 Kalman滤波器 损伤识别 结构健康监测
Keywords:
in-time model updating energy theory Kalman filter damage identification structural health monitoring
分类号:
TU192
DOI:
10.3969/j.issn.1001-0505.2015.03.022
摘要:
为了实时监测结构的安全状态, 提出了一种计算速度快、易收敛的模型修正策略.首先通过计算瞬时能量来建立结构单元刚度和结构响应之间的关系;然后,将结构瞬时能量代入Kalman滤波器中, 根据每一时间步能量预测值和实际测量值的差异进行修正,得到结构的真实刚度;最后,以美国地震工程模拟中心数据库(NEES)中的美国某州际公路指示牌支撑桁架为例进行数值验证, 结果表明: 无噪声干扰情况下,刚度发生20%, 40%, 60%,80%损伤的杆件和未发生损伤的杆件均能在0.4 s内从初始刚度收敛到各自的真实刚度;在5%随机噪声干扰下, 利用该策略修正得到的刚度误差均小于12%; 每一时间步所消耗的CPU时间远小于采样周期. 因此, 利用能量原理和Kalman滤波器能够快速有效地对未知刚度的结构进行实时模型修正.
Abstract:
In order to monitor structural safety in-time, a model updating strategy with high calculation speed and easy-to-convergence capacity is proposed. First, the relationship between the element stiffness and the structural responses is established by calculating the instantaneous energy. Secondly, the instantaneous energy is substituted into the Kalman filter. The structural element real stiffness is obtained by updating according to the difference of the prediction energy and measured energy in every time step. Finally, simulation tests are carried out on a highway sign support truss in network for engineering earthquake simulation(NEES)in the United States. The results show that without noise, the initial stiffness of the elements without damage and with 20%, 40%, 60%, 80% damage can be updated to real stiffness in 0.4 s. Even with 5% environmental noise, the updated stiffness errors obtained by this strategy are less than 12%. The time cost of CPU(central processing unit)in every time step is far less than the sampling time. Therefore, the energy theory and Kalman filter can update the structure with unknown stiffness promptly and effectively.

参考文献/References:

[1] Kim J T, Ryu Y S, Cho H M, et al. Damage identification in beam-type structures: frequency-based method vs mode-shape-based method [J]. Engineering Structures, 2003, 25(1): 57-67.
[2] Yan A M, Golinval J C. Structural damage localization by combining flexibility and stiffness methods[J]. Engineering Structures, 2005, 27(12): 1752-1761.
[3] 杨秋伟. 基于振动的结构损伤识别方法研究进展[J]. 振动与冲击, 2007, 26(10): 86-91, 100.
  Yang Qiuwei. A review of vibration-based structural damage identification methods [J]. Journal of Vibration and Shock, 2007, 26(10): 86-91, 100.(in Chinese)
[4] Khoo L M, Mantena P R, Jadhav P. Structural damage assessment using vibration modal analysis[J]. Structural Health Monitoring, 2004, 3(2): 177-194.
[5] Mordini A, Savov K, Wenzel H. The finite element model updating: a powerful tool for structural health monitoring[J]. Structural Engineering International, 2007, 17(4): 352-358.
[6] 朱宏平, 余璟, 张俊兵. 结构损伤动力检测与健康监测研究现状与展望[J]. 工程力学, 2011, 28(2): 1-11, 17.
  Zhu Hongping, Yu Jing, Zhang Junbing. A summary review and advantages of vibration-based damage identification methods in structural health monitoring [J]. Engineering Mechanics, 2011, 28(2): 1-11, 17.(in Chinese)
[7] Yuen K V, Lam H F. On the complexity of artificial neural networks for smart structures monitoring [J]. Engineering Structures, 2006, 28(7): 977-984.
[8] Rajasekaran S, Varghese S P. Damage detection in beams and plates using wavelet transforms [J]. Computers and Concrete, 2005, 2(6): 481-498.
[9] Yan Y J, Cheng L, Wu Z Y, et al. Development in vibration-based structural damage detection technique [J]. Mechanical Systems and Signal Processing, 2007, 21(5): 2198-2211.
[10] Kwon Y D, Kwon H W, Kim W, et al. Structural damage detection in continuum structures using successive zooming genetic algorithm [J]. Structural Engineering and Mechanics, 2008, 30(2): 135-146.
[11] Kalman R E. A new approach to linear filtering and prediction problems [J]. Journal of Basic Engineering, 1960, 82(1): 35-45.
[12] Yan G R, Dyke S J, Irfanoglu A. Experimental validation of a damage detection approach on a full-scale highway sign support truss [J]. Mechanical Systems and Signal Processing, 2012, 28:195-211.

备注/Memo

备注/Memo:
收稿日期: 2015-01-20.
作者简介: 黄兴淮(1986—),男,博士生;徐赵东(联系人),男,博士,教授,博士生导师,xzdsubmission@163.com.
基金项目: 中青年科技创新领军人才资助项目、江苏省“333人才培养工程”资助项目、美国国家自然科学基金资助项目(CNS-1035748, CNS-1035773)、东南大学优秀博士论文基金资助项目(YBJJ1207)、江苏省高校研究生科研创新计划资助项目(CXLX_0132).
引用本文: 黄兴淮,徐赵东,Dyke Shirley.基于能量原理和Kalman滤波器的实时模型修正策略[J].东南大学学报:自然科学版,2015,45(3):539-543. [doi:10.3969/j.issn.1001-0505.2015.03.022]
更新日期/Last Update: 2015-05-20