环境激励下桥梁结构模态参数识别的改进随机子空间算法

陈永高1,钟振宇1,2

振动与冲击 ›› 2020, Vol. 39 ›› Issue (16) : 196-204.

PDF(2754 KB)
PDF(2754 KB)
振动与冲击 ›› 2020, Vol. 39 ›› Issue (16) : 196-204.
论文

环境激励下桥梁结构模态参数识别的改进随机子空间算法

  • 陈永高1,钟振宇1,2
作者信息 +

An improved stochastic subspace method for modal parameter identification for bridge structures under ambient excitation

  • CHEN Yonggao1, ZHONG Zhenyu1, 2
Author information +
文章历史 +

摘要

为了剔除稳定图中的虚假模态和避免模态遗漏现象,提高模态参数识别的精确度,提出了一种基于滑动窗口和相似度的桥梁结构模态参数智能化识别算法。基于余弦相似原理提出了频率相似度和振型相似度,并依此构建置信度,以实现对系统真实阶次的自动化确定;引入改进集成经验模式分解算法,以消除响应信号内部的噪声信号,达到消除部分虚假模态的目的;接着引入滑动窗口以实现对响应信号的划分,并通过构建频率相似度、振型相似度以及阻尼比相似度实现多个窗口对应参数结果中同类模态的聚类处理,达到剔除虚假模态和避免模态遗漏的目的。最后将所提算法运用于实际斜拉桥结构的模态参数识别,并将识别结果与现场试验值以及有限元结果进行对比,结果表明,所提算法不仅能有效识别出频率结果还能识别出准确的模态振型图,能够实现桥梁结构模态参数的在线智能化识别。

Abstract

In order to eliminate the false mode in the stability diagram and avoid the modal missing phenomenon, so that to improve the accuracy of modal parameter identification, an intelligent recognition algorithm which is based on recursive window and similarity was proposed for bridge structure modal parameters.Firstly, the frequency similarity and mode similarity were proposed based on the cosine similarity principle, and in accordance with them, the confidence was constructed to realize the automatic determination of the real order of the system.Secondly, the improved integrated empirical mode decomposition algorithm was introduced to eliminate the noise inside the response signal and to achieve the purpose of eliminating some parts of false modes.And then the recursive window was introduced to realize the division of the response signal, and the clustering processing of similar modalities in multiple window corresponding parameter results was realized by constructing the frequency similarity, the mode shape similarity and the damping ratio similarity.The clustering process achieves the purpose of eliminating false modes and avoiding modal missing at the same time.Finally, the proposed algorithm was applied to the real cable-stayed bridge structure, and its identification results were compared with the field test values and the finite element results.The results show that the proposed algorithm can not only effectively identify the frequency results but also identify the accurate modal shape patterns, that is, the proposed algorithm can realize on-line intelligent identification of modal parameters of bridge structures.

关键词

桥梁结构 / 智能化识别 / 随机子空间算法 / 模态参数 / 系统阶次 / 相似度 / 滑动窗口

Key words

bridge structure / intelligent identification / stochastic subspace identification / modal parameter / system order / similarity degree / recursive window

引用本文

导出引用
陈永高1,钟振宇1,2. 环境激励下桥梁结构模态参数识别的改进随机子空间算法[J]. 振动与冲击, 2020, 39(16): 196-204
CHEN Yonggao1, ZHONG Zhenyu1, 2. An improved stochastic subspace method for modal parameter identification for bridge structures under ambient excitation[J]. Journal of Vibration and Shock, 2020, 39(16): 196-204

参考文献

[1] 秦世强.桥梁健康监测与工作模态分析的理论和应用及系统实现[D].成都:西南交通大学,2013.
QIN Shiqiang. Health monitoring implementation and theory and application and system operational modal analysis of [D]. Chengdu:Southwest Jiaotong University, 2013.
[2] Parametric modal identification of time-varying structures and the validation approach of modal parameters[J]. Si-Da Zhou,Ward Heylen,Paul Sas,Li Liu.  Mechanical Systems and Signal Processing . 2013.
[3] EDWIN R,JEROEN H,GUIDO D R, et al. Fully automated (operational) modal analysis[J].Mechanical Systems and Signal Processing,2012,29(31):228-250.
[4] 刘宇飞, 辛克贵, 樊健生,等. 环境激励下结构模态参数识别方法综述[J]. 工程力学, 2014, 31(4):46-53.
LIU Yufei, XIN Ke-gui, FAN Jian-sheng, CUI Dingyu. A method survey of structure modal parameter identification under Environmental excitation [J]. Engineering mechanics. 2014 (04).
[5] 刘宗政,陈恳.基于环境激励的桥梁模态参数识别[J].振动、测试与诊断,2010,30(3):300-303.
LIU Zongzheng, CHEN Ken. Bridge modal parameters identification based on ambient  excitation [J]. Journal of Vibration,Measurement & Diagnosis, 2010,30(3):300-303.
[6]Velazquez A, Swartz R A. Output-only cyclo-stationary linear-parameter time-varying stochastic subspace identification method for rotating machinery and spinning structures[J]. Journal of Sound and Vibration, 2015, 337: 45-70.
[7] Hong A L, Ubertini F, Betti R. New stochastic subspace approach for system identification and its application to long-span bridges[J]. Journal of Engineering Mechanics, 2012, 139(6): 724-736.
[8] 汤宝平,章国稳,陈卓.基于谱系聚类的随机子空间模态参数自动识别[J].振动与冲击.2012,31(10).
TANG Bao-ping, ZHANG Guo-wen, CHEN Zhuo, automatic identification of  stochastic subspace  modal parameter based on hierarchical clustering[J]. Journal of vibration and shock.2012, 31 (10).
[9]周思达,周小陈,刘莉,等.基于模糊聚类的模态参数全因素自动验证方法[J].北京航空航天大学学报,2015,41(5):811-816.
ZHOU Sida,ZHOU Xiaochen,LIU Li,et al.Fuzzy-clustering based all factor automatous validation approach Oder modal parameters of structures[J].Journal of Beijing university of aeronautics and astronautics ,2015,41(5):811-816.
[10] Ubertini F, Gentile C, Materazzi A L. Automated modal identification in operational conditions and its application to bridges[J]. Engineering Structures, 2013, 46: 264-278.
[11] 章国稳.环境激励下结构模态参数自动识别与算法优化[D].博士学位论文.重庆:重庆大学.2014.
ZHANG Guowen. Ambient excitation based automatic modal parameter identification for bridge structure and algorithm optimization [D]. Ph.D. Thesis. Chongqing: Chongqing University, 2014.
[12] 桥梁动力测试信号的自适应分解与重构[J]. 振动与冲击, 2015, 34(3):1-6.
SHAN Deshan, LI Qiao, HUANG Zheng.Adaptive Bridges dynamic test signal decomposition and reconstruction [J]. Vibration and Shock . 2015, 34(3):1-6.
[13] 练继建,李火坤,张建伟.基于奇异熵定阶降噪的水工结构振动模态ERA识别方法[J].中国科学: E辑. 2008. 38(9). 1398-1413.
LIAN Jijian, LI Huokun, ZHANG Jianwei. Hydraulic Structure Vibration Modal ERA Identification According to De-noising Order of Singular Entropy [J]. Science China: Series E. 2008, 38 (9), 1398-1413.
[14] 章国稳, 马婧华, 陈卓. 基于模态相似指数的PRCE虚假模态剔除[J]. 振动.测试与诊断, 2015(3):493-498.
ZHANG Guo-wen, MA Jinghua, CHEN Zhuo. An elimination of false modal Based on the modal similar index of PRCE [J]. Journal of Vibration, Measurement & Diagnosis. 2015 (3) :493-498..
 [15] Carden E P, Mita A. Challenges in developing confidence intervals on modal parameters estimated for large civil infrastructure with stochastic subspace identification[J]. Structural Control and Health Monitoring, 2011, 18(1): 53-78.
[16] 吴春利,刘寒冰,王静.模糊聚类算法稳定图应用于桥梁结构参数识别[J].振动与冲击,2013,32(4):121-126.
WU Chunli, LIU Hanbing,WANG Jiang. Parameter identification of a bridge structure based on a stabilization diagram with fuzzy clustering method [J].Journal of vibration and Shock,2013,32(4):121-126.
[17] 肖祥,任伟新.实时工作模态参数数据驱动随机子空间识别[J].振动与冲击,2009,28(8);148-206.
XIAO Xiang,REN Weixin. Improved data-driven stochastic subspace identification of online operational modal parameter [J].Journal od Vibration and Shock,2009,28(8):148-206.
[18] Boonyapinyo V, Janesupasaeree T. Data-driven stochastic subspace identification of flutter derivatives of bridge decks[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2010, 98(12): 784-799.
[19] 严鹏.基于信号理论的桥梁健康监测降噪处理和损伤识别研究[D].西南交通大学,2011.
YAN Peng. Research on noise reduction and damage Identification of Bridge Health Monitoring based on signal Theory [D]. Southwest Jiaotong University,2011.
[20] 陈永高,钟振宇.基于改进EEMD算法的桥梁结构响应信号模态分解研究[J].振动与冲击,2019,38(10):23-30.
CHEN Yonggao, ZHONG Zhenyu. Modal decomposition of response signal for a bridge structure based on the improved EEMD [J].Journal of Vibration and Shock,2019,38(10):23-30.
[21] 郑德智, 李子恒, 王豪. 基于环境激励的桥梁振动模态识别算法研究[J]. 传感技术学报, 2015(2):170-177.
ZHENG Dezhi, LI Ziheng, WANG Hao. The research of Bridge vibration modal identification based on the environment excitation [J]. Journal of sensing technology. 2015 (02) :170-177.

PDF(2754 KB)

824

Accesses

0

Citation

Detail

段落导航
相关文章

/