基于滑窗OPTICS算法和DATA-SSI算法的桥梁模态参数智能化识别

陈永高1,钟振宇1,2,罗晓峰1,2

振动与冲击 ›› 2024, Vol. 43 ›› Issue (7) : 18-29.

PDF(4367 KB)
PDF(4367 KB)
振动与冲击 ›› 2024, Vol. 43 ›› Issue (7) : 18-29.
论文

基于滑窗OPTICS算法和DATA-SSI算法的桥梁模态参数智能化识别

  • 陈永高1,钟振宇1,2,罗晓峰1,2
作者信息 +

Intelligent identification of bridge modal parameters based on sliding-window OPTICS algorithm and DATA-SSI algorithm

  • CHEN Yonggao1, ZHONG Zhenyu1,2, LUO Xiaofeng1,2
Author information +
文章历史 +

摘要

针对现有基于数据驱动的随机子空间算法(Data-driven Stochastic Subspace Identification-DATA SSI)存在的不足,无法实现稳定图中真假模态的智能化筛选,提出了一种新的模态参数智能化识别算法。首先通过引入滑窗技术来实现对输入信号的合理划分,以避免虚假模态和模态遗漏现象的出现;其次通过引入OPTICS密度聚类算法(Ordering Points To Identify the Clustering Structure)实现稳定图中真实模态的智能化筛选,最后将所提算法运用于某实际大型斜拉桥主梁结构的频率和模态振型识别过程中。结果表明,所提改进算法识别的频率值结果与理论值(MIDAS有限元结果)以及实际值(现场动力特性实测结果)间的误差均在5%以内,且识别的模态振型图与理论模态振型图具有很高的相似性。

Abstract

In order to better identify the modal parameters of existing bridge structures. Moreover, in view of the shortcomings of the existing Data-driven Stochastic Subspace Identification(DATA SSI algorithm), which is unable to achieve automatic screening of true and false modes in the stability diagram. A new intelligent identification algorithm of modal parameters is proposed. Firstly, the sliding window technology is adopted to achieved the division of input signals, so as to avoid the false modes and modal omission as much as possible. Secondly, the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm is used to fulfill the intelligent screening of the real modes in the stability diagram. Finally, the proposed algorithm is applied to a real large cable-stayed bridge for identifying the frequency and mode shapes of the main beam. The results show that the errors among the frequency value which is identified by the improved algorithm, the theoretical value (It means MIDAS finite element results) and the actual value (field dynamic characteristic measurement result) are within 5% separately, and the identified mode shape diagram has a high similarity with the theoretical mode shape diagram.

关键词

桥梁结构 / 随机子空间算法 / 滑窗原理 / 密度聚类算法 / 稳定图

Key words

Bridge structure / Stochastic Subspace Identification / Sliding-window theory / Density-based spatial clustering algorithm / Stability diagram

引用本文

导出引用
陈永高1,钟振宇1,2,罗晓峰1,2. 基于滑窗OPTICS算法和DATA-SSI算法的桥梁模态参数智能化识别[J]. 振动与冲击, 2024, 43(7): 18-29
CHEN Yonggao1, ZHONG Zhenyu1,2, LUO Xiaofeng1,2. Intelligent identification of bridge modal parameters based on sliding-window OPTICS algorithm and DATA-SSI algorithm[J]. Journal of Vibration and Shock, 2024, 43(7): 18-29

参考文献

[1] 周筱航,单德山,等. 多输入多输出桥梁结构时变模态参数的滑窗时域识别[J]. 应用基础与工程科学学报,2019(3): 230-242. ZHOU Xiaohang, SHAN Deshan, et al. Sliding window Time-domain method for identifying Time-varying modal parameter of multi-input and multi-output bridge structure[J].Journal of Basic Science and Engineering. 2019(3): 230-242. [2] 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. [3] 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. 2013, 139(6):724-736. [4] 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 & Vibration, 2015, 337:45-70. [5] 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. [6] Bakir P G. Automation of the stabilization diagrams for subspace based system identification[J]. Expert Systems with Application, 2011, 38(12): 14390-14397. [7] 孙国富.基于模糊聚类的模态参数自动识别[J].振动与冲击, 2010, 29(9): 86-88. SUN Guofu. Automatic Identification of Modal Parameters Based on the Fuzzy Clustering Analysis [J]. Journal of Vibration and Shock, 2010, 29 (9): 86-88. [8] 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 Jing. 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. [9]姜金辉,陈国平,张方,等. 模糊聚类法在试验模态参数识别分析中的应用[J].南京航空航天大学学报,2009,41(3):344-347. JIANG Jinhui,CHEN Guoping, ZHANG Fang, et al. Application of fuzzy clustering theory in experimental modal parameter identification analysis [J].Journal of Nanjing University of Aeronautics and Astronautics,2009,41(3):344-347. [10]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):92-96. TANG Baoping, ZHANG Guowen, CHEN Zhuo. Automatic Identification of Stochastic Subspace Modal Parameter Based on Hierarchical Clustering [J]. Journal of Vibration and Shock.2012, 31 (10) :92-96. [11] 李玉刚, 叶庆卫, 周宇, 等. 基于模态参数提取的随机子空间辨识算法改进[J]. 中国机械工程, 2017, 28(01): 69-74. LI Yugang, YE Qingwei, ZHOU Yu, et al. Improvement of SSI Algorithm Based on Extraction of Modal Parameters [J]. China Mechanical Engineering 2017, 28(01): 69-74. [12]单豪良,陈永高,孙泽阳.基于DBSCAN算法的改进确定-随机子空间模态参数识别算法[J].振动与冲击,2022,41(11):156-163 SHAN Hanliang, CHEN Yonggao, SUN Zeyang. Improved CDSI algorithm based on DBSCAN algorithm. Journal of Vibration and Shock. 2022,41(11):156-163. [13]张付志,常俊风,周全强.基于模糊C均值聚类的环境感知推荐算法[J].计算机研究与发展,2013,50(10):2185-2194. ZHANG Fuzhi, CHANG Junfeng, ZHOU Quanqiang. Context-Aware Recommendation Algorithm Based on Fuzzy C-Means Clustering[J].Journal of Computer Research and Development, 2013,50(10) :2185-2194. [14]宋金玉,郭一平,王斌. DBSCAN聚类算法的参数配置方法研究[J].计算机技术与发展,2019,2019(5):44-48. SONG Jinyu, Guo Yiping, Wang Bin. Reserach on parameter configiruation method of DBSCAN clustering algorithm.computer technology and development[J]. 2019,2019(5):44-48. [15]唐东明.聚类分析及其应用研究[D].电子科技大学,2010. TANG Dongming. Cluster analysis and its application [D]. University of Electronic Science and Technology of China, 2010. [16] Boroschek R L, Bilbao J A. Interpretation of stabilization diagrams using density based clustering algorithm[J].Engineering Structures, 2019, 178(JAN.1): 245-257. [17]陈栋军.确定-随机子空间模态参数识别研究[D].西南交通大学,2016. CHEN Dongjun. Modal Parameters ldentification by Combined Determinisic-Stochastic Subspace Identification Method [D]. Southwest Jiaotong University,2016. [18]Reynders E, Houbrechts J, Roeck G D. Fully automated (operational) modal analysis[J]. Mechanical Systems and Signal Processing, 2012, 29: 228-250. [19]Magalhães F, Cunha A, Caetano E. Online automatic identification of the modal parameters of a long span arch bridge[J]. Mechanical Systems & Signal Processing, 2009, 23(2): 316-329.

PDF(4367 KB)

Accesses

Citation

Detail

段落导航
相关文章

/