基于博弈K均值聚类的模态参数自动识别算法

PDF(1795 KB)

振动与冲击 ›› 2023, Vol. 42 ›› Issue (24) : 92-100.

论文

郭鹏1，李东升1,郭鑫2

作者信息 +

Game-based K-means clustering for automated modal parameters identification

GUO Peng1，LI Dongsheng1，GUO Xin2

Author information +

文章历史 +

摘要

为提高基于随机子空间法（Stochastic subspace identification, SSI）模态参数识别的自动化程度，提出了基于博弈K均值聚类（Game-based k-means, GBK）的两阶段模态参数自动识别算法。首先，对采集到的数据通过SSI识别出大量待分析的极点并根据所提出的硬性指标对虚假模态进行初步剔除；其次，将前后两阶模态的多类型偏差指标构造成特征矩阵进行GBK聚类，进一步将剩余虚假模态进行剔除；之后，将提取的结构模态进行层次聚类得到各阶模态；随后将提出的方法通过六自由度质量-弹簧结构数值模型进行验证，最后，将此算法应用于某大跨悬索桥的实际监测数据中进行模态参数自动识别，进一步验证了该方法在实际工程中对采集到的海量数据进行自动化识别的可行性和适用性。

Abstract

In order to improve the automation of modal parameter identification based on Stochastic Subspace Identification (Stochastic subspace identification, SSI), a two-stage automatic modal parameter identification algorithm based on game-based k-means (Game-based k-means, GBK) is proposed. Firstly, identify mode candidates from a large number of system orders through SSI for the collected data and remove certainly spurious modes using hard validation criteria. Secondly, the multi type deviation indicator of two adjacent poles is constructed into a feature vector matrix for GBK clustering, and the spurious modes are further eliminated. Then, the extracted structural modes are hierarchically clustered to obtain each order of modes; A six-degree-of-freedom mass-spring structure numerical model is then used to validate the suggested approach. Finally, it is applied to the actual monitoring data of a long-span suspension bridge for automatic modal parameter identification, which further verifies the feasibility and applicability of this method for automatic identification of massive data collected in practical engineering.

导出引用

郭鹏1，李东升1,郭鑫2. 基于博弈K均值聚类的模态参数自动识别算法[J]. 振动与冲击, 2023, 42(24): 92-100

GUO Peng1，LI Dongsheng1，GUO Xin2. Game-based K-means clustering for automated modal parameters identification[J]. Journal of Vibration and Shock, 2023, 42(24): 92-100

参考文献

[1] Magalhaes F, Cunha A, Caetano E. Online automatic identification of the modal parameters of a long span arch bridge[J]. Mechanical Systems and Signal Processing, 2009, 23(2): 316-329.
[2] Zhang Y, Kurata M, Lynch J P. Long-term modal analysis of wireless structural monitoring data from a suspension bridge under varying environmental and operational conditions: System design and automated modal analysis[J]. Journal of Engineering Mechanics, 2017, 143(4): 04016124.
[3] Yang X M, Yi T H, Qu C X, et al. Automated eigensystem realization algorithm for operational modal identification of bridge structures[J]. Journal of Aerospace Engineering, 2019, 32(2): 04018148.
[4] Qu C X, Yi T H, Li H N. Mode identification by eigensystem realization algorithm through virtual frequency response function. Structural Control and Health Monitoring. 2019 Oct;26(10):e2429.
[5] Reynders E, Houbrechts J, De Roeck G. Fully automated (operational) modal analysis[J]. Mechanical Systems and Signal Processing, 2012, 29: 228-250.
[6] Neu E, Janser F, Khatibi A A, et al. Fully automated operational modal analysis using multi-stage clustering[J]. Mechanical Systems and Signal Processing, 2017, 84: 308-323.
[7] Sun M, Makki Alamdari M, Kalhori H. Automated operational modal analysis of a cable-stayed bridge[J]. Journal of Bridge Engineering, 2017, 22(12): 05017012.
[8] Mao J X, Wang H, Fu Y G, et al. Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge[J]. Structural Control and Health Monitoring, 2019, 26(10): e2430.
[9] Qu C X, Yi T H, Li H N, et al. Closely spaced modes identification through modified frequency domain decomposition. Measurement. 2018 Nov 1;128:388-92.
[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 Bao-ping, ZHANG Guo-wen, CHEN Zhuo. Automatic stochastic subspace identification of modal parameters based on the hierarchical clustering method [J]. Journal of vibration and shock, 2012, 31(10): 92-96.
[11] Peeters B, De Roeck G. Reference-based stochastic subspace identification for output-only modal analysis[J]. Mechanical systems and signal processing, 1999, 13(6): 855-878.
[12] Zhao W L, Deng C H, Ngo C W. k-means: A revisit[J]. Neurocomputing, 2018, 291: 195-206.
[13] Rezaee M J, Eshkevari M, Saberi M, et al. GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game[J]. Knowledge-Based Systems, 2021, 213: 106672.
[14] Nash Jr J F. The bargaining problem[J]. Econometrica: Journal of the econometric society, 1950: 155-162.
[15] Yaghoubi V, Vakilzadeh M K, Abrahamsson T J S. Automated modal parameter estimation using correlation analysis and bootstrap sampling[J]. Mechanical Systems and Signal Processing, 2018, 100: 289-310.
[16] Box G E P, Cox D R. An analysis of transformations[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1964, 26(2): 211-243.
[17] 章关永,朱乐东. 虎门大桥主桥自振特性测定[J]. 同济大学学报：自然科学版, 1999, 27(2):4.
Zhang Guan-yong, Zhu Le-dong, Measurement of natural vibration characteristics of main bridge of Humen Bridge [J]. Journal of Tongji University (Natural Science), 1999, 27(2):4.