基于延时随机子空间方法的非白噪声环境激励结构模态参数识别

摘要
图/表
参考文献(27)
相关文章 (4)

全文: PDF (2088 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为了消除非白噪声环境激励在结构模态参数识别结果中产生的虚假模态，引入扩展状态模型，从状态空间方程的角度论证了虚假模态产生的原因；然后，针对一类具有典型性和代表性的（自相关函数在纵坐标轴附近一定范围内有非零值的）非白噪声环境激励信号，在传统随机子空间算法的基础上引入延时指标，建立延时随机子空间方法。数值算例表明延时随机子空间方法能够有效地剔除非白噪声环境激励在模态参数识别结果中产生的虚假模态，放宽了传统模态参数识别方法对环境输入的白噪声假设。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章

关键词 ：结构模态参数识别, 非白噪声环境激励, 随机子空间方法, 延时指标

Abstract：In order to eliminate spurious modes caused by non-white noise ambient inputs, the augmented state space model is introduced to explain how the spurious modes arise in the identified structural modal parameters. For a kind of typical non-white ambient excitations, whose autocorrelation values are nonzeroes near the vertical axis, delay index based stochastic subspace identification method is proposed in this paper, by introducing delay index in the traditional stochastic subspace identification method. Numerical examples verify that this improved method could eliminate spurious modes due to non-white noise inputs, so as to relax the assumption of ambient excitations in traditional operational modal analysis methods.

Key words： modal parameter identification of structures non-white noise ambient excitations stochastic subspace identification delay index

收稿日期: 2014-01-08 出版日期: 2015-04-25

引用本文:

胡异丁 1，李丹 2，任伟新 3，李子兵 4. 基于延时随机子空间方法的非白噪声环境激励结构模态参数识别[J]. 振动与冲击, 2015, 34(8): 71-76.
HU Yi-ding 1 LI Dan 2 REN Wei-xin 3 LI Zi-bing 4 . Modal parameter identification of structures under non-white noise ambient excitations using delay index based stochastic subspace identification method. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(8): 71-76.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2015/V34/I8/71

[1] Van der Auweraer H, Hermans L. Structural modal identification from real operating conditions [J]. Sound and Vibration, 1996, 33(1): 34-41.
[2] 傅志方, 华宏星. 模态分析理论与应用[M]. 上海: 上海交通大学出版社, 2000.
Fu Z F, Hua H X. Theory and application of modal analysis [M]. Shanghai: Shanghai Jiao Tong University Press, 2000.
[3] Bendat J S, Piersol A G. Engineering applications of correlation and spectral analysis (2nd edition) [M]. New York: John Wiley & Sons, 1993.
[4] Brinker R, Zhang L, Andersen P. Modal identification from ambient responses using frequency domain decomposition [C]// Proceedings of 18th International Modal Analysis Conference (IMAC18). San Antonio, USA: SEM, 2000. 625–630.
[5] Peeters B, Guillaume P, Van der Auweraer H, etc. Automotive and aerospace applications of the PolyMAX modal parameter estimation method [C]// Proceedings of the 22th International Modal Analysis Conference (IMAC 22). Dearborn, USA: SEM, 2004.
[6] Andersen P, Brincker R and Kirkegaard P.H. Theory of Covariance Equivalent ARMAV Models of Civil Engineering Structures [C]// Proceedings of the 14th International Modal Analysis Conference (IMAC 14). Dearborn, USA: SEM, 1996. 518-524.
[7] James G H, Garne T G. The natural excitation technique (NExT) for modal parameter extraction from ambient operating structure [J]. The International Journal of Analytical and Experimental Modal Analysis, 1995, 10(4): 260-277.
[8] Van Overschee P, De Moor B. Subspace identification for linear systems: theory, implementation, and applications [M]. Dordrecht: Kluwer Academic Publishers, 1996.
[9] Peeters B. System identification and damage detection in civil engineering [D]. Belgium: Katholieke Universiteit Leuven, 2000.
[10] Ren W X, Zhao T, Harik I E. Experimental and analytical modal analysis of a steel arch bridge [J]. Journal of Structural Engineering, ASCE, 2004, 130(7): 1022-1031.
[11] Peeters B, De Roeck G. Stochastic system identification for operational modal analysis: a review [J]. Journal of Dynamic Systems, Measurement, and Control, ASME, 2001, 123(4): 659-667.
[12] 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 C L, Liu H B, Wang J. 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.
[13] 李丹. 非白噪声环境激励下的结构模态参数识别 [D]. 长沙: 中南大学, 2012.
Li D. Structural modal parameter identification under non-white ambient vibration [D]. Changsha: Central South University, 2012.
[14] Pridham B A. State Space Modeling and Identification of Stochastic Linear Structural Systems [D]. Canada: McMaster University, 2004.
[15] Chiang D Y, Lin C S. Identification of modal parameters from nonstationary ambient vibration data using correlation technique [J]. AIAA Journal, 2008, 46(11): 2752-59.
[16] 李中付, 华宏星. 一种非稳态环境激励下线性结构的模态参数辨识方法[J]. 振动与冲击, 2008, 27(3): 8-12.
Li Z F, Hua H X. Modal parameters identification of linear structures undergoing non-stationary ambient excitation [J]. Journal of Vibration and Shock, 2008, 27(3): 8-12.
[17] Lisowski W. Classification of vibration modes in operational modal analysis [C]// Proceedings of the International Conference on Structural System Identification. Kassel, Germany: 2001. 593-592.
[18] Reynders E, Pintelon R, De Roeck G. Uncertainty bounds on modal parameters obtained from stochastic subspace identification [J]. Mechanical Systems and Signal Processing, 2008, 22(4): 948-69.
[19] Jacobsen N J. Identifying harmonic components in operational modal analysis[C]// Proceedings of 12th International Congress on Sound and Vibration. Lisbon, Portugal: 2005. 1-8.
[20] Brincker R, Zhang L M, Andersen P. Modal identification of output-only systems using frequency domain decomposition [J]. Smart Materials and Structures, 2001, 10( 3): 441-445.
[21] Mohanty P, Rixen D J. Accounting for harmonic excitations in operational modal analysis [C]// Proceedings of the 21st International Modal Analysis Conference (IMAC XXI). Kissimmee, USA: SEM, 2003.
[22] Yu D J, Ren W X. EMD-based stochastic subspace identification of structures from operational vibration measurements [J]. Engineering Structures, 2005, 27(12): 1741-1751.
[23] Goursat M, Mevel L. Covariance subspace identification: Numerical analysis of spurious mode stability [C]// Proceedings of the 26th International Modal Analysis Conference (IMAC-XXVI). Orlando, USA: SEM, 2008. 73-80.
[24] Saeed K, Mechbal N, Coffignal G, Verge M. Recursive modal parameter estimation using output-only subspace identification for structural health monitoring [C]// Proceedings of the 16th Mediterranean Conference on Control and Automation. Ajaccio, France: IEEE, 2008. 77-2.
[25] Li D, Ren W X, Hu Y D. Output-only Modal Parameter Identification Based on Augmented State Space Models [C]// Processings of International Symposium on Innovation and Sustainability of Structures in Civil Engineering (ISISS’2011). Xiamen, China: 2011. 412-417.
[26] Maybeck P S. Stochastic models estimation and control, volume 1-3 [M]. New York: A Subsidiary of Harcourt Brace Jovanovich, 1979.
[27] 禹丹江. 土木工程结构模态参数识别，理论、实现与应用[D]. 福州: 福州大学, 2005.
Yu D J. Modal parameter identification of civil engineering structures – theory, implementation and applications [D]. Fuzhou: Fuzhou University, 2005