基于随机减量法的分布式结构模态参数识别

摘要
图/表
参考文献(17)
相关文章 (15)

全文: PDF (891 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要分区进行平行分析处理的技术已成为大型结构密集布置无线智能传感器网络检测结构系统的重要任务。本文提出基于随机减量法的分布式数据采集和模态识别方法。以两边简支板模型试验为例，采用ISM400无线智能传感器，通过自然激励法获得测试结构的响应信号，计算随机减量函数，然后运用特征系统实现算法提取系统的状态空间参数，并结合稳定图的方法剔除虚假模态，识别出结构的模态性能参数。以模态置信度为判据对比分析分布式算法与集中式算法的识别效果，结果表明两种算法吻合良好。

	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 ：随机减量法, 分布式传感器网络, 特征系统实现算法, 稳定图, 模态识别

Abstract：Technology for decentralized processing and parallel analysis is essential to realize a dense array of wireless smart sensors networks on large-scale civil structures. This paper presents a decentralized data aggregation approach for system identification based on the random decrement technique (RDT). The performance of RDT–based decentralized data aggregation is experimentally investigated using a two side simply supported plate model. Using ISM400 wireless smart sensors, the random decrement function are obtained from the measured acceleration time histories of the plate by adopting natural excitation technique (NExT). A time domain algorithms integrating NExT and eigensystem realization algorithm (ERA), combined with the method of stability diagram (SD) to eliminate the false mode, was applied to identify modal parameters of the plate. The identification result was compared with that based on the centralized method. The global mode shapes are close to those from centralized method according to modal assurance criterion (MAC).

Key words： random decrement technique (RDT) distributed sensor networks eigensystem realization algorithm ( ERA) stabilization diagram (SD) modal identification

收稿日期: 2016-04-20 出版日期: 2017-08-28

引用本文:

逯静洲1 Sung Han Sim 2 Billie F.Spencer, Jr.3 . 基于随机减量法的分布式结构模态参数识别[J]. 振动与冲击, 2017, 36(17): 48-54.
LU Jingzhou1, Sung Han Sim2, Billie F. Spencer, Jr.3. Distribution Modal Identification based on Radom Decrement Technique. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(17): 48-54.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2017/V36/I17/48

[1] Sung-Han Sim and B F Spencer Jr. Decentralized strategies for monitoring structures using wireless smart sensor networks[R]. NSEL Report Series, No. 019, University of Illinois at Urbana-Champaign, U.S.A., 2010.
[2] Nagayama T and Spencer B F Jr. Structural health monitoring using smart sensors[R]. NSEL Report Series, No. 001 , University of Illinois at Urbana-Champaign, U.S.A., 2007.
[3] 刘宇飞，辛克贵，樊健生，崔定宇. 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 Kegui, Fan Jiansheng, Cui Dingyu. A review of structure modal identification methods through ambient excitation[J]. Engineering Mechanics, 214,31(4):46-53.
[4] Peeters B and De Roeck G. Reference-based stochastic subspace identification in civil engineering[J]. Inverse Problems in Engineering, 2000, 8: 47-7.
[5] 张敏，谢慧才，Sung Han Sim , B F Spencer Jr. 基于随机子空间的分布式结构模态参数识别[J].深圳大学学报，2009，26（4）：394-399.
Zhang Min, Xie Huicai, Sung-Han Sim BF Spencer Jr. Distribution modal identification based on stochastic subspace method[J]. Journal of Shenzhen University Science and Engineering. 2009,26(4):394-399.
[6] 张敏，谢慧才，Sung Han Sim , B F Spencer Jr. 基于分布式传感网络的结构模态识别方法[J].土木工程学报，2010，43（3）：106-110.
Zhang Min, Xie Huicai, Sung-Han Sim BF Spencer Jr. Modal identification technique based on distributed sensor networks[J]. China Civil Engieering Journal. 2010,43(3):106-110.
[7] Cole H A． On-the-line analysis of random vibration[C]. AIAA / ASME 9th Structure, Structural Dynamics and Materials Conference, AIAA paper, 1968:268-288.
[8] Cole H A． Method and apparatus for measuring the damping characteristic of a structure[P]. United State Patent, No:3620069,1971.
[9] 陈德成，姜节胜．随机减量技术的方法与理论[J]．振动与冲击，1984，3(4) :31-39.
Chen Decheng, Jiang Jiesheng. Method and Theory of Random Decrement Technique[J]. Journal of Vibration and Shock, 1984,3(4):31-39.
[10] 刘剑锋，李元兵，张启伟. 基于改进随机减量法和小波变换的结构模态参数识别[J]. 同济大学学报，2015，43（10）：1447-1453.
Liu Jianfeng, Li Yuanbing， Zhang Qiwei. Identification for modal parameters based on improved random decrement technique and wavelet transform[J]. Journal of Tongji University, 2015,43(10):1447-1453.
[11] 罗钧，刘纲，黄宗明. 基于随机减量法的非平稳激励下模态参数识别[J]. 振动与冲击，2015，34（21）：19-24.
Luo Jun, Liu Gang, Huang Zongming. Modal parametric identification under non-stationary excitation based on random decrement method[J]. Journal of vibration and shock, 2015,34(21):19-24.
[12] Sung Han Sim, Juan Francisco Carbonell-Marquez, B.F. Spencer Jr, etal. Decentralized random decrement technique for efficient data aggregation and system identification in wireless smart sensor networks[J]. Probabilistic Engineering Mechanics, 2011,26:81-91.
[13] 黄方林，何旭辉，陈政清等.随机减量法在斜拉桥拉索模态参数识别中的应用[J].机械强度，2002，，24（3）:331-334.
Huang Fanglin, He Xuhui, Chen Zhengqing, et al. Application of random decrement technique to modal parameter identification of cables for a cable-stayed bridge[J]. Journal of Mechanical Strength. 2002,24(3):331-334.
[14] Vandiver J K，Dunwoody A B，Campbell R B，et al． A mathematical basis for the random decrement vibration signature analysis technique[J]． Transactions of the ASME Journal of Mechanical Design，1982，104: 307-313.
[15] Brincker R，Krenk S，Kirkegaard P H，et al． Identification of dynamical properties from correlation function estimates[J]．Bygningsstatiske Meddelllser，1992，63( 1) : 1-38.
[16] Asmussen JC. Modal analysis based on the random decrement technique application to civil engineering structures[D]. Ph.D. thesis. Denmark: University of Aalborg; 1997.
[17] Juan J N, Pappa R S. An elgensystem realization algorithm (ERA) for modal parameter identification and model reduction [J]. Journal of Guidance Control and Dynamics, 1985, 8 (5): 620-627.