[1]贺敏,梁鹏,李琳国,等.基于两阶段改进的FCM法的模态参数自动识别[J].东南大学学报(自然科学版),2019,49(5):940-948.[doi:10.3969/j.issn.1001-0505.2019.05.018]
 He Min,Liang Peng,Li Linguo,et al.Automatic modal parameter identification based on improved two-stage FCM algorithm[J].Journal of Southeast University (Natural Science Edition),2019,49(5):940-948.[doi:10.3969/j.issn.1001-0505.2019.05.018]
点击复制

基于两阶段改进的FCM法的模态参数自动识别()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
49
期数:
2019年第5期
页码:
940-948
栏目:
交通运输工程
出版日期:
2019-09-20

文章信息/Info

Title:
Automatic modal parameter identification based on improved two-stage FCM algorithm
作者:
贺敏1梁鹏12李琳国1叶春生1
1长安大学公路学院, 西安 710064; 2长安大学公路大型结构安全教育部工程研究中心, 西安 710064
Author(s):
He Min1 Liang Peng12 Li Linguo1 Ye Chunsheng1
1School of Highway, Chang’an University, Xi’an 710064, China
2Engeeirng Reacher Center for Large Highway Structure Safety of Ministry of Education, Chang’an University, Xi’an 710064, Chin
关键词:
模态识别 改进模糊C均值聚类法 稳定图 模态参数不确定度 自动识别
Keywords:
modal identification improved fuzzy c-means algorithm stabilization diagram uncertainty of modal parameters automatic identification
分类号:
U446.1;U441.3
DOI:
10.3969/j.issn.1001-0505.2019.05.018
摘要:
为解决基于稳定图的模态参数自动识别中存在人工干预、参数设定不统一的问题,提出两阶段改进的稳定图自动识别方法.首先,将模态参数不确定度指标引入第一阶段虚假模态参数剔除过程,实现最大程度虚假模态参数剔除;然后,基于改进的FCM算法,采用迭代策略计算不同聚类数目的隶属度矩阵,构造累积邻接矩阵,并结合图切割算法解析累积邻接矩阵,自动确定最佳聚类数目,实现稳定图自动识别;最后,将提出的改进方法运用在Z24桥Benchmark模型和悬索桥实测数据上,验证了所提方法的可行性.研究结果表明:模态参数不确定度相较于传统指标对虚假模态具有更强的辨别能力;基于改进的FCM算法不需要任何人工调整的参数就能自动识别稳定轴,且具有较强的鲁棒性.改进算法在默认参数下准确识别得到Z24桥和悬索桥的模态参数,表明提出的改进算法可以用于桥梁健康监测的模态参数自动识别过程.
Abstract:
An improved two-stage clustering method was proposed for automatic stabilization diagram identification. Firstly, the uncertainty of modal parameters was introduced to eliminate the false modal results. This process eliminated most of the false results to provide a clearer stabilization diagram for automatic identification. Secondly, an improved fuzzy c-means(FCM)algorithm was introduced to interpret the stabilization diagram. The algorithm identified the optimal cluster number by an iteration process. Firstly, many clustering results were obtained.Then, these different results were integrated as a judgement matrix. And an iterative graph-partitioning process was implemented to identify the desired cluster number and the final identification result. Finally, the algorithm was validated through Z24 Benchmark and a suspension bridge. The results show that the uncertainty of modal parameters discriminate the false modal parameters better than the traditional index. The proposed algorithm can successfully interpret the stabilization diagram without any user-specified parameter, thus showing strong robustness. The algorithm can be applied in automatic modal identification for bridge health monitoring.

参考文献/References:

[1] Peeters B, de Roeck G. One-year monitoring of the Z24-bridge: Environmental effects versus damage events[J]. Earthquake Engineering & Structural Dynamics, 2001, 30(2): 149-171. DOI:10.1002/1096-9845(200102)30:2149::aid-eqe1>3.0.co;2-z.
[2] 周毅, 孙利民, 谢谟文. 运营环境作用对跨海大桥模态频率的影响研究[J]. 工程力学, 2018, 35(z1): 34-39. DOI:10.6052/j.issn.1000-4750.2017.05.S017.
Zhou Y, Sun L M,Xie M W. Influence of operational and environmental actions on modal frequencies of a sea-crossing bridge[J]. Engineering Mechanics, 2018, 35(z1): 34-39. DOI:10.6052/j.issn.1000-4750.2017.05.S017. (in Chinese)
[3] 樊可清, 倪一清, 高赞明. 大跨度桥梁模态频率识别中的温度影响研究[J]. 中国公路学报, 2006, 19(2): 67-73. DOI:10.3321/j.issn:1001-7372.2006.02.012.
Fan K Q, Ni Y Q, Gao Z M. Research on temperature influences in long-span bridge eigenfrequencies identification[J]. China Journal of Highway and Transport, 2006, 19(2): 67-73. DOI:10.3321/j.issn:1001-7372.2006.02.012. (in Chinese)
[4] 宗周红, 钟儒勉, 郑沛娟, 等. 基于健康监测的桥梁结构损伤预后和安全预后研究进展及挑战[J]. 中国公路学报, 2014, 27(12): 46-57. DOI:10.19721/j.cnki.1001-7372.2014.12.006.
Zong Z H, Zhong R M, Zheng P J, et al. Damage and safety prognosis of bridge structures based on structural health monitoring: Progress and challenges[J]. China Journal of Highway and Transport, 2014, 27(12): 46-57. DOI:10.19721/j.cnki.1001-7372.2014.12.006. (in Chinese)
[5] van Overschee P, de Moor B. Subspace identification for linear systems[M]. Boston, MA, USA: Springer US, 1996:57-93.
[6] van der Auweraer H, Peeters B. Discriminating physical poles from mathematical poles in high order systems: Use and automation of the stabilization diagram[C]//Proceeding of the 21st IEEE Instrumentation and Measurement Technology Conference.Como, Italy, 2004, 3: 2193-2198. DOI:10.1109/IMTC.2004.1351525.
[7] Reynders E, Houbrechts J, de Roeck G. Fully automated(operational)modal analysis[J]. Mechanical Systems and Signal Processing, 2012, 29: 228-250. DOI:10.1016/j.ymssp.2012.01.007.
[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. DOI:10.3969/j.issn.1000-3835.2013.04.024.
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. DOI:10.3969/j.issn.1000-3835.2013.04.024. (in Chinese)
[9] 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. DOI:10.3969/j.issn.1000-3835.2010.09.020.
Sun G F. Automatic modal parameters identification based on fuzzy clustering[J]. Journal of Vibration and Shock, 2010, 29(9): 86-88. DOI:10.3969/j.issn.1000-3835.2010.09.020. (in Chinese)
[10] 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. DOI:10.3969/j.issn.1005-2615.2009.03.012.
Jiang J H, Chen G P, Zhang F, et al. Application of fuzzy clustering theory in experimental modal parameter identification analysis[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2009, 41(3): 344-347. DOI:10.3969/j.issn.1005-2615.2009.03.012. (in Chinese)
[11] 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. DOI:10.1016/j.ymssp.2016.07.031.
[12] 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. DOI:10.1061/(asce)be.1943-5592.0001141.
[13] Cabboi A, Magalhães F, Gentile C, et al. Automated modal identification and tracking: Application to an iron arch bridge[J]. Structural Control and Health Monitoring, 2017, 24(1): e1854. DOI:10.1002/stc.1854.
[14] 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. DOI:10.1016/j.engstruct.2012.07.031.
[15] Magalhães F, Cunha Á, 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. DOI:10.1016/j.ymssp.2008.05.003.
[16] 郑沛娟, 林迪南, 宗周红, 等. 基于图论聚类的随机子空间模态参数自动识别[J]. 东南大学学报(自然科学版), 2017, 47(4): 710-716. DOI:10.3969/j.issn.1001-0505.2017.04.014.
Zheng P J, Lin D N,Zong Z H, et al. Automatic stochastic subspace identification of modal parameters based on graph clustering[J]. Journal of Southeast University(Natural Science Edition), 2017, 47(4): 710-716. DOI:10.3969/j.issn.1001-0505.2017.04.014. (in Chinese)
[17] 徐健, 周志祥, 唐亮, 等. 基于谱系聚类分析的桥梁结构模态参数自动化识别方法研究[J]. 振动与冲击, 2017, 36(11): 206-214. DOI:10.13465/j.cnki.jvs.2017.11.033.
Xu J, Zhou Z X, Tang L, et al. Automatic identification of bridge structural modal parameters based on improved EEMD and hierarchical clustering algorithm[J]. Journal of Vibration and Shock, 2017, 36(11): 206-214. DOI:10.13465/j.cnki.jvs.2017.11.033. (in Chinese)
[18] 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. DOI:10.3969/j.issn.1000-3835.2012.10.020.
Tang B P, Zhang G W, Chen Z. Automatic stochastic subspace identification of modal parameters based on hierarchical clustering method[J]. Journal of Vibration and Shock, 2012, 31(10): 92-96. DOI:10.3969/j.issn.1000-3835.2012.10.020. (in Chinese)
[19] Chiuso A, Picci G. The asymptotic variance of subspace estimates[J]. Journal of Econometrics, 2004, 118(1/2): 257-291. DOI:10.1016/s0304-4076(03)00143-x.
[20] 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): 948-969. DOI:10.1016/j.ymssp.2007.10.009.
[21] Döhler M, Mevel L. Efficient multi-order uncertainty computation for stochastic subspace identification[J]. Mechanical Systems and Signal Processing, 2013, 38(2): 346-366. DOI:10.1016/j.ymssp.2013.01.012.
[22] Döhler M, Lam X B, Mevel L. Uncertainty quantification for modal parameters from stochastic subspace identification on multi-setup measurements[J]. Mechanical Systems and Signal Processing, 2013, 36(2): 562-581. DOI:10.1016/j.ymssp.2012.11.011.
[23] Tondreau G, Deraemaeker A. Numerical and experimental analysis of uncertainty on modal parameters estimated with the stochastic subspace method[J]. Journal of Sound and Vibration, 2014, 333(18): 4376-4401. DOI:10.1016/j.jsv.2014.04.039.
[24] Mok P Y, Huang H Q, Kwok Y L, et al. A robust adaptive clustering analysis method for automatic identification of clusters[J]. Pattern Recognition, 2012, 45(8): 3017-3033. DOI:10.1016/j.patcog.2012.02.003.
[25] Maeck J, de Roeck G. Description of z24 benchmark[J]. Mechanical Systems and Signal Processing, 2003, 17(1): 127-131. DOI:10.1006/mssp.2002.1548.
[26] 叶锡均. 基于环境激励的大型土木工程结构模态参数识别研究[D]. 广州: 华南理工大学, 2012.
  Ye X J. Modal parameter identification of large-scale civil engineering structures based on ambient excitation[D]. Guangzhou: South China University of Technology, 2012.(in Chinese)

相似文献/References:

[1]杜秀丽,汪凤泉.非平稳随机激励下线性系统模态识别的小波方法[J].东南大学学报(自然科学版),2006,36(2):319.[doi:10.3969/j.issn.1001-0505.2006.02.029]
 Du Xiuli,Wang Fengquan.Method of modal identification based on wavelet transform in LTI system under non-stationary random excitation[J].Journal of Southeast University (Natural Science Edition),2006,36(5):319.[doi:10.3969/j.issn.1001-0505.2006.02.029]

备注/Memo

备注/Memo:
收稿日期: 2019-02-18.
作者简介: 贺敏(1990—),男,博士生;梁鹏(联系人),男,博士,教授,博士生导师,bridgedoctor@qq.com.
基金项目: 国家自然科学基金资助项目(51878059)、国家重点专项资助项目(2018YFB1600300)、中央高校基本科研业务费专项资金资助项目(300102218406,300102219202)、陕西交通科技项目重点资助项目(15-18K)、广东交通科技计划重大工程专项资助项目(2011-01-001).
引用本文: 贺敏,梁鹏,李琳国,等.基于两阶段改进的FCM法的模态参数自动识别[J].东南大学学报(自然科学版),2019,49(5):940-948. DOI:10.3969/j.issn.1001-0505.2019.05.018.
更新日期/Last Update: 2019-09-20