[1]吴睿,廖聿宸,宗周红,等.基于GNSS信号的随机子空间模态参数识别方法[J].东南大学学报(自然科学版),2020,50(6):1045-1051.[doi:10.3969/j.issn.1001-0505.2020.06.008]
　Wu Rui,Liao Yuchen,Zong Zhouhong,et al.Stochastic subspace modal parameter identification method based on GNSS signals[J].Journal of Southeast University (Natural Science Edition),2020,50(6):1045-1051.[doi:10.3969/j.issn.1001-0505.2020.06.008]
点击复制

基于GNSS信号的随机子空间模态参数识别方法()
分享到：

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

卷:
50
期数:
2020年第6期
页码:
1045-1051
栏目:
交通运输工程
出版日期:
2020-11-20

文章信息/Info

Title:
Stochastic subspace modal parameter identification method based on GNSS signals
作者:
吴睿1; 廖聿宸1; 宗周红1; 张坤2
1东南大学爆炸安全防护教育部工程研究中心, 南京 211189; 2中交第二公路勘察设计研究院有限公司, 武汉430056
Author(s):
Wu Rui1;  Liao Yuchen1;  Zong Zhouhong1;  Zhang Kun2
1Engineering Research Center of Safety and Protection of Explosion and Impact of Ministry of Education, Southeast University, Nanjing 211189, China
2 CCCC Second Highway Survey, Design and Research Institute Co., Ltd., Wuhan 430056, China
关键词:
GNSS;  小波阈值降噪;  随机子空间方法;  模态参数识别
Keywords:
global navigation satellite system(GNSS);  wavelet threshold noise reduction;  stochastic subspace method;  modal parameter identification
分类号:
U446.2
DOI:
10.3969/j.issn.1001-0505.2020.06.008
摘要:
为了从全球卫星定位系统(GNSS)监测信号中获得桥梁结构模态特性信息,提出了一种基于GNSS信号的随机子空间模态参数识别方法.通过小波分解将GNSS信号分频,对各频段信号采用小波阈值降噪方法进行处理,并采用数据驱动的随机子空间模态参数识别方法对处理后的信号进行结构模态参数识别.以灌河大桥健康监测为例,对比研究了基于GNSS信号和基于加速度信号的随机子空间模态参数识别方法的模态频率识别效果.结果表明:通过分频段小波阈值降噪可有效滤除GNSS信号噪声,所提方法可以识别出灌河大桥多阶模态频率,与基于加速度信号的随机子空间模态参数识别方法识别结果吻合良好,最大误差在3.44%以内.
Abstract:
To obtain the bridge structure modal characteristic information from the signals of the global navigation satellite system(GNSS), a stochastic subspace modal parameter identification method based on GNSS signals was proposed. The GNSS signals were divided by wavelet decomposition, and the wavelet threshold noise reduction method was used to deal with the signal in each dependent frequency band. Then, the data-driven stochastic subspace modal parameter identification method was applied to identify the structural modal parameters. Based on the health monitoring of the Guanhe Bridge, the modal frequency recognition results of the stochastic subspace modal parameter identification method based on the GNSS signals and those based on the acceleration signals were compared. The results indicate that the noise in the GNSS signals can be effectively filtered by the wavelet threshold noise reduction method in each frequency band. The proposed method can identify the multi-modal frequencies of the Guanhe Bridge. The recognition results of the proposed method are in good agreement with those of the stochastic subspace modal parameter identification method based on the acceleration signals, and the maximum error is within 3.44%.

参考文献/References:

[1] Yi T H, Li H N,Gu M. Recent research and applications of GPS based technology for bridge health monitoring[J]. Science China Technological Sciences, 2010, 53(10): 2597-2610. DOI:10.1007/s11431-010-4076-3.
[2] Yu J Y,Meng X L, Yan B F, et al. Global navigation satellite system-based positioning technology for structural health monitoring: A review[J]. Structural Control and Health Monitoring, 2020, 27(1): e2467. DOI:10.1002/stc.2467.
[3] Kijewski-Correa T, Kareem A, Kochly M. Experimental verification and full-scale deployment of global positioning systems to monitor the dynamic response of tall buildings[J]. Journal of Structural Engineering, 2006, 132(8): 1242-1253. DOI:10.1061/(asce)0733-9445(2006)132:8(1242).
[4] 姚连璧, 姚平, 王人鹏, 等. 南浦大桥形变GPS动态监测试验及结果分析[J]. 同济大学学报(自然科学版), 2008, 36(12): 1633-1636, 1664. DOI:10.3321/j.issn:0253-374X.2008.12.007.
Yao L B, Yao P, Wang R P, et al. GPS-based dynamic monitoring and analysis of Nanpu bridge deformation[J]. Journal of Tongji University(Natural Science), 2008, 36(12): 1633-1636, 1664. DOI:10.3321/j.issn:0253-374X.2008.12.007. (in Chinese)
[5] 余加勇, 邵旭东, 孟晓林, 等. 联合GNSS和加速度计的桥梁结构动态监测试验[J]. 中国公路学报, 2014, 27(2): 62-69. DOI: 10.19712/j. cnki. 1001-7372.2014.02.008.
Yu J Y, Shao X D, Meng X L, et al. Experimental research on dynamic monitoring of bridges using GNSS and accelerometer[J]. China Journal of Highway and Transport, 2014, 27(2): 62-69. DOI:10.19712/j. cnki. 1001-7372.2014.02.008. (in Chinese)
[6] 熊春宝, 路华丽, 朱劲松, 等. 基于GPS-RTK和加速度计的桥梁动态变形监测试验[J]. 振动与冲击, 2019, 38(12): 69-73. DOI:10.13465/j. cnki. jvs. 2019.12.010.
Xiong C B, Lu H L, Zhu J S, et al. Dynamic deformation monitoring of bridge structures based on GPS-RTK and accelerometers[J]. Journal of Vibration and Shock, 2019, 38(12): 69-73. DOI:10.13465/j. cnki. jvs. 2019.12.010. (in Chinese)
[7] 戴建彪, 岳东杰, 汤同旭, 等. 桥梁索塔GPS监测信号的小波分析[J]. 测绘科学, 2019, 44(9): 151-157. DOI: 10. 16251/j. cnki. 1009-2307. 2019. 09.022.
Dai J B, Yue D J, Tang T X, et al. Wavelet analysis of GPS monitoring signals of bridge towers [J]. Science of Surveying and Mapping, 2019, 44(9): 151-157. DOI:10.16251/j.cnki.1009-2307.2019.09.022. (in Chinese)
[8] 姚平. GPS在桥梁监测中的应用研究[D]. 上海:同济大学, 2008.
　　Yao P. Research on the application of GPS in bridge monitoring[D]. Shanghai: Tongji University, 2008.(in Chinese)
[9] Yi T H, Li H N, Zhao X Y. Noise smoothing for structural vibration test signals using an improved wavelet thresholding technique[J]. Sensors, 2012, 12(8): 11205-11220. DOI:10.3390/s120811205.
[10] Yi T H, Li H N,Gu M. Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge[J]. Measurement: Journal of the International Measurement Confederation, 2013, 46(1): 420-432.
[11] 王东振, 曲国庆, 殷海涛. 高频GPS桥梁动态监测及数据特征信息识别[J]. 山东理工大学学报(自然科学版), 2016, 30(2): 45-48. DOI: 10. 13367/j.cnki.sdgc.2016.02.011.
Wang D Z, Qu G Q, Yin H T. High-frequency GPS bridge dynamic monitoring and data feature information identification [J]. Journal of Shandong University of Technology(Natural Science Edition), 2016, 30(2): 45-48. DOI:10.13367/j.cnki.sdgc.2016.02.011. (in Chinese)
[12] 刘镇清, 黄瑞菊. 小波变换及其应用[J]. 无损检测, 2001, 23(4): 174-177.
　　Liu Z Q, Huang R J. Wavelet transform and its application[J]. Nondestructive Testing, 2001, 23(4): 174-177.(in Chinese)
[13] Daubechies I. Orthonormal bases of compactly supported wavelets[J]. Communications on Pure and Applied Mathematics, 1988, 41(7): 909-996. DOI:10.1002/cpa.3160410705.
[14] Mallat S. A theory for multiresolution signal decomposition: The wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693. DOI:10.1109/34.192463.
[15] Vetterli M, Herley C. Wavelets and filter banks: Theory and design[J]. IEEE Transactions on Signal Processing, 1992, 40(9): 2207-2232. DOI:10.1109/78.157221.
[16] 张文丽, 曲俊海, 李静, 等. 自适应小波阈值去噪在光纤陀螺上的应用研究[J]. 科技创新与应用, 2020(5): 178-179.
　　Zhang W L, Qu J H, Li J, et al. Application research of adaptive wavelet threshold denoising on fiber optic gyro [J]. Science and Technology Innovation and Application, 2020(5): 178-179.(in Chinese)
[17] Moschas F, Stiros S. Measurement of the dynamic displacements and of the modal frequencies of a short-span pedestrian bridge using GPS and an accelerometer[J]. Engineering Structures, 2011, 33(1): 10-17. DOI:10.1016/j.engstruct.2010.09.013.
[18] Abbate A, Koay J, Frankel J, et al. Signal detection and noise suppression using a wavelet transform signal processor: Application to ultrasonic flaw detection[J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 1997, 44(1): 14-26. DOI:10.1109/58.585186.
[19] 甘若, 陈天伟, 郑旭东, 等. 改进的小波阈值函数对变形监测数据的去噪研究[J]. 大地测量与地球动力学, 2020, 40(1): 17-22. DOI: 10. 14075/j. jgg.2020.01.004.
Gan R, Chen T W, Zheng X D, et al. Research on denoising of deformation monitoring data with improved wavelet threshold function [J]. Geodetics and Geodynamics, 2020, 40(1): 17-22. DOI:10.14075/j.jgg.2020.01.004. (in Chinese)
[20] 张笑华. 结构环境振动模态参数识别随机子空间方法与应用[D]. 福州:福州大学, 2006.
　　Zhang X H. Stochastic subspace method and application of structural environmental vibration modal parameter identification [D]. Fuzhou: Fuzhou University, 2006.(in Chinese)
[21] 常军, 张启伟, 孙利民. 稳定图方法在随机子空间识别模态参数中的应用[J]. 工程力学, 2007, 24(2): 39-44. DOI: 10.3969/j.issn.1000-4750.2007.02.007.
Chang J, Zhang Q W, Sun L M. Application of stabilization diagram for modal parameter identification using stochastic subspace method[J]. Engineering Mechanics, 2007, 24(2): 39-44. DOI:10.3969/j.issn.1000-4750.2007.02.007. (in Chinese)
[22] 徐敏. 桥梁结构模态参数识别研究[D]. 成都:西南交通大学, 2010.
　　Xu M. Research on bridge structure modal parameter identification [D]. Chengdu: Southwest Jiaotong University, 2010.(in Chinese)
[23] 王树青, 王典鹤, 林裕裕, 等. 一种模态参数识别的虚假模态剔除技术[J]. 中国海洋大学学报(自然科学版), 2012, 42(9): 97-101. DOI: 10. 16441/j. cnki. hdxb. 2012.09.016.
Wang S Q, Wang D H, Lin Y Y, et al.A false modal elimination technique for modal parameter identification[J]. Journal of Ocean University of China(Natural Science), 2012, 42(9): 97-101. DOI:10. 16441/j. cnki. hdxb. 2012.09.016. (in Chinese)
[24] 李湛, 李鹏飞, 姜震宇, 等. 不同激励模式下桥梁实测阻尼比差异[J]. 振动与冲击, 2016, 35(3): 62-67. DOI: 10. 13465/j.cnki.jvs.2016.03.010.
Li Z, Li P F, Jiang Z Y, et al. Differences in measured damping ratios of bridges under different excitation modes[J]. Vibration and Shock, 2016, 35(3): 62-67. DOI:10. 13465/j.cnki.jvs.2016.03.010. (in Chinese)
[25] 张坤. 基于健康监测的斜拉桥结构在线模型修正和确认方法研究[D]. 南京:东南大学, 2019.
　　Zhang K. Research on online model modification and confirmation method of cable-stayed bridge structure based on health monitoring [D]. Nanjing: Southeast University, 2019.(in Chinese)

备注/Memo

备注/Memo:
收稿日期: 2020-05-07.
作者简介: 吴睿(1996—),女,硕士生;宗周红(联系人),男,博士,教授,博士生导师,zongzh@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51678141).
引用本文: 吴睿,廖聿宸,宗周红,等.基于GNSS信号的随机子空间模态参数识别方法[J].东南大学学报(自然科学版),2020,50(6):1045-1051. DOI:10.3969/j.issn.1001-0505.2020.06.008.
更新日期/Last Update: 2020-11-20