An Operational Modal Parameters Identification Algorithm for Structures Based on HHT and RDT
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摘要: 为提高结构自振频率识别精度和识别工作的自动化水平,提出基于希尔伯特-黄变换(HHT)和随机减量技术(RDT)的结构模态参数识别方法。首先利用傅里叶变换、巴特沃斯滤波获得目标频段动力响应,接着用经验模态分解(EMD)获得系列固有模态函数(IMF),采用RDT提取每个IMF的自由衰减振动信号,通过Hilbert变换,获得相位曲线和振幅曲线,最后拟合曲线斜率,获得自振频率和阻尼比。研究发现,无论是线性平稳信号还是非平稳信号,提出的方法在鲁棒性和准确性上均较传统的快速傅里叶变换(FFT)和HHT有优势,且无须人工介入判断模态真伪。对于非线性短信号,为获得更好的分析效果,推荐截取阈值取1.2σ,自由衰减时长取75 s。Abstract: An algorithm, utilizing the Hilbert-Huang Transform (HHT) and the random decrement technique (RDT), was proposed to enhance the precision and automation of identifying the modal parameters of structures under operational conditions. The algorithm involves the use of Fourier transform, Butterworth filter, empirical mode decomposition (EMD), random decrement technique (RDT), and Hilbert transform. Fourier transform and Butterworth filter were applied to obtain the dynamic responses of the target frequency range (DRTFR). EMD was employed to decompose the DRTFR into multiple intrinsic mode functions (IMFs). RDT was utilized to derive the free decay response signal of each IMFs. By subjecting the free decay response signals to Hilbert transform, phase curves and amplitude curves were generated. The slops of the phase curves and the amplitude curves were the frequencies and damping, respectively. The results showed that the proposed algorithm was suitable for processing both stationary linear signals and non-stationary non-linear signals. This algorithm exhibited superior accuracy and robustness compared to the fast Fourier transform (FFT) and HHT. It is recommended to use the intercept threshold of 1.2σ and the free decay duration of 75 seconds for processing the non-stationary non-linear signals.
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[1] 杜彦良,张浩,刘伯奇. 大型铁路客站结构健康监测现状与发展思考[J]. 铁道标准设计,2023,67(3):1-9,16. [2] 罗尧治,赵靖宇. 空间结构健康监测研究现状与展望[J]. 建筑结构学报,2022,43(10):16-28. [3] 孙启刚,宋卓彦,鉴庆之,等. 基于无线传感网的输电铁塔结构健康状态监测技术及应用[J]. 工业建筑,2022,52(10):224-228. [4] 张兴斌,杨昕光,潘蓉,等.土木工程智能化监测评估系统的理论研究及应用[J].工业建筑, 2021,51(2):102-106. [5] RAINIERI C,FABBROCINO G.Operational modal analysis of civil engineering structures-an introduction and guide for applications[M].New York: Springer-Verlag New York Inc,2014:53. [6] ZHOU K, ZHI L, LI Q, et al. Investigation of modal parameters of a 600-m-tall skyscraper based on two-year-long structural health monitoring data and five typhoons measurements[J]. Engineering Structures, 2023, 274(1):1-14. [7] 黄珍,单德山,李乔. 大跨斜拉桥运营模态分析方法对比[J]. 应用基础与工程科学学报,2019,27(1):144-155. [8] 贺文宇,丁绪聪,任伟新. 环境激励下移动车辆对桥梁模态参数识别的影响研究[J]. 振动与冲击,2021,40(3):48-53. [9] RAO R, LI C, HUANG Y,et al. Method for structural frequency extraction from GNSS displacement monitoring signals[J/OL].Journal of Testing and Evaluation, 2019, 47(3)[2018-10-24]. https://doi.org/10.1520/JTE20180087. [10] LI Y, ZHU L, QIAN C,et al.The time-varying modal information of a cable-stayed bridge: Some consideration for SHM[J/OL].Engineering Structures, 2021, 235[2021-05-15]. https://doi.org/10.1016/j.engstruct.2020.111835. [11] 胡强,程耀东.大地脉动数据的分析及建模[J].浙江大学学报(自然科学版), 1997, 31(6):767-774. [12] 亓兴军,杨晓天,王珊珊,等. 基于车辆响应的连续梁桥频率识别[J]. 工业建筑,2021,51(9):150-155. [13] 伊廷华,李宏男,王国新. 基于小波变换的高层建筑脉动风速模拟与实测研究[J]. 振动与冲击,2007,26(3):13-18. [14] 陈永高,钟振宇. 环境激励下桥梁结构模态参数识别的改进随机子空间算法[J]. 振动与冲击,2020,39(16):196-204. [15] 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(21):19-24,64. [16] 广州大学.利通广场健康监测综合分析报告Ⅱ-风环境和振动测试部分[R].广州:广东省利通置业投资有限公司,2011. [17] 广州大学,暨南大学.大跨屋盖结构风效应的原型实测、风洞试验及数值模拟研究[R].广州:广州凯得文化娱乐有限公司,2011. [18] 汪子豪. 温度与桥梁模态频率的相关模型研究[D]. 广州:广州大学,2018. [19] 广州市市政工程设计研究总院有限公司.广州大道(天河北路—洛溪大桥)快速化改造系统工程—洛溪大桥拓宽工程:洛溪大桥主桥静动载试验检测报告[R].广州:广州市中心区交通项目管理中心,2021.
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