基于VMD-KLD的桥梁挠度监测数据温度效应分离方法

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摘要传统经验模态分解（Empirical Mode Decomposition，EMD）方法在处理桥梁挠度信号时存在模态混叠、分解误差累积等问题，致使分解结果尚不理想。为此，本文提出了一种结合变分模态分解(Variational Mode Decomposition，VMD)和K-L散度（Kullback-Leibler divergence，KLD）的桥梁挠度监测数据温度效应分离方法。首先，利用VMD分解桥梁挠度信号，获得若干个本征模态函数（Intrinsic Mode Function，IMF）；其次，借助核密度估计求得各IMF分量的概率密度函数分布，进而得到各分量KLD值，剔除虚假IMF分量，选定最佳分量；然后，运用Pearson相关系数对最佳分量进行效果评价；最后，通过数值仿真算例与实桥监测数据，验证了本文所提方法的有效性。结果表明：1）本文所提方法融合了VMD自适应、抗噪能力强和KLD快速选取最优信号的优势，克服了传统EMD模态混叠等缺陷，减少了虚假分量的干扰，将二者结合使得分解及筛选特征信号分量高效可靠，温度效应分离效果良好；2）仿真信号经VMD-KLD分析得到日、年温差效应及长期挠度相关系数分别为0.9946、0.9837和0.9704，实测信号得到的日、年温差效应相关系数分别为0.9081、0.9364；3）同EMD-KLD相比，VMD-KLD分离出的各挠度成分相关系数更接近于1，仿真信号分析中日、年温差效应及长期挠度分别提升了4.43%、10.84%和8.81%，实测信号分析中日、年温差效应分别提升了12.35%、5.57%。本文所提方法可为桥梁挠度监测数据温度效应在线分离提供一种新的思路。

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	李双江1
	辛景舟1
	2
	付雷1
	3
	唐启智1
	赵月明4
	周建庭1

关键词 ：温度效应, 变分模态分解（VMD）, K-L散度（KLD）, 桥梁挠度分离, 健康监测

Abstract：The traditional Empirical Mode Decomposition (EMD) method has problems of modal confusion and accumulation of decomposition errors when dealing with bridge deflection signals, resulting in unsatisfactory decomposition results. For this reason, a bridge deflection monitoring data temperature effect separation method combining Variational Mode Decomposition (VMD) and K-L divergence (KLD) is proposed in this article. Firstly, the bridge deflection signal is decomposed by VMD to obtain several Intrinsic Mode Functions (IMFs); secondly, the probability density function distribution of each IMF component is obtained by kernel density estimation, and then the KLD value of each component is obtained, and the best component is selected by eliminating spurious IMF components; then, the Pearson correlation coefficient is used to evaluate the effect of the best component; finally, the effectiveness of the proposed method is verified by numerical simulation cases and real bridge monitoring data. The results show that: 1) the proposed method combines the advantages of VMD adaptive and strong noise immunity and KLD fast selection of optimal signals, overcomes the defects of traditional EMD modal blending and reduces the interference of spurious components, and the combination of the two makes the decomposition and screening of characteristic signal components efficient and reliable, with good separation of temperature effects. 2) The correlation coefficients of daily and annual temperature difference effects and long-term deflection obtained from the simulated signal by VMD-KLD analysis are 0.9946, 0.9837 and 0.9704, respectively, and the correlation coefficients of daily and annual temperature difference effects obtained from the measured signal are 0.9081 and 0.9364, respectively. 3) Compared with EMD-KLD, the correlation coefficients of each deflection component separated by VMD-KLD are closer to 1. The daily and annual temperature difference effects and long-term deflection are improved by 4.43%, 10.84% and 8.81%, respectively, in the simulated signal analysis, and the daily and annual temperature difference effects are improved by 12.35% and 5.57%, respectively, in the measured signal analysis. The method proposed in this article can provide a new idea for online separation of temperature effects of bridge deflection monitoring data.

Key words： temperature effect variational modal decomposition（VMD） K-L divergence（KLD） bridge deflection separation health monitoring

收稿日期: 2020-12-24 出版日期: 2022-03-15

引用本文:

李双江1，辛景舟1,2，付雷1,3，唐启智1，赵月明4，周建庭1. 基于VMD-KLD的桥梁挠度监测数据温度效应分离方法[J]. 振动与冲击, 2022, 41(5): 105-113.
LI Shuangjiang1, XIN Jingzhou1,2, FU Lei1,3, TANG Qizhi1, ZHAO Yueming4, ZHOU Jianting1. Temperature effect separation method of bridge deflection monitoring data based on VMD-KLD. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(5): 105-113.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I5/105

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