基于EEMD-JADE的桥梁挠度监测中温度效应的分离
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U441

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国家自然科学基金(51408452);道路桥梁与结构工程湖北省重点实验室(武汉理工大学)开放基金(DQJJ201906)


Separation of bridge deflection temperature effect based on EEMD-JADE
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    摘要:

    针对桥梁挠度各成分的分离问题,提出一种基于EEMD-JADE的单通道盲源分离算法。首先,利用传统的集合经验模态分解法(Ensemble Empirical Mode Decomposition,EEMD)将单通道的桥梁挠度信号分解为一系列线性平稳的本征模函数(Intrinsic Mode Function,IMF);然后,采用基于能量熵增量的判别法识别并剔除虚假的IMF分量,将能量熵增量较大的IMF分量组成盲源分离模型的输入信号;最后,采用矩阵联合近似对角化(Joint Approximate Diagonalization of Eigen-matrices,JADE)算法对输入信号进行盲源分离。JADE算法在源信号频率差异较小且频率有所混叠的状况下也能较好地分离出源信号,但要求观测信号数必须大于等于源信号数目;EEMD具有良好的自适应性,能够将单通道的混合信号进行多尺度分解,形成多通道信号,但分解结果存在端点效应与模态混叠。JADE算法能够解决EEMD分解结果存在的端点效应与模态混叠问题,且EEMD也解决了JADE分离算法的先决条件。两种算法优势互补,能够较好地分离出各挠度组分。通过有限元软件Midas/civil建立了背景桥梁模型,经仿真分析得到了各单项因素作用下的桥梁结构响应,并将其叠加在一起作为待分离的混合挠度信号。仿真信号分离的结果与源信号的相关系数均在0.98以上,说明分离效果较好。最后,采集实测挠度信号进行分离,处于对称位置测点分离出的各挠度组分的相关系数均在0.9以上,证明了该算法的适用性。

    Abstract:

    For the seperation problem of bridge deflection monitoring, it presents a single channel blind source separation algorithm based on EEMD-JADE. First, the single channel signal of bridge deflection is decomposed into a series of linear and stationary intrinsic mode function (intrinsic mode function, IMF) by traditional ensemble empirical mode decomposition (EEMD), and then using the discriminant method based on the energy entropy increment to identify and eliminate the false IMF component. The IMF component with larger energy entropy increment compose the input signal of the blind source separation model. Finally, the Joint Approximate Diagonalization of Eigen-matrices (JADE) algorithm is used for blind source separation of the input signal. JADE can also separate the source signal well under the condition that the frequency difference of the source signal is small and the frequency is mixed, but the number of observed signals must be greater than or equal to the number of source signals. The Ensemble Empirical Mode Decomposition (EEMD) has good adaptability, which can decompose the mixed signals of single channel into multi-scale and form multi-channel signal, but the decomposition result has the endpoint effect and the modal aliasing. JADE algorithm can solve the end-point effect and modal aliasing problem in the decomposition result of EEMD, while EEMD also solves the prerequisite of JADE separation algorithm. The two algorithms have complementary advantages and can better separate the deflection components. With the model of background bridge established by the finite element software Midas/civil, the response of the bridge structure under the action of each single factor is obtained by the simulation analysis, and it is superimposed together as a mixed deflection signal to be separated. The correlation coefficient between the result of the simulation signal separation and the source signal is above 0.98, and the separation effect is better. Finally, the measured deflection signals are collected for separation. The correlation coefficients of the deflection components separated at the symmetric position are above 0.9, which proves the applicability of the algorithm.

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谭冬梅,刘晓飞,姚欢,聂顺,吴浩.基于EEMD-JADE的桥梁挠度监测中温度效应的分离[J].土木与环境工程学报(中英文),2020,42(3):90-99.

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  • 收稿日期:2019-09-24
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  • 在线发布日期: 2020-06-13
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