基于改进EMD-ICA的结构模态参数识别研究

该文针对频带滤波改进经典经验模态分解(EmpiricalModeDecomposition，EMD)的模态分解能力不足时产生过多虚假模态的问题以及真正本征模函数(IntrinsicModeFunction，IMF)的判定问题，提出了将改进EMD与独立分量相结合的信号分析方法。该方法不需要人为预先设定阈值，能够自动分离出真正的IMF分量，消除改进EMD过程中产生的虚假模态，保障EMD分解信号的有效性。然后利用随机减量技术获得各IMFs的自由模态，最后利希尔伯特变换和最小二乘拟合技术相结合的方法来识别出结构的频率和阻尼比，并通过两个数值算例和一个七层钢框架的模态试验予以验证。研究结果表明：该方法可有效解决改进EMD的缺陷，并成功识别出结构的模态参数。

STUDY ON THE MODAL PARAMETER IDENTIFICATION BASED ON IMPROVED EMD AND INDEPENDENT COMPONENT ANALYSIS

Considering the problem of a false modal and the decision of a real IMF in an improved Empirical Mode Decomposition (EMD) by using frequency-band filters, a signal analysis method based on the combination of improved EMD and Independent Component Analysis (ICA) was proposed. This proposed method does not require setting a threshold in advance, and the real IMF component can be isolated automatically. This method also can eliminated the false modal components, which appeared in the process of an improved EMD, thusly it can make sure the validity of an EMD very well. Afterwards, the random decrement technique (RDT) is used to obtain free vibration modes of each IMF. Finally, Hilbert Transform (HT) and the least square fitting are employed to identify structural modal parameters from these free vibration modes. Finally, the presented method is used to identify modal parameters of two numerical examples and a seven-story steel frame. The results show that the proposed method can resolve the drawbacks of an improved EMD more effectively, and the modal parameters can be identified successfully.