关键词: 经验模态分解(EMD),    ,  盲源分离(BSS),    ,  模态混叠,    , 希尔伯特变换(HT)

Abstract: The Empirical mode decomposition (EMD) has significant advantages in nonlinear and nonsteady response signal processing algorithms, but in the actual application process, the EMD existed some defects, in which the modal aliasing and false mode phenomenon is most prominent, the modal aliasing phenomenon can be simple in the 1 intrinsic mode function (IMF) containing more than one order structure of intrinsic mode components. The false mode phenomenon is the frequency component that should not be existed, which has a serious influence on the identification accuracy of structural modal parameters. To solve this problem, this paper studies the above two defects in EMD, and proposes an improved EMD algorithm which combines the fast ICA algorithm (FastICA) in frequency band filtering and independent component analysis (ICA) algorithm. The structure frequency is identified by hilbert transform (HT), and the effectiveness of the proposed algorithm is verified by the the Benchmark structure.

Key words: Empirical mode decomposition (EMD),    ,  Blind source separation (BSS),    , Mode mixing,    ,  Hilbert transform