Empirical mode decomposition (EMD) is a self-adaptive method and suitable to analysing the non-stationary and nonlinear signals. Noise-assisted versions have been proposed to alleviate the so-called “mode mixing” phenomenon,which may appear when an EMD algorithm is used to deal with a signal with intermittency. Among them,the complementary ensemble EMD (CEEMD) and complete ensemble EMD with adaptive noise (CEEMDAN) recover the completeness property of EMD. In this work a new algorithm named complete complementary wavelet ensemble empirical mode decomposition with adaptive noise(CCWEEMDAN) was presented based on those existing techniques,obtaining better spectral separation of the modes with fewer sifting iterations and less noise of components with small ensemble number and extremely low computational cost.
Key words
empirical mode dercompostion /
ensemble empirical mode decomposition /
noise-assisted data analysis /
mode mixing /
complementary ensemble empirical mode decomposition
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References
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Footnotes
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