Abstract:

Empirical Mode Decomposition (EMD) is a totally datadriven and selfadaptive decomposition algorithm that is used to analyze nonlinear, nonstationary and timevarying signal, it breaks out the limitation of needing of presetting basis function for traditional data analysis method like Fourier transformation and wavelet decomposition, and it can decompose a signal into a few intrinsic mode function components with physical meaning and a residue from highfrequency to lowfrequency. Firstly, the principle and algorithm of the original EMD method are introduced. Secondly, we present an overview of the current development of EMD and analyze EMD's existing end effects, mode mixing, running speed problems and the problems that appeared when the original data are twodimensional and compared researchers' solutions to these problems. Finally, combined with its problems, several directions of further research and application are pointed out.

Key words: empirical mode decomposition(EMD);intrinsic mode function (IMF);HilbertHuang transform