Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition

Abstract

The empirical mode decomposition (EMD) is an algorithm pioneered by N. Huang et. al. as an alternative technique to the traditional Fourier and wavelet methods for analyzing nonlinear and non-stationary signals. It aims at decomposing a signal, via an iterative sifting procedure, into several intrinsic mode functions (IMFs), and each of the IMFs has better behaved instantaneous frequency analysis. This paper presents an alternative approach for EMD. The main idea is to replace the average of upper and lower envelopes in the sifting procedure of EMD by a local average obtained by variational optimization framework. Therefore, an IMF can be produced by simply subtracting the average from the signal without iteration. Our numerical examples illustrate that the resulting decomposition is convergent and robust against noise.

An Improved Empirical Mode Decomposition Method for Vibration Signal

“…Screening Process. The basic foundation of EMD is that the signal is composed of high-frequency components and low-frequency components, so it can be generalized [18,19]. This algorithm is a process of continuously separating the high-frequency components of the signal and using the remaining low-frequency components as a new signal [20].…”

Spectrum Reconstruction of a Spatially Modulated Fourier Transform Spectrometer Based on Stepped Mirrors

“…The EMD was first proposed by Huang et al at NASA 26 and was widely used in many fields. [28][29][30][31] It is a data-driven method that could be used to deal with non-stationary and non-linear signals because its advantages of adaptively decomposing a signal to different frequencies and high time-frequency resolution.…”