Extraction of Significant Features using Empirical Mode Decomposition and Its Application

Abstract

 Abstract—Empirical Mode Decomposition (EMD) is recently used in a broad range of applications for extracting signals from data generated in noisy nonlinear and nonstationary processes. However, it has a major drawback, mode mixing, which is defined as a single Intrinsic Mode Function (IMF) consisting of signals of widely disparate scales. This often makes the physical meaning of individual IMF unclear. To solve this problem, a novel algorithm to select significant IMFs is applied to fault signal of the induction motor and musical sound of the percussion instrument.