Ensemble Empirical Mode Decomposition with Supervised Cluster Analysis

Ensemble empirical mode decomposition (EEMD) is a noise-assisted data analysis method which decomposes a signal into a collection of intrinsic mode functions (IMFs). There nevertheless appears a multi-mode problem where signals with a similar timescale are decomposed into different IMF components. A possible solution to this problem is to recombine the multi-mode IMF components into a proper single mode but as of yet, no general rules have been proposed in the literature. This paper presents the incorporation of a statistical cluster analysis to assist in the diagnosis of multi-mode IMFs and to recombine them based on the classified clusters. As a result, signals are reorganized into a condensed set of clustered intrinsic mode functions (CIMFs). The method is applied to two sets of artificially synthesized signals and two sets of practical signals: wind turbine noise and earthquake motion. These applications demonstrate that, with the additional cluster analysis, the multi-mode problem can be largely eliminated in a statistically reliable manner, and in situ applications can be improved.