Application of the EEMD and CEEMDAN algorithm for non- linear signal processing

claude mukaz; Moise NGOYI

Authors

claude mukaz ok https://orcid.org/0009-0007-8402-880X
Moise NGOYI KASANJI High Institute of Applied Techniques https://orcid.org/0009-0007-8402-880X

Keywords:

EEMD, CEEMDAN, Non-linear, Algorithm

Abstract

Nonlinear signals are often encountered in many applications, such as biomedical signal processing, fault diagnosis, and image processing. Ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithms have been proposed for the analysis of nonlinear and non-stationary signals. In this paper, we compare the performance of EEMD and CEEMDAN algorithms based on the Root Mean Square (RMS) statistical indicator for nonlinear signal processing. We evaluate the effectiveness of these algorithms using two synthetic signals and a real-world vibration signal from a gearbox. The results show that CEEMDAN provides a 50% improvement over EEMD in terms of RMS and the number of trials or computation time required. The study also shows that EEMD is prone to mode mixing and requires a large number of trials to achieve accurate results. On the other hand, CEEMDAN overcomes the mode mixing issue and provides more accurate results with fewer trials or computation time. Our findings suggest that CEEMDAN is a more efficient algorithm for nonlinear signal processing, particularly in real-world applications where computation time is a limiting factor.

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Author Biography

Moise NGOYI KASANJI, High Institute of Applied Techniques

ok

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