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
