This paper presents an adaptive noise reduction method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) to address the non-stationary characteristics and noise interference present in friction vibration signals from mechanical equipment. and friction testing machine simulation experiments. The performance of CEEMD and Ensemble Empirical Mode Decomposition (EEMD) was compared through MATLAB R2023b simulations and experiments conducted on a friction testing machine. CEEMD achieved a computational efficiency 85.6% higher than that of EEMD and effectively reduced mode aliasing. Among them, the adaptive correlation coefficient screening method performed well in signal reconstruction, and the high correlation (correlation coefficient > 0.8) between the denoised signal and the laboratory noise signal was verified using the multi-scale permutation entropy (MPE) theory, which is of great significance for early diagnosis of mechanical faults, prediction of equipment life and timely maintenance decisions.
Yu et al. (Wed,) studied this question.