Paper
25 May 2023 Comparison of the effects of multiple empirical mode decomposition on vibration signals
Zhen-gang Shi, Yi-fan Song
Author Affiliations +
Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126362B (2023) https://doi.org/10.1117/12.2675284
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China
Abstract
The signal collected by vibration sensor is nonlinear and unstable, which is difficult to analyze in time domain or frequency domain. Therefore, the original signal is decomposed into several intrinsic mode functions (IMF) through empirical mode decomposition (EMD), and then further processing such as feature extraction is carried out. However, modal aliasing and endpoint effects exist in EMD. Therefore, complementary ensemble empirical mode decomposition (CEEMD) is adopted in this paper. CEEMD adds multiple groups of white noises with opposite signs before empirical mode decomposition, which not only reduces the problem of mode aliasing, but also improves the analysis results. The complementary ensemble empirical mode decomposition effectively deals with the collected nonlinear and unstable vibration signals.
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Zhen-gang Shi and Yi-fan Song "Comparison of the effects of multiple empirical mode decomposition on vibration signals", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126362B (25 May 2023); https://doi.org/10.1117/12.2675284
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KEYWORDS
Modal decomposition

Vibration

Signal processing

Aliasing

Interference (communication)

Denoising

Nonlinear optics

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