An Improved Empirical Mode Decomposition Based on Local Integral Mean and Its Application in Signal Processing

Empirical mode decomposition (EMD) is an effective method to deal with nonlinear nonstationary data, but the lack of orthogonal decomposition theory and mode-mixing are the main problems that limit the application of EMD. In order to solve these two problems, we propose an improved method of EMD. Th...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-30
Main Authors: Niu, Xiao-dong, Lu, Li-rong, Wang, Jian, Han, Xing-cheng, Li, Xuan, Wang, Li-ming
Format: Article
Language:English
Subjects:
Decomposition
Integrals
Mathematical analysis
Methods
Monte Carlo simulation
Noise
Robustness (mathematics)
Sifting
Signal processing
Wavelet transforms
White noise
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Summary:Empirical mode decomposition (EMD) is an effective method to deal with nonlinear nonstationary data, but the lack of orthogonal decomposition theory and mode-mixing are the main problems that limit the application of EMD. In order to solve these two problems, we propose an improved method of EMD. The most important part of this improved method is to change the mean value by envelopes of signal in EMD to the mean value by the definite integral, which enables the mean value to be mathematically expressed strictly. Firstly, we prove that the signal is orthogonally decomposed by the improved method. Secondly, the Monte Carlo method of white noise is used to explain that the improved method can effectively alleviate mode-mixing. In addition, the improved method is adaptive and does not need any input parameters, and the intrinsic mode functions (IMFs) generated from it is robust to sifting. We have carried out experiments on a series of artificial and real data, the results show that the improved method is the orthogonal decomposition method and can effectively alleviate mode-mixing, and it has better decomposition performance and physical meaning than EMD, ensemble EMD (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In addition, the improved method is generally more time-consuming than EMD, but far less than EEMD and CEEMDAN.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/8891217