Self-tuning variational mode decomposition

•A self-tuning variational mode decomposition (SVMD) is proposed.•SVMD can adaptively update the parameters K and α.•Several properties of SVMD are studied and compared with those of other methods.•Comparative studies of real-world applications show the advantages of SVMD. Variational mode decomposi...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2021-10, Vol.358 (15), p.7825-7862
Main Authors: Chen, Qiming, Chen, Junghui, Lang, Xun, Xie, Lei, Rehman, Naveed ur, Su, Hongye
Format: Article
Language:English
Subjects:
Adaptability
Algorithms
Climate change
Decomposition
Filter banks
Geophysics
Matched pursuit
Measurement
Meteorology
Noise
Noise levels
Orthogonality
Parameters
Self tuning
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Summary:•A self-tuning variational mode decomposition (SVMD) is proposed.•SVMD can adaptively update the parameters K and α.•Several properties of SVMD are studied and compared with those of other methods.•Comparative studies of real-world applications show the advantages of SVMD. Variational mode decomposition (VMD) has attracted a lot of attention recently owing to its robustness to sampling frequency and its high-frequency resolution. However, its performance highly depends on two key preset parameters (the mode number K and the penalty parameter α), both of which tightly limit its adaptability and applications. In this study, a self-tuning VMD (SVMD) is proposed to tackle this problem. Within the proposed method, K and α update themselves respectively and adaptively via the energy ratio and orthogonality between modes in the frequency domain. The proposed SVMD is similar to a matching pursuit method and it shows a VMD-like equivalent filter bank structure but with much less mode-mixing probability. We show that SVMD is more robust to both changes in α and noise level than the original VMD; also, it has better convergence and reduces mode-mixing and end-effect. The experiments on SVMD indicate that SVMD outmatches several classic signal decomposition algorithms. In the end, real-world applications in three fields, namely, length of day variation analysis in geophysics, climate cycle study in meteorology, and oscillation detection in process control, are provided to demonstrate the effectiveness and advantages of the proposed SVMD.
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2021.07.021