Self-tuning variational mode decomposition

Qiming Chen; Junghui Chen; Xun Lang; Lei Xie; Naveed ur Rehman; Hongye Su

doi:10.1016/j.jfranklin.2021.07.021

Self-tuning variational mode decomposition

Qiming Chen, Junghui Chen^*, Xun Lang, Lei Xie^*, Naveed ur Rehman, Hongye Su

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Abstract

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.

Original language	English
Journal	Journal of the Franklin Institute
Volume	358
Issue	15
Pages (from-to)	7825-7862
Number of pages	38
ISSN	0016-0032
DOIs	https://doi.org/10.1016/j.jfranklin.2021.07.021
Publication status	Published - Oct 2021

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10.1016/j.jfranklin.2021.07.021

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title = "Self-tuning variational mode decomposition",

abstract = "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.",

author = "Qiming Chen and Junghui Chen and Xun Lang and Lei Xie and Rehman, {Naveed ur} and Hongye Su",

note = "Publisher Copyright: {\textcopyright} 2021",

year = "2021",

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doi = "10.1016/j.jfranklin.2021.07.021",

language = "English",

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AU - Chen, Qiming

AU - Chen, Junghui

AU - Lang, Xun

AU - Xie, Lei

AU - Rehman, Naveed ur

AU - Su, Hongye

PY - 2021/10

Y1 - 2021/10

N2 - 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.

AB - 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.

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DO - 10.1016/j.jfranklin.2021.07.021

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VL - 358

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EP - 7862

JO - Journal of the Franklin Institute

JF - Journal of the Franklin Institute

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ER -

Self-tuning variational mode decomposition

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