Self-tuning Multivariate Variational Mode Decomposition

LANG Xun; WANG Jiayi; CHEN Qiming; HE Bingbing; MAO Rukai; XIE Lei

doi:10.11999/JEIT230763

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 >

LANG Xun, WANG Jiayi, CHEN Qiming, HE Bingbing, MAO Rukai, XIE Lei. Self-tuning Multivariate Variational Mode Decomposition[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230763

Citation:

LANG Xun, WANG Jiayi, CHEN Qiming, HE Bingbing, MAO Rukai, XIE Lei. Self-tuning Multivariate Variational Mode Decomposition[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230763

Citation:

PDF( 5682 KB)

Self-tuning Multivariate Variational Mode Decomposition

doi: 10.11999/JEIT230763

1.
School of Information Science and Engineering, Yunnan University, Kunming 650504, China
2.
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
3.
Yunnan Yuntianhua Limited by Share Ltd, Kunming 650000, China

Funds: The National Natural Science Foundation of China (62003298, 62201495), Yunnan Fundamental Research Projects (202301AT070277), The Major Project of Science and Technology of Yunnan Province (202202AD080005, 202202AH080009)

Received Date: 2023-07-25
Rev Recd Date: 2024-04-27

Available Online: 2024-05-15

Abstract

Abstract

The Multivariate Variational Mode Decomposition (MVMD), being an extension of the Variational Mode Decomposition (VMD), inherits the merits of VMD. However, it encounters an issue wherein its decomposition performance relies heavily on two predefined parameters, the number of modes (K) and the penalty factor ($ \alpha $). To address this issue, a Self-tuning MVMD (SMVMD) algorithm is proposed. SMVMD employs the notion of matching pursuit to adaptively update K and $ \alpha $ based on energy occupation and mode orthogonality in the frequency domain, respectively. The experimented results of both simulated signals and real cases demonstrate that the proposed SMVMD not only effectively addresses the parameter rectification problem of the original MVMD, but also exhibits the following advantages: (i) SMVMD displays superior resilience to mode-mixing compared to MVMD, along with enhanced robustness to both noise and variations in $ \alpha $-value. (ii) In comparison to the classical algorithms of multivariate empirical mode decomposition, fast multivariate empirical mode decomposition, and multivariate variational mode decomposition, SMVMD showcases the lowest decomposition error and the best decomposition effect.
- Multivariate signal processing,
- Multivariate Variational Mode Decomposition (MVMD),
- Self-tuning,
- Matching pursuit method,
- Robustness

FullText(HTML)

References(28)

References

[1]	CHEN Qiming, LANG Xun, XIE Lei, et al. Multivariate intrinsic chirp mode decomposition[J]. Signal Processing, 2021, 183: 108009. doi: 10.1016/j.sigpro.2021.108009.
[2]	ZAHRA A, KANWAL N, REHMAN N U, et al. Seizure detection from EEG signals using multivariate empirical mode decomposition[J]. Computers in Biology and Medicine, 2017, 88: 132–141. doi: 10.1016/j.compbiomed.2017.07.010.
[3]	HAN G, LIN B, and XU Z. Electrocardiogram signal denoising based on empirical mode decomposition technique: An overview[J]. Journal of Instrumentation, 2017, 12(3): P03010. doi: 10.1088/1748-0221/12/03/P03010.
[4]	王宇红, 高志兴. 改进多维本质时间尺度分解的厂级振荡检测[J]. 控制工程, 2022, 29(10): 1835–1840. doi: 10.14107/j.cnki.kzgc.CAC2020-1557. WANG Yuhong and GAO Zhixing. Plant-wide oscillation detection based on improved multivariate intrinsic time-scale decomposition[J]. Control Engineering of China, 2022, 29(10): 1835–1840. doi: 10.14107/j.cnki.kzgc.CAC2020-1557.
[5]	REHMAN N U and AFTAB H. Multivariate variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2019, 67(23): 6039–6052. doi: 10.1109/TSP.2019.2951223.
[6]	TANAKA T and MANDIC D P. Complex empirical mode decomposition[J]. IEEE Signal Processing Letters, 2007, 14(2): 101–104. doi: 10.1109/LSP.2006.882107.
[7]	RILLING G, FLANDRIN P, GONCALVES P, et al. Bivariate empirical mode decomposition[J]. IEEE Signal Processing Letters, 2007, 14(12): 936–939. doi: 10.1109/LSP.2007.904710.
[8]	REHMAN N U and MANDIC D P. Empirical mode decomposition for trivariate signals[J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1059–1068. doi: 10.1109/TSP.2009.2033730.
[9]	REHMAN N and MANDIC D P. Multivariate empirical mode decomposition[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2010, 466(2117): 1291–1302. doi: 10.1098/rspa.2009.0502.
[10]	ASGHAR M A, KHAN M J, RIZWAN M, et al. AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification[J]. Multimedia Systems, 2022, 28(4): 1275–1288. doi: 10.1007/s00530-021-00782-w.
[11]	LANG Xun, ZHANG Yufeng, XIE Lei, et al. Detrending and denoising of industrial oscillation data[J]. IEEE Transactions on Industrial Informatics, 2023, 19(4): 5809–5820. doi: 10.1109/TII.2022.3188844.
[12]	HUANG Guoqing, PENG Liuliu, KAREEM A, et al. Data-driven simulation of multivariate nonstationary winds: A hybrid multivariate empirical mode decomposition and spectral representation method[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2020, 197: 104073. doi: 10.1016/j.jweia.2019.104073.
[13]	LANG Xun, ZHENG Qian, ZHANG Zhiming, et al. Fast multivariate empirical mode decomposition[J]. IEEE Access, 2018, 6: 65521–65538. doi: 10.1109/ACCESS.2018.2877150.
[14]	蔡念, 黄威威, 谢伟, 等. 基于互补自适应噪声的集合经验模式分解算法[J]. 电子与信息学报, 2015, 37(10): 2383–2389. doi: 10.11999/JEIT141632. CAI Nian, HUANG Weiwei, XIE Wei, et al. Ensemble empirical mode decomposition base on complementary adaptive noises[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2383–2389. doi: 10.11999/JEIT141632.
[15]	WANG Yanxue, LIU Fuyun, JIANG Zhansi, et al. Complex variational mode decomposition for signal processing applications[J]. Mechanical Systems and Signal Processing, 2017, 86: 75–85. doi: 10.1016/j.ymssp.2016.09.032.
[16]	ZOSSO D, DRAGOMIRETSKIY K, BERTOZZI A L, et al. Two-dimensional compact variational mode decomposition[J]. Journal of Mathematical Imaging and Vision, 2017, 58(2): 294–320. doi: 10.1007/s10851-017-0710-z.
[17]	孟明, 闫冉, 高云园, 等. 基于多元变分模态分解的脑电多域特征提取方法[J]. 传感技术学报, 2020, 33(6): 853–860. doi: 10.3969/j.issn.1004-1699.2020.06.011. MENG Ming, YAN Ran, GAO Yunyuan, et al. Multi-domain feature extraction of EEG based on multivariate variational mode decomposition[J]. Chinese Journal of Sensors and Actuators, 2020, 33(6): 853–860. doi: 10.3969/j.issn.1004-1699.2020.06.011.
[18]	YAN Xiaoan, LIU Ying, XU Yadong, et al. Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity[J]. Renewable Energy, 2021, 170: 724–748. doi: 10.1016/j.renene.2021.02.011.
[19]	ZHANG Yijie, ZHANG Haoran, YANG Yang, et al. Seismic random noise separation and attenuation based on MVMD and MSSA[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5908916. doi: 10.1109/TGRS.2021.3131655.
[20]	CHEN Qiming, CHEN Junghui, LANG Xun, et al. Self-tuning variational mode decomposition[J]. Journal of the Franklin Institute, 2021, 358(15): 7825–7862. doi: 10.1016/j.jfranklin.2021.07.021.
[21]	CHEN Shiqian, YANG Yang, PENG Zhike, et al. Adaptive chirp mode pursuit: Algorithm and applications[J]. Mechanical Systems and Signal Processing, 2019, 116: 566–584. doi: 10.1016/j.ymssp.2018.06.052.
[22]	REHMAN N U and MANDIC D P. Filter bank property of multivariate empirical mode decomposition[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2421–2426. doi: 10.1109/TSP.2011.2106779.
[23]	LANG Xun, ZHANG Yufeng, XIE Lei, et al. Use of fast multivariate empirical mode decomposition for oscillation monitoring in noisy process plant[J]. Industrial & Engineering Chemistry Research, 2020, 59(25): 11537–11551. doi: 10.1021/acs.iecr.9b06351.
[24]	CHEN Qiming, WEN Qingsong, WU Xialai, et al. Detection and time–frequency analysis of multiple plant-wide oscillations using adaptive multivariate intrinsic chirp component decomposition[J]. Control Engineering Practice, 2023, 141: 105715. doi: 10.1016/j.conengprac.2023.105715.
[25]	YU Wanke, ZHAO Chunhui, and HUANG Biao. MoniNet with concurrent analytics of temporal and spatial information for fault detection in industrial processes[J]. IEEE Transactions on Cybernetics, 2022, 52(8): 8340–8351. doi: 10.1109/TCYB.2021.3050398.
[26]	LINDNER B, AURET L, and BAUER M. A systematic workflow for oscillation diagnosis using transfer entropy[J]. IEEE Transactions on Control Systems Technology, 2020, 28(3): 908–919. doi: 10.1109/TCST.2019.2896223.
[27]	CHEN Qiming, LANG Xun, LU Shan, et al. Detection and root cause analysis of multiple plant-wide oscillations using multivariate nonlinear chirp mode decomposition and multivariate granger causality[J]. Computers & Chemical Engineering, 2021, 147: 107231. doi: 10.1016/j.compchemeng. 2021.107231.
[28]	AFTAB M F, HOVD M, and SIVALINGAM S. Detecting non-linearity induced oscillations via the dyadic filter bank property of multivariate empirical mode decomposition[J]. Journal of Process Control, 2017, 60: 68–81. doi: 10.1016/j.jprocont.2017.08.005.