吉林大学学报(信息科学版) ›› 2021, Vol. 39 ›› Issue (3): 252-259.

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VMD-SCT-GMF 滤波算法

王冬梅a, 何 彬a, 路敬祎a,b , 肖建利a   

  1. 东北石油大学 a. 电气信息工程学院; b. 黑龙江省网络化与智能控制重点实验室, 黑龙江 大庆 163318
  • 收稿日期:2020-10-16 出版日期:2021-05-24 发布日期:2021-05-24
  • 作者简介:王冬梅(1977— ),女,黑龙江大庆人,东北石油大学副教授,硕士生导师,主要从事数字信号处理等研究,(Tel)86-18745977161(E-mail)wdmlju@126.com
  • 基金资助:
    国家自然科学基金资助项目(61873058); 中国石油科技创新基金资助项目(2018D-5007-0302); 东北石油大学青年科学基 金资助项目(2018QNL-33); 冶金装备及其控制教育部重点实验室开放基金资助项目(MECOF2019B01)

VMD-SCT-GMF Filtering Algorithm

WANG Dongmeia, HE Bina, LU Jingyia,b , XIAO Jianlia   

  1. a. School of Electrical Engineering and Information; b. Heilongjiang Provincial Key Laboratoryof Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China
  • Received:2020-10-16 Online:2021-05-24 Published:2021-05-24

摘要: 针对天然气管道发生泄漏时信号受到强烈的噪声干扰难以准确提取有用信号的问题, 提出一种变分模态分解(VMD: Variational Mode Decomposition)结合变点理论(SCT: Statistical Change-point Theory)和广义形态滤波 (GMF: Generalized Morphological Filtering)的有效信号去噪方法(VMD-SCT-GMF)。 该方法首先利用 VMD 对信号进行分解得到若干个模态分量, 然后计算各模态分量的自相关函数绝对值均值并结合变点理论区分出噪声模态和有效模态, 重构有效模态分量作为去噪后信号, 最后通过广义形态滤波器进一步滤波得到去噪后的信号。 实验结果表明, 所提出的方法与基于 VMD 结合豪斯多夫距离去噪方法、VMD 结合互相关系数和小波的去噪方法、基于互信息的 VMD 去噪方法相比, 去噪效果更佳。

关键词: 变分模态分解 , 变点理论 , 广义形态滤波 , 降噪

Abstract: Aiming at the characteristic that it is difficult to accurately extract useful signals when the signal is interfered by strong noise while there is a leaking at the natural gas pipeline, an effective signal denoising method combining variational modal decomposition and generalized morphological filtering is proposed. Firstly by using VMD (Variational Mode Decomposition) several modal component is decomposed. Then the mean absolute value for autocorrelations function of the model component is calculated. Using SCT (Statistical Change-point) modal and effective modal noise are distinguished. And reconstruction after effective modal component as the denoising signal. Finally, the GMF (Generalized Morphological Filtering) is used to give further filtering to the denoising signal. The experimental results show that compared with the method based on Hausdorff distance VMD, VMD combined with correlation number and wavelet, and VMD based on mutual information, the proposed method has better denoising effect.

Key words:  , variational mode decomposition (VMD), statistical change-point theory, generalized morphological filtering, de-noising

中图分类号: 

  • TN911. 72