辽宁石油化工大学学报

辽宁石油化工大学学报 ›› 2017, Vol. 37 ›› Issue (5): 67-70.DOI: 10.3969/j.issn.1672-6952.2017.05.013

• 计算机与控制 • 上一篇    下一篇

基于FastICA改进EMD的算法研究

付 春1,孙祥磊1,王 昆1,管海伟1,刘 娟1,李 明2   

  1. (1.辽宁石油化工大学石油天然气工程学院,辽宁抚顺113001;2.沈阳建筑大学土木工程学院,辽宁沈阳110168)
  • 收稿日期:2017-03-19 修回日期:2017-04-06 出版日期:2017-10-31 发布日期:2017-11-09
  • 作者简介:付春(1980-),女,博士,讲师,从事结构健康监测与损伤识别研究;E-mail:fuchun@lnpu.edu.cn。
  • 基金资助:
    住建部科学技术计划项目(2016-K5-009)。

Research on Improved EMD Algorithm Based on FastICA

  1. (1.College of Petroleum Engineering, Liaoning Shihua University, Fushun Liaoning 113001,China; 2.College of Civil Engineering, Shenyang Jianzhu University, Shenyang Liaoning 110168,China)
  • Received:2017-03-19 Revised:2017-04-06 Published:2017-10-31 Online:2017-11-09

摘要: 经验模态分解(EMD)算法在非线性、非稳态的信号处理上具有显著的优势,但EMD在实际应用过程中存在着一些缺陷,其中以模态混叠和虚假模态现象最为突出。模态混叠现象可以简单地概述为在1个本征模函数 (IMF)含有多于一阶的结构固有模态分量;虚假模态现象则是指不该有的频率组分对结构模态参数识别精度的严重影响。针对这一问题,对EMD中存在的以上两大缺陷展开研究,提出了利用频带滤波和独立分量分析算法(ICA)中的快速ICA算法(FastICA)相结合改进的EMD 算法。利用希尔伯特变换(HT)识别结构频率,并通过Benchmark 结构验证了所提算法的有效性。

关键词: 经验模态分解(EMD),    ,  盲源分离(BSS),    ,  模态混叠,    , 希尔伯特变换(HT)

Abstract: The Empirical mode decomposition (EMD) has significant advantages in nonlinear and nonsteady response signal processing algorithms, but in the actual application process, the EMD existed some defects, in which the modal aliasing and false mode phenomenon is most prominent, the modal aliasing phenomenon can be simple in the 1 intrinsic mode function (IMF) containing more than one order structure of intrinsic mode components. The false mode phenomenon is the frequency component that should not be existed, which has a serious influence on the identification accuracy of structural modal parameters. To solve this problem, this paper studies the above two defects in EMD, and proposes an improved EMD algorithm which combines the fast ICA algorithm (FastICA) in frequency band filtering and independent component analysis (ICA) algorithm. The structure frequency is identified by hilbert transform (HT), and the effectiveness of the proposed algorithm is verified by the the Benchmark structure.

Key words: Empirical mode decomposition (EMD),    ,  Blind source separation (BSS),    , Mode mixing,    ,  Hilbert transform

引用本文

付 春,孙祥磊,王 昆,等. 基于FastICA改进EMD的算法研究[J]. 辽宁石油化工大学学报, 2017, 37(5): 67-70.

Fu Chun,Sun Xianglei,Wang Kun,et al. Research on Improved EMD Algorithm Based on FastICA[J]. Journal of Liaoning Petrochemical University, 2017, 37(5): 67-70.

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链接本文: http://journal.lnpu.edu.cn/CN/10.3969/j.issn.1672-6952.2017.05.013

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