期刊文献+

基于CEEMDAN的最优平滑降噪算法 被引量:2

OPTIMAL SMOOTHING NOISE REDUCTION ALGORITHM BASED ON CEEMDAN
下载PDF
导出
摘要 经验模态分解类算法处理非线性、非平稳信号具有良好的自适应分解能力,可以将复杂信号分解成按照频率由高到低顺序排列的固有模态函数形式,提取分解后的模态函数构造滤波器可以实现对原始信号的降噪处理。针对构造滤波器时对模态函数缺乏最优的筛选指标,从而影响到降噪的准确性与降噪效果,提出一种基于CEEMDAN的最优平滑降噪算法。通过参数调节方式对模态进行筛选,从而设计出性能最优的滤波器实现对信号的降噪处理。通过模拟实验与实际实验,验证了该算法对于转动机械噪声信号具有良好的降噪效果。 The empirical mode decomposition algorithm deals with nonlinear and non-stationary signals with good adaptive decomposition ability.It can decompose the complex signal into the form of the intrinsic mode function arranged in order of frequency from high to low.Extracting the decomposed modal function constructing filter can realize the noise reduction processing of the original signal.Aiming at the lack of optimal screening index for modal function when constructing the filter,which affects the accuracy and noise reduction effect of noise reduction,an optimal smoothing noise reduction algorithm based on CEEMDAN is proposed.The modal was screened by adjusting the parameters to design a filter with the best performance to achieve noise reduction of the signal.The simulation experiment and actual experiment of the proposed noise reduction algorithm were carried out,and it is verified that the proposed optimal noise reduction algorithm has good noise reduction effect on the rotating mechanical noise signal.
作者 张荣彬 Zhang Rongbin(Dezhou Degong Machinery Co.,Ltd.,Dezhou 253000,Shandong,China)
出处 《计算机应用与软件》 北大核心 2021年第6期294-298,317,共6页 Computer Applications and Software
关键词 经验模态分解算法 CEEMDAN 最优平滑降噪 转动机械噪声信号 Empirical mode decomposition algorithm CEEMDAN Optimal smoothing noise reduction Rotating mechanical noise signal
  • 相关文献

参考文献4

二级参考文献47

  • 1李志农,吕亚平,范涛,冷传广.基于经验模态分解的机械故障欠定盲源分离方法[J].航空动力学报,2009,24(8):1886-1892. 被引量:18
  • 2程军圣,于德介,杨宇.EMD方法在转子局部碰摩故障诊断中的应用[J].振动.测试与诊断,2006,26(1):24-27. 被引量:46
  • 3于德介,程军圣,杨宇.机械故障诊断的Hilbert-Huang变换方法[M].北京:科学出版社,2007.
  • 4DAVID L,IGNACIO S,JESUS I,et al.A fast blind SIMO channel identification algorithm for sparse sources[J].IEEE Signal Processing Letters,2003,10(5):148-151.
  • 5DAVID L,IGNACIO S,LUIS V,et al.Underdetermined blind separation of sparse sources with instantaneous and convolutive mixtures[C] //2003 IEEE XlII Workshop on Neural Networks for Signal Processing,2003:279-288.
  • 6LI Yuanqing,AMARI S I,ANDRZEJ C,et al.Probability estimation for recoverability analysis of blind source separation based on sparse representation[J].IEEE Transactions on Information Theory,2006,52(7):3139-3152.
  • 7LI Yuanqing,AMARI S I,ANDRZEJ C,et al.Underdetermined blind source separation based on sparse representation[J].IEEE Transactions on Signal Processing,2006,54(2):423-437.
  • 8FABIAN J T,CARLOS G P,ELMAR W L.Median-based clustering for underdetermined blind signal processing[J].IEEE Signal Processing Letters,2006,13(2):96-99.
  • 9LU Yao,LI Shuangtian.Underdetermined blind source separation of anechoic speech mixtures in the time-frequency domain[C] // ICSP2008 Proceedings,2008:22-25.
  • 10TAKEHIRO Hamada,KAZUSHI Nakano,AKIHIRO Ichijo.Wavelet-based underdetermined blind source separation of speech mixtures[C] // International Conference on Control,Automation and Systems 2007:2790-2794.

共引文献288

同被引文献20

引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部