基于改进经验模态分解的HHT密集模态识别方法
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摘要
针对传统HHT方法不能有效识别密集模态的问题,提出基于改进经验模态分解(EMD)的HHT密集模态识别方法。EMD密频信号分解能力不足是限制HHT法识别密集模态的主要原因,因此在EMD分解过程中嵌入信号调频(FM)和模态解相关操作提升其分解密频信号的能力,称改进后的方法为调频—解相关模态分解(FM-DEMD)。以FM-DEMD分解取代传统HHT法中的EMD分解,得到改进HHT模态识别方法。仿真实验证明,传统HHT法和ITD法密集模态识别失效时,改进HHT法仍能准确地识别密集模态信息。
In view of traditional HHT method which can't effectively identify dense modes,this paper proposed a modified HHT method based on improved empirical mode decomposition. The lack of decomposition ability of EMD was the main reason to limit HHT method to identify dense modes. Therefore,it would enhance the EMD decomposition capability by embedding signal frequency modulation( FM) and mode decorrelation operation into EMD decomposition process,and the combined mode decomposition method was called FM-DEMD. By replacing EMD in the traditional HHT method with FM-DEMD,then it obtained the modified HHT method. Simulation results show that the modified HHT method can accurately identify dense modes even if traditional HHT and ITD fail.
In view of traditional HHT method which can't effectively identify dense modes,this paper proposed a modified HHT method based on improved empirical mode decomposition. The lack of decomposition ability of EMD was the main reason to limit HHT method to identify dense modes. Therefore,it would enhance the EMD decomposition capability by embedding signal frequency modulation( FM) and mode decorrelation operation into EMD decomposition process,and the combined mode decomposition method was called FM-DEMD. By replacing EMD in the traditional HHT method with FM-DEMD,then it obtained the modified HHT method. Simulation results show that the modified HHT method can accurately identify dense modes even if traditional HHT and ITD fail.
引文
[1] Huang N E,Shen Zheng,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc of the Royal Society A,1998,454(1971):903-995.
[2] Huang N E,Shen Zheng,Long S R. A new view of nonlinear water waves:the Hilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31(1):417-457.
[3]郭艳平,颜文俊.基于EMD和优化K-均值聚类算法诊断滚动轴承故障[J].计算机应用研究,2012,29(7):2555-2557.
[4]郭艳平,颜文俊.一种新的IMF判据及其在风力发电机滚动轴承故障诊断中的应用[J].计算机应用研究,2012,29(9):3362-3364.
[5]徐信,李志华,杨越.基于EMD-ICA的激电数据降噪处理方法[J].计算机应用研究,2017,34(6):1737-1739,1744.
[6]龚敏,邱燚可可,孟祥栋,等.基于HHT的雷管实际延时识别法在城市环境微差爆破中的应用[J].振动与冲击,2015,34(10):206-212.
[7]练继建.水电站厂房结构研究[M].北京:中国水利水电出版社,2007:237-238.
[8] Rilling G,Flandrin P. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Trans on Signal Processing,2008,56(1):85-95.
[9] Deering R,Kaiser J F. The use of a masking signal to improve empirical mode decomposition[C]//Proc of IEEE International Conference on Acoustics,Speech,and Signal Processing. Piscataway,NJ:IEEE Press,2005:485-488.
[10]孙伟峰,彭玉华,杨阳,等.经验模态分解频率分辨率的一种改进方法[J].计算机工程与应用,2010,46(1):129-133.
[11]胡维平,莫家玲,杜明辉.经验模态分解中的频域分辨率及其改进方法[J].华南理工大学学报:自然科学版,2007,35(5):15-19.
[12] Tang Baoping,Dong Shaojiang,Song Tao. Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation[J]. Signal Processing,2012,92(1):248-258.
[13]Mandic D,Rehman N,Wu Zhaohua,et al. Empirical mode decomposition based time-frequency analysis of multivariate signals:the power of adaptive data analysis[J]. IEEE Signal Processing Magazine,2013,30(6):74-86.
[14]Mandic D P. Filter bank property of multivariate empirical mode decomposition[J]. IEEE Trans on Signal Processing,2011,59(5):2424-2426.
[15]黄天立,邱发强,楼梦麟.基于改进HHT方法的密集模态结构参数识别[J].中南大学学报:自然科学版,2011,42(7):2054-2062.
[16] Wu Zhaohua,Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proc of the Royal Society A,2004,460:1597-1611.
[17]Wu Zhaohua,Huang N E. Ensemble empirical mode decomposition:a noise assisted data analysis method[J]. Advance in Adaptive Data Analysis,2009,1(1):1-41.
[18]肖瑛,殷福亮.解相关EMD:消除模态混叠的新方法[J].振动与冲击,2015,34(4):25-29.
[2] Huang N E,Shen Zheng,Long S R. A new view of nonlinear water waves:the Hilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31(1):417-457.
[3]郭艳平,颜文俊.基于EMD和优化K-均值聚类算法诊断滚动轴承故障[J].计算机应用研究,2012,29(7):2555-2557.
[4]郭艳平,颜文俊.一种新的IMF判据及其在风力发电机滚动轴承故障诊断中的应用[J].计算机应用研究,2012,29(9):3362-3364.
[5]徐信,李志华,杨越.基于EMD-ICA的激电数据降噪处理方法[J].计算机应用研究,2017,34(6):1737-1739,1744.
[6]龚敏,邱燚可可,孟祥栋,等.基于HHT的雷管实际延时识别法在城市环境微差爆破中的应用[J].振动与冲击,2015,34(10):206-212.
[7]练继建.水电站厂房结构研究[M].北京:中国水利水电出版社,2007:237-238.
[8] Rilling G,Flandrin P. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Trans on Signal Processing,2008,56(1):85-95.
[9] Deering R,Kaiser J F. The use of a masking signal to improve empirical mode decomposition[C]//Proc of IEEE International Conference on Acoustics,Speech,and Signal Processing. Piscataway,NJ:IEEE Press,2005:485-488.
[10]孙伟峰,彭玉华,杨阳,等.经验模态分解频率分辨率的一种改进方法[J].计算机工程与应用,2010,46(1):129-133.
[11]胡维平,莫家玲,杜明辉.经验模态分解中的频域分辨率及其改进方法[J].华南理工大学学报:自然科学版,2007,35(5):15-19.
[12] Tang Baoping,Dong Shaojiang,Song Tao. Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation[J]. Signal Processing,2012,92(1):248-258.
[13]Mandic D,Rehman N,Wu Zhaohua,et al. Empirical mode decomposition based time-frequency analysis of multivariate signals:the power of adaptive data analysis[J]. IEEE Signal Processing Magazine,2013,30(6):74-86.
[14]Mandic D P. Filter bank property of multivariate empirical mode decomposition[J]. IEEE Trans on Signal Processing,2011,59(5):2424-2426.
[15]黄天立,邱发强,楼梦麟.基于改进HHT方法的密集模态结构参数识别[J].中南大学学报:自然科学版,2011,42(7):2054-2062.
[16] Wu Zhaohua,Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proc of the Royal Society A,2004,460:1597-1611.
[17]Wu Zhaohua,Huang N E. Ensemble empirical mode decomposition:a noise assisted data analysis method[J]. Advance in Adaptive Data Analysis,2009,1(1):1-41.
[18]肖瑛,殷福亮.解相关EMD:消除模态混叠的新方法[J].振动与冲击,2015,34(4):25-29.