激光超声信号去噪的经验模态分解实现及改进

罗玉昆; 罗诗途; 罗飞路; 潘孟春

doi:10.3788/OPE.20132102.0479

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激光超声信号去噪的经验模态分解实现及改进

信息科学 | 更新时间：2020-08-12

- 激光超声信号去噪的经验模态分解实现及改进
- Realization and improvement of laser ultrasonic signal denoising based on empirical mode decomposition
- 光学精密工程 2013年21卷第2期页码：479-487
- 作者机构：
  
  国防科学技术大学机电工程与自动化学院
- 作者简介：
- 基金信息：
  
  中国博士后科学基金面上项目资助()
- DOI：10.3788/OPE.20132102.0479
  中图分类号： TN249;TP391
- 收稿日期：2012-07-17，
  
  修回日期：2012-11-08，
  
  网络出版日期：2013-02-23，
  
  纸质出版日期：2013-02-15
- 稿件说明：
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罗玉昆罗诗途罗飞路潘孟春. 激光超声信号去噪的经验模态分解实现及改进[J]. 光学精密工程, 2013,21(2): 479-487

LUO Yu-kun LUO Shi-tu LUO Fei-lu PAN Meng-chun. Realization and improvement of laser ultrasonic signal denoising based on empirical mode decomposition[J]. Editorial Office of Optics and Precision Engineering, 2013,21(2): 479-487
罗玉昆罗诗途罗飞路潘孟春. 激光超声信号去噪的经验模态分解实现及改进[J]. 光学精密工程, 2013,21(2): 479-487 DOI： 10.3788/OPE.20132102.0479.

LUO Yu-kun LUO Shi-tu LUO Fei-lu PAN Meng-chun. Realization and improvement of laser ultrasonic signal denoising based on empirical mode decomposition[J]. Editorial Office of Optics and Precision Engineering, 2013,21(2): 479-487 DOI： 10.3788/OPE.20132102.0479.

摘要

考虑激光超声检测过程中噪声对缺陷和材料特征分析和检测的影响，本文以激光超声信号去噪为目的，研究了基于经验模态分解（EMD）的激光超声信号时间尺度滤波过程。针对分解过程中固有模态函数（IMF）上有用信号与噪声的混叠现象对重构信噪比的影响，结合信号多模态和宽频带的特点，提出了基于峰度检验策略的时域加窗方法。该方法通过局部峰度检验判断重构起点附近IMF中有用信号的位置及信噪分界点，利用Turkey-Hanning窗保存有用信号，抑制噪声，实现信号与噪声的解混叠，改善重构信号质量。仿真和实验结果表明，该方法具有良好的自适应性，有效识别并分离了信号和噪声成分，信噪改善比达14 dB以上，相对原始方法提升了3 dB，相对性能增强了20%，并且改进效果随信号受污染程度的加重而愈发突出，有望在高噪声水平下发挥优势。

Abstract

To suppress the influence of noises associated with a laser ultrasonic testing process on the detection for defects and material parameters

the time-scale filtering process of laser ultrasonic signals is studied based on Empirical Mode Decomposition (EMD). As the aliasing between useful signals and noises in Intrinsic Mode Functions (IMF) will reduce the reconstruction signal-to-noise ratio

a time-windowing method with kurtosis test strategy is proposed considering the multi-mode and broad-band characteristics of laser ultrasonic signals.With proposed method

the positions and boundaries of useful signals in IMFs near the reconstruction start are estimated by computing the local kurtosis values. Then

a Turkey-Hanning window is used to preserve useful signals and suppress the noises. Thus

aliasing removal is achieved and the reconstruction signal quality is improved. Simulation and experimental data show that the method proposed has a good self-adaptability

and it recognizes and separates the signal and noise components effectively. The signal-to-noise improvement factor is over 14 dB

which improves the original method by 3 dB and enhances the performance by 20%. Besides

as the heavier the signal is polluted

the better the improving effect is

the advantage of the method is expected to be given in high noise levels.

关键词

Keywords

references

JIN Y, UME I C. Laser ultrasonic technique for evaluating solder bump defects in flip chip packages using modal and signal analysis methods [J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2010, 57(4): 920-932.[2]谭项林，潘孟春，罗诗途，等. 基于激光超声的材料力学性能测试方法研究[J]. 激光与红外,2011,41(11):1183-1187.TAN X L, PAN M C, LUO SH T, et al.. Testing method for mechanical properties of materials based on laser-generated ultrasonic [J]. Laser ＆ Infrared, 2011, 41(11):1183-1187. (in Chinese)[3]耿森林,尚志远,石焕文,等. 基于小波变换的激光超声信号处理[J]. 云南大学学报:自然科学版,2005,27(1):44-46,51.GENG S L, SHANG ZH Y, SHI H W. Laser ultrasound signal processing based on Wavelet Transform [J]. Journal of Yunnan University:Natural Sciences Edition, 2005,27(1):44-46,51. (in Chinese)[4]龚志强,邹明玮,高新全,等. 基于非线性时间序列分析经验模态分解和小波分解异同性的研究[J]. 物理学报,2005,54(8):3947-3957.GONG ZH Q, ZOU M W, GAO X Q, et al.. On the difference between empirical mode decomposition and wavelet decomposition in the nonlinear time series [J]. Acta Physica Sinica, 2005, 54(8): 3947-3957. (in Chinese)[5]孙伟峰,彭玉华,许建华. 基于EMD的激光超声信号去噪方法[J]. 山东大学学报:工学版,2008,38(5):121-126.SUN W F, PENG Y H, XU J H. A de-noising method for laser ultrasonic signal based on EMD [J]. Journal of Shandong University:Engineering Science, 2008, 38(5): 121-126. (in Chinese)[6]李欣,梅德庆,陈子辰. 基于经验模态分解和希尔伯特-黄变换的精密孔镗削颤振特征提取[J]. 光学精密工程,2011,19(6):1291-1297.LI X, MEI D Q, CHEN Z C. Feature extraction of chatter for precision hole boring processing based on EMD and HHT [J]. Opt. Precision Eng., 2011, 19(6): 1291-1297. (in Chinese)[7]刘玉梅,袁文华,彭雨. 基于小波EMD的柴油机油耗量测量信号去噪处理[J]. 中南大学学报:自然科学版,2012,43(2):516-521.LIU Y M, YUAN W H, PENG Y. Denoising disposal of measurement signals of fuel consumption from diesel engine based on wavelet EMD method [J]. Journal of Central South University:Natural Science, 2012, 43(2): 516-521. (in Chinese)[8]陈卫萍,潘紫微. 基于经验模态分解的小波阈值滤波去噪[J].安徽工业大学学报:自然科学版,2010,27(4):397-400.CHEN W P, PAN Z W. Denoising of wavelet threshold filtering based on empirical mode decomposition [J]. Journal of Anhui University of Technology:Natural Science, 2010,27(4):397-400. (in Chinese)[9]秦品乐,林焰,陈明. 基于平移不变小波阈值算法的经验模态分解方法[J]. 仪器仪表学报,2008,29(12):2637-2641.QIN P L, LIN Y, CHEN M. Empirical mode decomposition method based on wavelet with translation invariance algorithm [J]. Chinese Journal of Scientific Instrument, 2008, 29(12):2637-2641.[10]HUANG N E,SHEN ZH,LON S R,et al.. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [C]. Proceedings of the Royal Society of London A, 1998, 454: 903-995.[11]WU Z,HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method [J]. Proceedings of Royal Society London A, 2004, 460: 1597-1611.[12]钱昌松,刘代志,刘志刚,等. 基于递归高通滤波的经验模态分解及其在地震信号分析中的应用[J]. 地球物理学报，2010，53(5)：1215-1225.QIAN CH S, LIU D ZH, LIU ZH G, et al.. EMD based on recursive high-pass filter and its application on seismic signal analysis [J]. Chinese Journal of Geophysics, 2010, 53(5): 1215-1225.[13]MANUEL B V,WENG B W,KENNETH E B. ECG signal denoising and baseline wander correction based on the empirical mode decomposition [J]. Computers in Biology and Medicine, 2008, 38: 1-13.[14]MILLIOZ F, MARTIN N. Circularity of the STFT and spectral kurtosis for time-frequency segmentation in Gaussian environment [J]. IEEE Transactions on Signal Processing, 2011, 59(2): 515-524.[15]郭洁,陈祥献,黄海,等. 基于峰度的电力变压器铁芯松动故障在线监测方法[J]. 仪器仪表学报,2010,31(11)：2401-2407.GUO J, CHEN X X, HUANG H, et al.. Application of kurtosis in on line detection of transformer iron core looseness [J]. Chinese Journal of Scientific Instrument, 2010, 31(11): 2401-2407. (in Chinese)[16]朱军华,余岭.基于时间序列分析与高阶统计矩的结构损伤检测[J].东南大学学报:自然科学版,2012,42(1):137-143.ZHU H J, YU L. Damage detection based on time series analysis and higher statistical moments [J]. Journal of Southeast University:Natural Sciences Edition, 2012, 42(1): 137-143. (in Chinese)

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