激光超声信号去噪的经验模态分解实现及改进
详细信息 本馆镜像全文    |  推荐本文 | | 获取馆网全文
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14dB以上,相对原始方法提升了3dB,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。
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.
引文
[1]JIN Y,UME I C.Laser ultrasonic technique for e-valuating solder bump defects in flip chip packagesusing modal and signal analysis methods[J].IEEETransactions on Ultrasonics,Ferroelectrics andFrequency 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..Testingmethod for mechanical properties of materials basedon laser-generated ultrasonic[J].Laser&Infra-red,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 ul-trasound signal processing based on Wavelet Trans-form[J].Journal of Yunnan University:NaturalSciences Edition,2005,27(1):44-46,51.(in Chi-nese)
    [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..Onthe difference between empirical mode decomposi-tion and wavelet decomposition in the nonlinear timeseries[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-noisingmethod for laser ultrasonic signal based on EMD[J].Journal of Shandong University:EngineeringScience,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 ofchatter for precision hole boring processing based onEMD 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 dis-posal of measurement signals of fuel consumptionfrom diesel engine based on wavelet EMD method[J].Journal of Central South University:NaturalScience,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 waveletthreshold filtering based on empirical mode decom-position[J].Journal of Anhui University ofTechnology: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 de-composition method based on wavelet with transla-tion invariance algorithm[J].Chinese Journal ofScientific Instrument,2008,29(12):2637-2641.
    [10]HUANG N E,SHEN ZH,LON S R,et al..Theempirical mode decomposition and the Hilbertspectrum for nonlinear and non-stationary time se-ries analysis[C].Proceedings of the Royal Socie-ty of London A,1998,454:903-995.
    [11]WU Z,HUANG N E.A study of the characteris-tics of white noise using the empirical mode decom-position method[J].Proceedings of Royal Socie-ty 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 ap-plication on seismic signal analysis[J].ChineseJournal of Geophysics,2010,53(5):1215-1225.
    [13]MANUEL B V,WENG B W,KENNETH E B.ECG signal denoising and baseline wander correc-tion based on the empirical mode decomposition[J].Computers in Biology and Medicine,2008,38:1-13.
    [14]MILLIOZ F,MARTIN N.Circularity of the STFTand spectral kurtosis for time-frequency segmentationin Gaussian environment[J].IEEE Transactions onSignal 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..Appli-cation of kurtosis in on line detection of transform-er iron core looseness[J].Chinese Journal of Sci-entific 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 timeseries analysis and higher statistical moments[J].Journal of Southeast University:Natural SciencesEdition,2012,42(1):137-143.(in Chinese)

版权所有:© 2023 中国地质图书馆 中国地质调查局地学文献中心