Hilbert-Huang变换与大电机局部放电超声信号消噪
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摘要
通过分析Hilbert-Huang变换(HHT)的核心经验模态分解(empirical mode decomposition,EMD)算法及其实现过程,提出了基于EMD算法的数据压缩消噪。运用仿真模型信号和实验信号对提出地消噪算法效果进行验证;对同样条件下的数据源,与基于db2小波、db8小波的数据压缩消噪的效果进行了分析比较。此外,运用研制地硬件系统,在重庆某电机厂进行实验,采集在电机运行地真实环境下局部放电超声信号,并对其进行基于EMD算法、db2小波和db8小波数据压缩消噪分析和比较。仿真和实验结果分析表明,基于EMD算法的数据压缩消噪,与基于db2小波、db8小波的数据压缩消噪能达到同样的消噪效果,甚至更优,而且不损耗原信号能量;在对真实信号处理方面,前者更优于后者。
Empirical mode decomposition(EMD) algorithm is introduded as the core of the Hilbert-huang transform(HHT),and implementation process of EMD is analyzed.Then data compression denoising algorithm based on EMD is proposed,simulation and experimental signals are used for verification of the effect of EMD.In the same data sources,the comparison of data compression denoising approaches based on the EMD,db2 wavelet and db8 wavelet are conducted.In addition,physical experiment of the same analysis and comparisons are conducted on a running motor in a Chongqing electrical plant.Simulation and experimental results show that data compression denoising algorithm based on EMD can achieve the same denoising effect,or even better than based on db2 wavelet,db8 wavelet.The former is more perfect than the latter in the real signal processing,and denoising based on EMD is not loss of the original signal energy.
Empirical mode decomposition(EMD) algorithm is introduded as the core of the Hilbert-huang transform(HHT),and implementation process of EMD is analyzed.Then data compression denoising algorithm based on EMD is proposed,simulation and experimental signals are used for verification of the effect of EMD.In the same data sources,the comparison of data compression denoising approaches based on the EMD,db2 wavelet and db8 wavelet are conducted.In addition,physical experiment of the same analysis and comparisons are conducted on a running motor in a Chongqing electrical plant.Simulation and experimental results show that data compression denoising algorithm based on EMD can achieve the same denoising effect,or even better than based on db2 wavelet,db8 wavelet.The former is more perfect than the latter in the real signal processing,and denoising based on EMD is not loss of the original signal energy.
引文
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[14]ZOU Q,TANG J T,TANG Y.Hilbert-Huangtransform for ECG de-noising[J].Chinese Journal ofMedical Physics,2007,24(4):309-312.
[15]HUANG N E.The empirical mode decomposition andthe Hilbert spectrum for nonlinear and non-stationarytime series analysis[J].Proceedings of the RoyalSociety,1998,454(A):903-995.
[16]HUANG N E,WU Z.An adaptive data analysismethod for nonlinear and nonstationary time series:The empirical mode decomposition and Hilbert spectralanalysis[C]∥Proceedings of the 4th InternationalConference on Wavelet and Its Application,Macao,China:IEEE,363-376.
[17]HUANG N E,WU Z.A review on Hilbert-huangtransform:method and its applications to geophysicalstudies[J].Reviews of Geophysics,2008,46(2):1-23.
[18]WU Z,HUANG N E.A study of the characteristics ofwhite noise using the empirical mode decompositionmethod[J].Proceedings of the Royal Society ofLondon,2004,460(2046):1597-1611.
[19]FLANDRIN P,RILLING G,GONCALVES P.Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2004,11(2):112-114.
[20]SATISH L,NAZNEEN B.Wavelet-based denoising ofpartial discharge signals buried in excessive noise andinterference[J].IEEE Trans on Dielectrics andElectrical Insulation,2003,10(2):354-367.
[21]北京鹏翔科技有限公司.PXR系列谐振式声发射传感器技术参数[M].北京:北京鹏翔科技有限公司,2009.
[2]OLIVER B M.Directional electromagnetic couplers[J].Proceedings of the Institute of Radio Engineers,1954,42:1686-1694.
[3]NATTRAS D H.The early history of partial dischargeresearch[J].IEEE Electr Insul Mag,1993,19(4):27-31.
[4]孙才新,李新,杨永明.从白噪声中提取局部放电信号的小波变换方法研究[J].电工技术学报,1999,14(3):47-50.SUN CAI-XIN,LI XIN,YANG YONG-MING.Themethod of drawing the PD signals from white noise bywavelet analysis[J].Transactions of ChinaElectrotechnical society,1999,14(3):47-50.
[5]杨永明,孙才新,李新.用小波分析去除局部放电在线监测中的白噪干扰[J].高压电器,1999(3):8-12.YANG YONG-MING,SUN CAI-XIN,LI XIN.Themethod of eliminating white noise at on-line detection ofpartial discharge by wavelet analysis[J].High VoltageApparatus,1999(3):8-12.
[6]杨永明,严欣平,孙才新.抑制宽频带局部放电在线监测系统中周期性干扰的直接陷波滤波法的研究[J].仪器仪表学报,2001(4):419-421.YANG YONG-MING,YAN XIN-PING,SUN CAI-XIN.The study of direct trap wave filtering to rejectperiodic interference in partial discharge widebandedmeasurement system on line[J].Chinese Journal ofScientific Instrument,2001(4):419-421.
[7]唐炬,许中荣,孙才新,等.应用复小波变换抑制GIS局部放电信号中白噪声干扰的研究[J].中国电机工程学报,2005,25(16):30-34.TANG JU,XU ZHONG-RONG,SUN CAI-XIN,et al.Application of complex wavelet transform to suppresswhite-noise interference in GIS PD signals[J].Proceedings of the Csee,2005,25(16):30-34.
[8]唐炬,邓志勇,周倩,等.一种抑制PD白噪干扰的有效复合信息技术[J].重庆大学学报,2008,31(4):401-407.TANG JU,DENG ZHI-YONG,ZHOU QIAN,et al.An effective technique of combining information forreducing the white noise of partial discharge signals[J].Journal of Chongqing University,2008,31(4):401-407.
[9]ADAM O.Advantages of the Hilbert Huang transformfor marine mammals signals analysis[J].J Acoust SocAm,2006,120(5):2965-2973.
[10]HUANG N E,WU M L,QU W D,et al.Applicationsof Hilbert-Huang transform to non-stationary financialtime series analysis[J].Appl Stochastic Models BusInd,2003(19):245-268.
[11]HASSAN H H,PEIRC E J W.Empirical modedecomposition(EMD)of potential field data:airbornegravity data as an example[J].Canadian Society ofExploration Geophysicists Recorder,2008,33(1):25-30.
[12]YAN R Q,GAO R X.Hilbert-Huang Transform-Based vibration signal analysis for machine healthmonitoring[J].IEEE Transactions on Instrumentationand Measurement,2006,55(6):2320-2329.
[13]曹晖,曹永红.HH变换在地震动信号分析中的应用[J].重庆大学学报,2008,31(8):922-927.CAO HUI,CAO YONG-HONG.Applicationlimitation of the Hilbert-Huang transform toearthquake ground motion analysis[J].Journal ofChongqing University,2008,31(8):922-927.
[14]ZOU Q,TANG J T,TANG Y.Hilbert-Huangtransform for ECG de-noising[J].Chinese Journal ofMedical Physics,2007,24(4):309-312.
[15]HUANG N E.The empirical mode decomposition andthe Hilbert spectrum for nonlinear and non-stationarytime series analysis[J].Proceedings of the RoyalSociety,1998,454(A):903-995.
[16]HUANG N E,WU Z.An adaptive data analysismethod for nonlinear and nonstationary time series:The empirical mode decomposition and Hilbert spectralanalysis[C]∥Proceedings of the 4th InternationalConference on Wavelet and Its Application,Macao,China:IEEE,363-376.
[17]HUANG N E,WU Z.A review on Hilbert-huangtransform:method and its applications to geophysicalstudies[J].Reviews of Geophysics,2008,46(2):1-23.
[18]WU Z,HUANG N E.A study of the characteristics ofwhite noise using the empirical mode decompositionmethod[J].Proceedings of the Royal Society ofLondon,2004,460(2046):1597-1611.
[19]FLANDRIN P,RILLING G,GONCALVES P.Empirical mode decomposition as a filter bank[J].IEEE Signal Processing Letters,2004,11(2):112-114.
[20]SATISH L,NAZNEEN B.Wavelet-based denoising ofpartial discharge signals buried in excessive noise andinterference[J].IEEE Trans on Dielectrics andElectrical Insulation,2003,10(2):354-367.
[21]北京鹏翔科技有限公司.PXR系列谐振式声发射传感器技术参数[M].北京:北京鹏翔科技有限公司,2009.
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