基于AR模型的声发射信号到达时间自动识别
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摘要
声发射信号到达时间的信息,对于声发射事件的定位、识别以及声发射源机理分析都是非常重要的。实际应用中,常用人工读取或通过设定幅值阈值来获取信号的到达时间。针对以上常用方法的缺点,结合噪声信号的AR模型和声发射信号的AR模型,应用Akaike信息准则,实现了对声发射信号到达时间的自动识别。对实验数据的识别结果显示,该方法对信号的幅频特性变化比较敏感。在相同信噪比的情况下,该方法识别的偏差要小于阈值法。当信噪比较低时,阈值法可能会给出错误的结果,而该方法仍然能够给出较准确的结果。
The information of acoustic emission(AE) signal's arrival time is very important for event location,event identification and source mechanism analysis.In practical,engineering,the manual picking and amplitude threshold setting up are routinely used to determine the arrival time of acoustic emission signal.To improve the defect of commonly used method,the first arrival time of AE signal was automatically determined through combining AR model of the noise with the acoustic emission signal based on Akaike's information criterion(AIC).The results show that the proposed method is more sensitive to the change of amplitude-frequency characteristics in contrast to the amplitude threshold method.In case the signal-to-noise ratio(SNR) takes the same value,the error of the approach is less than that of threshold detection.Especially,when the SNR is so law that the detection by use of threshold will be no longer effective,the results of the approach are still of enough accuracy.
The information of acoustic emission(AE) signal's arrival time is very important for event location,event identification and source mechanism analysis.In practical,engineering,the manual picking and amplitude threshold setting up are routinely used to determine the arrival time of acoustic emission signal.To improve the defect of commonly used method,the first arrival time of AE signal was automatically determined through combining AR model of the noise with the acoustic emission signal based on Akaike's information criterion(AIC).The results show that the proposed method is more sensitive to the change of amplitude-frequency characteristics in contrast to the amplitude threshold method.In case the signal-to-noise ratio(SNR) takes the same value,the error of the approach is less than that of threshold detection.Especially,when the SNR is so law that the detection by use of threshold will be no longer effective,the results of the approach are still of enough accuracy.
引文
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[18]Lokajicek T,Klima K.A first arrival identification system ofacoustic emission(AE)signals by means of a high-order sta-tistics approach[J].Measurement Science and Technology,2006,17(9):2461-466.
[19]Akaike H.Markovian representation of stochastic processesand its application to the analysis of autoregressive moving av-erage process[J].Annals of the Institute of Statistical Mathe-matics,1974,26(1):363-387.
[20]Kitagawa G,Akaike H.A procedure for the modeling of non-stationary time series[J].Annals of the Institute of StatisticalMathematics,1978,30(1):351-363.
[2]Gorman M R.Plate Wave Acoustic Emission[J].Journal ofthe Acoustical Society of America,1990,90(1):358-364.
[3]Hamstad MA,Downs K S.On Characterization and Locationof Acoustic Emission Sources in Real Size Composite Struc-tures:A Waveform Study[J].Journal of Acoustic Emission,1995,13(12):31-41.
[4]Steven M,Ziola S M,Gorman M R.Source location in thinplates using cross-correlation[J].Journal of the AcousticalSociety of America,1991,90(5):2551-2556.
[5]Hamstad M A,Gallagher A O,Gary J.Examination of theApplication of a Wavelet Transform to Acoustic Emission Sig-nals:Part 1 Source Identification[J].Journal of Acoustic E-mission,2002,20:39-61.
[6]Hamstad M A,Gallagher A O,Gary J.Examination of theApplication of a Wavelet Transform Applied to Acoustic E-mission Signals:Part 2 Source Location[J].Journal of A-coustic Emission,2002,20:62-82.
[7]李光海,刘时风.基于小波分析的声发射源定位技术[J].机械工程学报,2004,40(7):136-140.
[8]Jochen H.Kurz,Christian U.Grosse,Hans-Wolf Reinhardt.Strategies for reliable automatic onset time picking of acousticemissions and of ultrasound signals in concrete[J].Ultrason-ics,2005,43(7):538-546.
[9]Baer M,Kradolfer U.An automatic phase picker for local andteleseismic events[J].Bulletin of the Seismological Societyof America,1987,77(4):1437-1445.
[10]Earle P,Shearer P.Characterization of global seismograms u-sing an automatic picking algorithm[J].Bulletin of the Seis-mological Society of America,1994,84(2):366-376.
[11]Sleeman R,Van Eck T.Robust automatic P-phase picking:an on-line implementation in the analysis of broadband seis-mogram recordings[J].Physics of the Earth and PlanetaryInteriors,1999,113(1):265-275.
[12]Leonard M,Kennett B L N.Multi-component autoregressivetechniques for the analysis of seismograms[J].Physics of theEarth and Planetary Interiors,1999,113(1):247-263.
[13]Leonard M.Comparison of manual and automatic onset timepicking[J].Bulletin of the Seismological Society of America,2000,90(6):1384-1390.
[14]Maeda N.A method for reading and checking phase times inauto-processing system of seismic wave data[J].J.Seismol.Soc.Jpn,1985(38):365-379.
[15]Saragiotis C HD,Hadjileontiadis LJ,Panas S M.PAI-S/K:a robust automatic seismic P-phase arrival identificationscheme[J].IEEE Transactions on Geoscience and RemoteSensing,2002,40(6):1395-1404.
[16]刘代志,王仁明,李夕海,等.基于小波包分解及AR模型的单通道地震波信号初至点检测[J].地球物理学报,2005,48(5):1098-1102.
[17]Zhang H J,Thurber C,Rowe C.Automatic P-wave arrivaldetection and picking with multiscale wavelet analysis for sin-gle-component recordings[J].Bulletin of the SeismologicalSociety of America,2003,93(5):1904-1912.
[18]Lokajicek T,Klima K.A first arrival identification system ofacoustic emission(AE)signals by means of a high-order sta-tistics approach[J].Measurement Science and Technology,2006,17(9):2461-466.
[19]Akaike H.Markovian representation of stochastic processesand its application to the analysis of autoregressive moving av-erage process[J].Annals of the Institute of Statistical Mathe-matics,1974,26(1):363-387.
[20]Kitagawa G,Akaike H.A procedure for the modeling of non-stationary time series[J].Annals of the Institute of StatisticalMathematics,1978,30(1):351-363.
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