超声脉冲回波信号分析与识别
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摘要
目前,超声无损检测技术已应用于很多领域,例如医学检测、海底探测、材料探伤、超声测量等。然而由于超声在传播时的固有特性,使得超声无损检测技术在实际应用时遇到一些干扰因素,比如在医学检测领域,超声在人体内传播时遇到不同组织界面会发生多重回波,除首次回波外其他的回波会对生物组织的结构成像造成阴影或增强,给检测人员的判断带来干扰。因此论文提出了一种算法,用于识别第一次回波和多重回波。这项工作对提高结果的准确性和精度、对超声无损检测技术的进一步推广和应用具有一定意义。
1、超声脉冲波在检测多层介质时,反射回波会包含不同界面的多重回波,不利于对回波特征值的提取。因此论文提出了一种用于识别多层介质的多重回波的算法,以区别回波中首次回波和多重回波,并能够找出该多重回波的反射源。该算法基于多层介质的不同介质层对超声波表现出不同的反射率、不同的回波衰减、不同的延时等物理特性,得出穿越相同介质的超声回波比穿越不同介质的超声回波在频谱上有更大的相似性的结论,各回波频谱间的相似程度采用能量谱最小二乘(LMS)偏差表示,然后结合各个回波TOF的时间逻辑关系完成对首次回波和多重回波的识别。
2、采用实验仿真和真实实验的方法对文中提出的算法进行了验证。仿真结果表明,10组80个回波的正确识别率达到98.75%。在真实实验中,两层介质的回波正确识别率100%。仿真中使用的回波采用基本信号在频域与指数衰减项相乘的办法得到,基本信号取自实际实验的铁块超声脉冲回波(高信噪比)。
3、使用该算法对信噪比较低的多重回波进行了仿真实验。结果显示,回波正确识别率降低。根据小波变换的原理,信号做离散小波变换后,相当于信号通过了高通和低通滤波器,小波系数体现高频成分,表现信号的细节,尺度系数体现低频成分,表现信号基本特征。因此,论文提出用小波变换的尺度系数代替原信号分析,结果回波识别率有效提高。
总之,超声脉冲回波的首次回波和多重回波识别算法,可以基于回波间的能量谱最小二乘偏差和各个回波TOF的时间逻辑关系进行判断。对于低信噪比的回波可以采用其小波变换后的尺度系数代替原信号使用本算法来识别。
At present, ultrasonic nondestructive testing technology has been widely applied to many areas, such as medical testing、survey the sea、machines、materials, etc. However, according to the characteristics of ultrasound propagation, ultrasonic nondestructive testing technology encounters some interference factor in practical application. For example, in medical testing, reverberation echoes are tested except the primary echo when ultrasound propagating in human body. In addition to the primary echo, reverberation echoes make the structure of the organization as a shadow or enhanced for the inspection officer. Therefore, a new algorithm are proposed in this article used to identify and separate the primary echo and reverberation echoes. This study has great sense in improving the accuracy and precision of the results of ultrasound testing and in further developing the application of ultrasonic nondestructive testing technology.
1、Echoes involve reverberation echoes when ultrasound pulses are used to test multilayer media. Reverberation echoes are unfavorable to extracting the features of echoes. Therefore, a new algorithm is proposed in this article to identify reverberation echoes and to find out the source of the reverberation echoes. Based on the physical characteristics of ultrasound propagating in multilayered media,such as different reflectivity、different decay and different time delay, using this algorithm we can get the result that echoes through the same media have greater similarity than echoes through multilayered media. And the similarity score of echoes spectrum is denoted by Least mean square deviation of energy spectrum, then identifying primary echo and reverberation echoes with the time logical relationship of different echoes.
2、The algorithm proposed in this article is tested by simulation and real experiment. And simulation results show that identification rate reaches 98.75% in ten groups 80 echoes. Echoes used in simulation get from basic signal in frequency domain multiplying exponential decay. And the basic signal extracted from the practical experiments of ultrasound pulse waves in the iron block (high signal to noise ratio).
3、With the algorithm proposed in this article, we simulate the reverberation echoes with low signal to noise ratio. The experimental results show that recognition rate of echoes are greatly reduced. According to the principle of wavelet transformation, signal adopted discrete wavelet transformation is equivalent to the signal through high pass filter or low pass filter. Wavelet coefficients indicate the high frequency components, expressing the signal details, and scale coefficients indicate the low frequency components, expressing the basic features of the signals. Therefore, in this article we use the scale coefficients of wavelet transformation to take the place of the basic signal, which can effectively improve the recognition rate of echoes.
In short, the algorithm proposed in this article to identify primary echo and reverberation echoes can be judged by least mean square deviation of energy spectrum and the time logical relationship of different echoes. To identify the echoes of low signal to noise ratio, we can apply the algorithm by using scale coefficients of wavelet transformation in place of the basic signal.
1、超声脉冲波在检测多层介质时,反射回波会包含不同界面的多重回波,不利于对回波特征值的提取。因此论文提出了一种用于识别多层介质的多重回波的算法,以区别回波中首次回波和多重回波,并能够找出该多重回波的反射源。该算法基于多层介质的不同介质层对超声波表现出不同的反射率、不同的回波衰减、不同的延时等物理特性,得出穿越相同介质的超声回波比穿越不同介质的超声回波在频谱上有更大的相似性的结论,各回波频谱间的相似程度采用能量谱最小二乘(LMS)偏差表示,然后结合各个回波TOF的时间逻辑关系完成对首次回波和多重回波的识别。
2、采用实验仿真和真实实验的方法对文中提出的算法进行了验证。仿真结果表明,10组80个回波的正确识别率达到98.75%。在真实实验中,两层介质的回波正确识别率100%。仿真中使用的回波采用基本信号在频域与指数衰减项相乘的办法得到,基本信号取自实际实验的铁块超声脉冲回波(高信噪比)。
3、使用该算法对信噪比较低的多重回波进行了仿真实验。结果显示,回波正确识别率降低。根据小波变换的原理,信号做离散小波变换后,相当于信号通过了高通和低通滤波器,小波系数体现高频成分,表现信号的细节,尺度系数体现低频成分,表现信号基本特征。因此,论文提出用小波变换的尺度系数代替原信号分析,结果回波识别率有效提高。
总之,超声脉冲回波的首次回波和多重回波识别算法,可以基于回波间的能量谱最小二乘偏差和各个回波TOF的时间逻辑关系进行判断。对于低信噪比的回波可以采用其小波变换后的尺度系数代替原信号使用本算法来识别。
At present, ultrasonic nondestructive testing technology has been widely applied to many areas, such as medical testing、survey the sea、machines、materials, etc. However, according to the characteristics of ultrasound propagation, ultrasonic nondestructive testing technology encounters some interference factor in practical application. For example, in medical testing, reverberation echoes are tested except the primary echo when ultrasound propagating in human body. In addition to the primary echo, reverberation echoes make the structure of the organization as a shadow or enhanced for the inspection officer. Therefore, a new algorithm are proposed in this article used to identify and separate the primary echo and reverberation echoes. This study has great sense in improving the accuracy and precision of the results of ultrasound testing and in further developing the application of ultrasonic nondestructive testing technology.
1、Echoes involve reverberation echoes when ultrasound pulses are used to test multilayer media. Reverberation echoes are unfavorable to extracting the features of echoes. Therefore, a new algorithm is proposed in this article to identify reverberation echoes and to find out the source of the reverberation echoes. Based on the physical characteristics of ultrasound propagating in multilayered media,such as different reflectivity、different decay and different time delay, using this algorithm we can get the result that echoes through the same media have greater similarity than echoes through multilayered media. And the similarity score of echoes spectrum is denoted by Least mean square deviation of energy spectrum, then identifying primary echo and reverberation echoes with the time logical relationship of different echoes.
2、The algorithm proposed in this article is tested by simulation and real experiment. And simulation results show that identification rate reaches 98.75% in ten groups 80 echoes. Echoes used in simulation get from basic signal in frequency domain multiplying exponential decay. And the basic signal extracted from the practical experiments of ultrasound pulse waves in the iron block (high signal to noise ratio).
3、With the algorithm proposed in this article, we simulate the reverberation echoes with low signal to noise ratio. The experimental results show that recognition rate of echoes are greatly reduced. According to the principle of wavelet transformation, signal adopted discrete wavelet transformation is equivalent to the signal through high pass filter or low pass filter. Wavelet coefficients indicate the high frequency components, expressing the signal details, and scale coefficients indicate the low frequency components, expressing the basic features of the signals. Therefore, in this article we use the scale coefficients of wavelet transformation to take the place of the basic signal, which can effectively improve the recognition rate of echoes.
In short, the algorithm proposed in this article to identify primary echo and reverberation echoes can be judged by least mean square deviation of energy spectrum and the time logical relationship of different echoes. To identify the echoes of low signal to noise ratio, we can apply the algorithm by using scale coefficients of wavelet transformation in place of the basic signal.
引文
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[5]Mori K,Spagnoli A,Murakami Y,etc.a new non-contacting testing method for defect detection in concrete[J].NDT&E International,2002,35:399-406.
[6]张广明.超声无损检测中的时频分析理论及其应用研究[D].西安：西安交通大学,1999.
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[18]胡广书,现代信号处理教程[M].北京：清华大学出版社,2004.52-60,72-85,97-103,239-266.
[19]周光平,李坚,刘镇清等.超声信号处理法检测表面缺陷[J].无损检测,1999,18(12)：331-332.
[20]Mouritz AP, Townsend C, Shah Khan MZ. Non-destructive detection of fatigue damage in thick composite by pulse-echo ultrasonics [J].Composites Science and Technology,2000,60:23-32.
[21]陈换过,姜节胜,闫云聚.小波分析在复合材料层合结构损伤检测中的应用[J].应用力学学报,2004.12,21(4)：67：75.
[22]林莉,聂颖,李喜孟,基于Marr小波分析的薄层超声检测[J],材料工程,2007,2(3)：50-56.
[23]徐志辉,林莉,李喜孟等.基于功率谱分析的表面涂层厚度超声无损测量方法[s].中国表面工程,2004,6：7-9.
[24]李建文等,应用模式识别技术识别超声检测的缺陷类型,第25卷第4期增刊.
[25]苏勇,林维正,用信号分析技术检测材料的声速和衰减[J],材料建筑学报,2001,1(4)：22：27.
[26]朱根兴,刘继忠,周晓军,熊勇,张连文,相关技术在超声信号渡越时间参数估计中的应用[J].机床与液压,2005,4：140-145.
[27]董建华等.几种时频分析方法的比较及应用[J].工程地球物理学报,2007,10：312-316.
[28]A bbate A. Application of wavelet transform signal processor to ultrasound[J]. IEEE transactions on instrumentation and measurement,1992,41(4):1147-115.
[29]黄晶,阂沛文.小波分析在管道缺陷超声检测中的应用[J].传感技术学报,2003,(3)：263-266.
[30]林莉,李喜孟.超声波频谱分析技术及其应用[M].北京：机械工业出版社, 2009：1-11.
[31]尚志远.检测声学原理及应用[M].西安：西北大学出版社,1996.
[32]王月雾,李萍,李喜孟.数字化超声回波时域信号拟合技术的初探[J].无损探伤,2003,27(1)：4-7.
[33]Parrilla M, et al. Digital Signal Processing Techniques for High Accuracy Ult rasonic Range Measurement s [C], IEEE,Transactions On Instrumentation and Measurement 1998,1056-1061.
[34]武良丹,贺西平,张小凤.超声回波参数的高斯与牛顿迭代法计算[J].陕西师范大学(自然科学版),2006,34(3)：56-58.
[35]武良丹,贺西平,张小凤等.高斯回波模型在超声回波模拟中的应用及其迭代算法的讨论[J].应用声学,2007,26(劝：119-124.
[35]拉米什.戈皮那思,C.西德尼.罗伯斯.小波与小波变换导论[M].北京：机械工业出版社,2005：11：30.
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[2]K.B.Smith,E.B. Cushman,A comparison of quasi-continuous wave and broadband travel time techniques in the prediction of long-ranger reverberation[J],Journal of Acoustical Society of America,1997,102(4):63-71.
[3]葛哲生,陈仲生.Matlab时频分析技术及其应用[M].北京：人民邮电出版社,2006.
[4]杨福生.小波变换的工程分析.北京：科学出版社,1999.
[5]Mori K,Spagnoli A,Murakami Y,etc.a new non-contacting testing method for defect detection in concrete[J].NDT&E International,2002,35:399-406.
[6]张广明.超声无损检测中的时频分析理论及其应用研究[D].西安：西安交通大学,1999.
[7]林莉,李喜孟.超声波频谱分析技术及其应用[M]。北京：机械工业出版社,2009：1-11.
[8]J.Sloat. Identification of echo reflections[J], Geophysics,1954,13(1):27-35.
[9]E.A. Robinson, Predictive decomposition of time-series with application to seismic exploration,Ph.D Thesis,MIT,1954,32(3):41-48.
[10]M.M.Backus. Water reverberation-their nature and elimination[J]. Geophysics, 1959,24(2):233-261.
[11]K.Lepage. Bottom reverberation in shallow-water:coherent properties as a function of bandwidth, waveguide characteristics,and scatterer distributions[C]. Journal of Acoustical Society of America,1999,106(3):3240-3254.
[12]A.P. Lyons, D,A,Abrahan, Statistical characterization of high-frequency shallow-water seafloor backscatter[C],Journal of Acoustical Society of America 1999,106(3):1307-1315.
[13]E.J. Feleppa, A. Kalisz, J.B. Sokil-Melgar, f.L, Lizzi. T.Liu, AL.Rosado, M.C. Shao, W.R. Fair, Y.wang, M.S. Cookson, V.E. Reuter,W.D.W. Heston, Typing of prostate tissue by ultrasonic spectrum analysis[C]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control,1996,43(4);609-618.
[14]F.L. Lizzi, E.J. Feleppa, M. Astor A.Kalisz, Statistics of ultrasonic spectral parameters for prostate and liver examinations[C],IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control,1996,43(4),935-942.
[15]K.D, Donohue, C.W. Piccoli, B.B. Goldberg, Analyses and classification of tissue with scatterer structure templates[C], IEEE transactions on Ultrasonics, Ferroelectrics and Frequency Control,1996,43(4),935-942.
[16]W.C.A. Pereira, AV.D.G reco, J.C. Machado, Ultrasonic velocity mapping of a multilayered media[J]. Acoustical Imaging,1996,22:63-68.
[17]陈友兴等,自适应滤波在多边界超声检测中的应用[J].中国测试技术,2006,32(14)31：35.
[18]胡广书,现代信号处理教程[M].北京：清华大学出版社,2004.52-60,72-85,97-103,239-266.
[19]周光平,李坚,刘镇清等.超声信号处理法检测表面缺陷[J].无损检测,1999,18(12)：331-332.
[20]Mouritz AP, Townsend C, Shah Khan MZ. Non-destructive detection of fatigue damage in thick composite by pulse-echo ultrasonics [J].Composites Science and Technology,2000,60:23-32.
[21]陈换过,姜节胜,闫云聚.小波分析在复合材料层合结构损伤检测中的应用[J].应用力学学报,2004.12,21(4)：67：75.
[22]林莉,聂颖,李喜孟,基于Marr小波分析的薄层超声检测[J],材料工程,2007,2(3)：50-56.
[23]徐志辉,林莉,李喜孟等.基于功率谱分析的表面涂层厚度超声无损测量方法[s].中国表面工程,2004,6：7-9.
[24]李建文等,应用模式识别技术识别超声检测的缺陷类型,第25卷第4期增刊.
[25]苏勇,林维正,用信号分析技术检测材料的声速和衰减[J],材料建筑学报,2001,1(4)：22：27.
[26]朱根兴,刘继忠,周晓军,熊勇,张连文,相关技术在超声信号渡越时间参数估计中的应用[J].机床与液压,2005,4：140-145.
[27]董建华等.几种时频分析方法的比较及应用[J].工程地球物理学报,2007,10：312-316.
[28]A bbate A. Application of wavelet transform signal processor to ultrasound[J]. IEEE transactions on instrumentation and measurement,1992,41(4):1147-115.
[29]黄晶,阂沛文.小波分析在管道缺陷超声检测中的应用[J].传感技术学报,2003,(3)：263-266.
[30]林莉,李喜孟.超声波频谱分析技术及其应用[M].北京：机械工业出版社, 2009：1-11.
[31]尚志远.检测声学原理及应用[M].西安：西北大学出版社,1996.
[32]王月雾,李萍,李喜孟.数字化超声回波时域信号拟合技术的初探[J].无损探伤,2003,27(1)：4-7.
[33]Parrilla M, et al. Digital Signal Processing Techniques for High Accuracy Ult rasonic Range Measurement s [C], IEEE,Transactions On Instrumentation and Measurement 1998,1056-1061.
[34]武良丹,贺西平,张小凤.超声回波参数的高斯与牛顿迭代法计算[J].陕西师范大学(自然科学版),2006,34(3)：56-58.
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