基于Gauss调制脉冲模型的超声信号提取算法
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摘要
奥氏体材料的厚壁焊缝是焊缝超声检测的难点,由于焊缝组织晶粒粗大,超声散射与衰减严重,检测信号中含有大量噪声信号,因此需要通过信号处理提高其信噪比。该文提出了一种基于Gauss调制脉冲(GMP)模型的超声检测信号提取算法,使用参考信号对原始信号进行互相关滤波,并基于GMP模型对滤波结果进行信号分解,由信号特征直接估计各信号分量的控制参数。先后设计了仿真实验及超声检测实验以验证算法性能,结果表明:该算法可以有效提取超声检测信号中的缺陷信息,为厚壁焊缝缺陷信号分析、焊缝检测算法优化等相关应用奠定基础。
Austenitic thick-wall welds are difficult to evaluate using ultrasonic testing because the ultrasonic waves are scattered and attenuated by the coarse grain microstructure.The ultrasonic signals then contain much noise with signal processing needed to reduce the noise.This paper presents an ultrasonic signal extraction algorithm based on the Gaussian modulated pulse(GMP)model to improve the signal processing. The original signals are filtered by a cross-correlation method and then the filtered signals are decomposed to acquire the control parameters for each component based on the GMP model and the signal features.Simulations and experiments verify the algorithms performance.This algorithm can extract useful information about defects from ultrasonic signals as the foundation for further analyses of thick-wall welds for defect information analyses and inspection method optimization.
Austenitic thick-wall welds are difficult to evaluate using ultrasonic testing because the ultrasonic waves are scattered and attenuated by the coarse grain microstructure.The ultrasonic signals then contain much noise with signal processing needed to reduce the noise.This paper presents an ultrasonic signal extraction algorithm based on the Gaussian modulated pulse(GMP)model to improve the signal processing. The original signals are filtered by a cross-correlation method and then the filtered signals are decomposed to acquire the control parameters for each component based on the GMP model and the signal features.Simulations and experiments verify the algorithms performance.This algorithm can extract useful information about defects from ultrasonic signals as the foundation for further analyses of thick-wall welds for defect information analyses and inspection method optimization.
引文
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[2]高天,陈怀玉,曹丽.一种基于超声缺陷回波分析的小波降噪方法[J].仪器仪表学报,2015,36Supplement:73-79.GAO T,CHEN H Y,CAO L.A wavelet denoising method based on ultrasonic defect echo analysis[J].Chinese Journal of Scientific Instrument,2015,36 Supplement:73-79.(in Chinese)
[3]陶亮.Gabor展开与变换研究综述[J].安徽大学学报(自然科学版),2017,41(2):2-13.TAO L.Review on Gabor expansion and transform[J].Journal of Anhui University(Natural Science Edition),2017,41(2):2-13.(in Chinese)
[4]王柄方.厚壁焊缝超声检测方法研究[D].北京:清华大学,2010.WANG B F. Study on ultrasonic testing method for thick-wall welds[D].Beijing:Tsinghua University,2010.(in Chinese)
[5] HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[6] WU Z, HUANG N E. Ensemble empirical mode decomposition:A noise-assisted data analysis method[J].Advances inAdaptiveDataAnalysis, Theoryand Applications,2009,1(1):1-41.
[7] MALLAT S G,ZHANG Z F. Matching pursuits with time-frequency dictionaries[J].IEEE Transactions on Signal Processing,1993,41(12):3397-3415.
[8]周健鹏.稀疏分解理论在超声无损检测信号处理中的应用[D].鞍山:辽宁科技大学,2016.ZHOU J P.Application of sparse decomposition theory to ultrasonic nondestructive testing signal processing[D].Anshan:University of Science and Technology Liaoning,2016.(in Chinese)
[9]李迎雪.基于稀疏表示的管道缺陷超声复合阵列成像研究[D].镇江:江苏大学,2017.LI Y X.Research on sparse-representation based ultrasonic composite array imaging for pipeline defect[D].Zhenjiang:Jiangsu University,2017.(in Chinese)
[10]卢振坤,杨萃,王金炜.基于Gabor变换的超声回波信号时频估计[J].电子与信息学报,2013,35(3):652-657.LU Z K,YANG C,WANG J W.Gabor transform based time-frequency estimation of ultrasonic echo signal[J].Journal of Electronics&Information Technology,2013,35(3):652-657.(in Chinese)
[11]MANN S,HAYKIN S.The Chirplet transform:Physical considerations[J].IEEE Transactions on Signal Processing,1995,43(11):2745-2761.
[12]郭剑峰,刘金朝,王卫东.基于变参数域和短时高斯线性调频基的自适应信号分解算法[J].振动与冲击,2015,34(12):133-139.GUO J F,LIU J C,WANG W D.Variable parameters domain and short time adaptive Gaussian chirplet signal decomposition algorithm[J].Journal of vibration and shock,2015,34(12):133-139.(in Chinese)
[13]ZHOU H P,HAN Z D,DU D,et al.A combined marching and minimizing ray-tracing algorithm developed for ultrasonic array imaging of austenitic welds[J]. NDT&E International,2018,95:45-56.