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| A wide adaptability deconvolution technique for ultrasonic nondestructive testing |
| YANG Ke-ji1, FANG Wen-ping1, HUANG Yi-chun2, QIAO Hua-wei1 |
(1. State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China;
2. Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China) |
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Abstract A new wide adaptability deconvolution technique based on wavelet transform and particle swarm optimization (PSO) was developed to increase the resolution of ultrasonic nondestructive testing (UNDT) of composite materials. The original ultrasonic echo signal was de-noised and the position set of ultrasonic reflection coefficient was determined by using wavelet transform multi-resolution analysis. Then PSO was adopted to solve the ultrasonic reflection coefficient amplitudes corresponding to the position set, so as to eliminate the distorted wavelets smoothness effect and improve the detection resolution. Mean while, limitations of the traditional methods, such as only suitable for stationary ultrasonic signal, white noise and prior knowledge provided beforehand, were broken through by the new technique. Simulation and experimental results showed that the new method could greatly improve the ultrasonic testing resolution compared with the conventional deconvolution technique, and its wide adaptability as well as strong robustness was demonstrated．
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Published: 29 November 2009
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一种应用于超声无损检测的广谱反卷积技术研究
为了提高复合材料超声无损检测（UNDT）分辨率,提出一种基于小波变换和粒子群算法（PSO）的广谱反卷积新技术.在利用小波变换多分辨率分析能力对超声反射回波信号消噪,并确定超声反射系数位置集的基础上,采用粒子群优化算法求出相应位置反射系数的幅值,从而消除畸变小波的平滑作用,有效改善检测分辨率.同时,该技术还突破传统方法仅适合于超声回波信号为平稳、检测噪声为白色以及先验知识已知的场合应用的局限性.计算机仿真和实验研究表明,与传统反卷积技术相比,该方法能极大地提高超声检测的分辨率,并体现出较强的广谱适应性和鲁棒性.
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| ［1］ 刘福顺,汤明. 无损检测基础［M］. 北京: 北京航空航天大学出版社,2002．
［2］ 杨克己.基于神经网络的检测声学信号处理理论与实践［D］.杭州:浙江大学,1997．
YANG Ke-ji. Theory and application of testing acoustics signal processing based on artificial neural network［D］. Hangzhou: Zhejiang University,1997．
［3］ CHEN C H, SIN S K. On effective spectrum-based ultrasonic deconvolution techniques for hidden flaw characterization［J］. Acoustical Society of America, 1990, 87(3): 976987．
［4］ JHANG K, JANG H, PARK B, et al. Wavelet analysis based deconvolution to improve the resolution of scanning acoustic microscope images for the inspection of thin die layer in semiconductor［J］. NDT and E International Volume, 2002, 35(8): 549557．
［5］ KENNEDY J, EBERHART R. Particle swarm optimization［C］∥ Proceedings of IEEE International Conference on Neural Networks. Australia: IEEE, 1995: 19421948．
［6］ 乔华伟.检测声学信号智能处理技术的研究［D］.杭州：浙江大学,2008．
Qiao Hua-wei. Study on the intelligent processing technologies of testing acoustics signal［D］. Hangzhou: Zhejiang University, 2008．
［7］ ANGRISANI L , DAPONTE P, DAPUZZO M. The detection of echoes from multilayer structures using the wavelet transform［J］. Instrumentation and Measurement, 2000, 49(4): 727731．
［8］ DONOHO D L. De-Noising by soft-thresholding ［J］. IEEE Transactions on Imformation Technology, 1995, 41(3)：612627．
［9］ 杨克己.基于神经网络的小波域超声信号消噪技术研究［J］.浙江大学学报：工学版,2005,39(6):775779．
YANG Ke-ji. Study on denoising techiniques for ultrasonic signals in wavelet domain based on neural networks［J］. Journal of Zhejiang University：Engineering Science, 2005, 39(6): 775779．
［10］ TOMAS O, TADEUSZ S. Minimum entropy deconvolution of pulse-echo signals acquired from attenuative layered media［J］. Acoustical Society of America, 2001, 109(6): 28312839． |
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