Denoising techniques for ultrasonic signals in wavelet domain based on support vector machine

被引：0
作者：
Institute of Modern Manufacture Engineering, Zhejiang University, Hangzhou 310027, China [1 ]
机构：
[1] Institute of Modern Manufacture Engineering, Zhejiang University
来源：
Jixie Gongcheng Xuebao | 2008年 / 6卷 / 66-71期
关键词：
Signal to noise ratio (SNR); Support vector machine (SVM); Ultrasonic nondestructive testing (UNDT); Wavelet transform;
D O I：
10.3901/jme.2008.06.066
中图分类号：
学科分类号：
摘要：
In order to enhance the signal to noise ratio (SNR) of fundamental ultrasonic echo signals for ultrasonic nondestructive testing (UNDT) and ultrasonic nondestructive evaluation (UNDE), an improved technique to suppress structural noises of ultrasonic signals on the basis of pattern recognition theory of support vector machine is presented. After the formation mechanism of structural noises is studied and the shortcomings of classical split spectrum processing (SSP) algorithm are analyzed, the fundamental ultrasonic signals are decomposed into wavelet domain by discrete wavelet transform. A signal and noise separator based on support vector machine (SVM) of which the kernel function is Gauss function is used to distinguish the target signals from the noises in wavelet domain, and the target signals are reconstructed to realize the aim of enhancing SNR by removing noises. The experimental results indicate that the presented technique has high performance reliability and can improve the SNR enhancing ability for ultrasonic target echo signals contaminated by structural noises compared with the classical SSP algorithm.
引用
收藏
页码：66 / 71
页数：5
相关论文
共 12 条
[1]  
Yang K., Theory and application of testing acoustics signal processing based on artificial neural network, (1997)
[2]  
Shankar P.M., Bencharit U., Bilgutay N.M., Et al., Grain noise suppression through bandpass filtering, Materials Evaluation, 46, 7, pp. 1100-1104, (1988)
[3]  
Chen J., Shi Y., Shi S., Noise analysis of digital ultrasonic nondestructive evaluation system, The International Journal of Pressure Vessels and Piping, 76, 9, pp. 619-630, (1999)
[4]  
Yang K., An adaptive split spectrum processing technique based on neural networks, China Mechanical Engineering, 13, 9, pp. 806-809, (2002)
[5]  
Gustafsson, Mats G., Stepinski T., Studies of split spectrum processing, optimal detection, and maximum likelihood amplitude estimation using a simple clutter model, Ultrasonics, 35, 1, pp. 31-52, (1997)
[6]  
Drai R., Benammar A., Benchaala A., Signal processing for the detection of multiple imperfection echoes drowned in the structural noise, Ultrasonics, 42, 9, pp. 831-835, (2004)
[7]  
Newhause V.L., Bilguaty N.M., Flaw-to-grain echo enhancement by split-spectrum processing, Ultrasonics, 20, 2, pp. 59-68, (1982)
[8]  
David L., Donoho, De-Noising by soft-thresholding, IEEE Transactions On Information Technology, 41, 3, pp. 612-627, (1995)
[9]  
Vapnik V., The Nature of Statistical Theory, (1995)
[10]  
Qi H., Support vector machines and application research overview, Computer Engineering, 30, 10, pp. 6-9, (2004)
12