摘要
针对超声检测回波信号中可能具有噪声干扰并难以剔除的问题 ,提出了利用“小波降噪”对超声信号进行处理的算法和应用“类别可分性判据”评价特征值的方法 ,并通过实验进行了验证 .首先将小波变换用于超声信号噪声处理 ,然后利用类别可分性判据对缺陷信号的特征选择进行评价 ,最后通过 RBF网络对获得的超声检波信号进行缺陷分类以验证这种方法的有效性 .实验结果表明 :小波降噪算法充分利用了超声回波信号的时域、频域信息 ,不仅降噪效果明显 ,而且缺陷定位准确 ;类别可分性判据对缺陷信号的特征提取也起了定量衡量尺度的作用 .
A noise eliminating method for ultrasonic signal with wavelet denoise was presented and sort separability criterion was used to solve the criterion problem. Firstly wavelet transform was used in denoising process of ultrasonic signal; then sort separability criterion was used to evaluate the characteristic choice of flaw signals; finally the characteristic values of flaws in demodulated signal were classified by RBF neural network. Results of the experiments show that due to making the best use of the information of time and frequency domain at the same time in ultrasonic echo signals, wavelet deniose algorithm not only decreases the noises obviously, but also locates flaws accurately. The sort separability criterion can also play the role of being a quantification measure on the characteristic extracting of flaw signals.
出处
《中国矿业大学学报》
EI
CAS
CSCD
北大核心
2001年第3期248-251,共4页
Journal of China University of Mining & Technology
基金
国家自然科学基金!资助项目 (5 9975 0 85 )
