铝合金锻件超声信号的自适应小波压缩方法

针对铝合金锻件超声信号中含有晶粒散射引起的相干噪声,建立了相应的缺陷回波检测数学模型.提出了基于新的阈值函数Stein无偏估计自适应压缩方法.对离散小波变换各尺度上的小波系数用新的阈值函数进行估值后,进行了小波阈值最小均方误差意义上的迭代,并用小波系数估值进行离散小波反变换.以得到信号的估值,通过反复迭代,得到缺陷回波的最优压缩模型.对含缺陷铝合金锻件的超声信号处理实验的结果表明,与常用的固定阈值方法相比,在相同压缩比下,自适应压缩方法能更好地去除散射噪声及识别缺陷回波信号.

Ultrasonic Signal Compression Based on Adaptive Wavelet Thresholding

Aimed at ultrasonic signal compression and detection of the flaw echo of aluminum alloy forge in the presence of high scattering microstructure noise,the adaptive stein unbiased risk estimation(SURE)compression method based on the new thresholding function was presented,and the flaw model under ultrasonic signal has been built.The new estimated wavelet coefficients were deduced by using the new thresholding function,as the original wavelet coefficients decomposed from signal inputted.The new estimated signal was rebuilt by using the new estimated wavelet coefficients.An iterative process of the thresholds,which depend on the minimum mean square error(MMSE),was implemented to decide the termination of signal rebuilt.The experiment results indicated that the adaptive compression method has better de-noising and compressing performance and improved the SNR ability for flaw echo detection.