【摘要】 由于粗晶材料中材料微结构引起的散射（结构噪声），使得超声探伤比较困难。时延神经网络（TDNN）已在多种场合成功地用于处理时间序列数据，因此我们有可能采用这一技术来提高超声检测的信噪比。本文提出了一种新的TDNN结构用于降低粗晶材料结构噪声，该结构具有波形及其相位差组成的双变量输入。实验结果显示，这种TDNN结构能在较强的背景噪声下提取超声探伤的缺陷回波。更多还原

【Abstract】 Abstract: Ultrasonic flaw detection in large-grained materials is difficult because of multiple scattering from material microstructure (grain noise). The Time Delay Neural Network (TDNN) has been used quite successfUlly to process time series data. Therefore, it is possible to achieve SNR enhancement in ultrasonic testing by using this technique. In this paper, a new TDNN architecture with two input variable,viz. wave form and its phase difference, is developed to reduce the grain noise. Experimental results demonstrate the ability of the TDNN architecture to reveal flaw echoes from high level of background noise.更多还原