摘要
为了准确确定混凝土缺陷的类型、范围及大小,利用小波分析方法,将采集的超声波信号进行小波包分解,分别提取各个频率成分的信号特征,并对小波包进行分解系数重构,求出各频带信号的能量与信号总能量的比值。基于此构造的特征向量作为神经网络输入向量,再由其对信号进行缺陷的识别判断。试验表明,该方法不但对识别缺陷位置和范围效果较好,而且对识别缺陷类型也有较高精度。
In order to determine the type, range and size of concrete defects, the paper gave description of wavelet packets analysis of received ultrasonic waves, by finding the character of different frequency parts and reconstructing the decomposing coefficients to get the energy proportion of different frequency parts. Based on this, a characteristic vector was constructed, which was the input character of neural network. After being trained, the vector was used to identify and judge the defect. The results showed that this method was akle to give a highly accurate measurement not only for the range and size of defects, but also for the type of defects.
出处
《无损检测》
2009年第1期20-22,共3页
Nondestructive Testing
