一种基于小波包和PCA的超声缺陷识别方法

在超声检测中,对缺陷进行定性分析是无损检测与评价的关键内容。本研究提出一种对缺陷类型进行分类的检测方法,通过对不同类型的缺陷波信号进行特征量提取,实现对缺陷的类型识别。首先使用空气耦合超声检测系统采集无缺陷信号与3种不同类型的缺陷波信号,提取信号的时域无量纲参数和小波包能量系数组成多维特征向量;然后使用主成分分析法(Principal component analysis,PCA)对多维特征向量进行降维处理得到特征融合量;最后输入BP神经网络系统进行缺陷类型的分类,并与未经过PCA处理的测试结果进行对比分析。实验结果证明,经过PCA处理的测试结果准确率更高,测试时间更短。

An Ultrasonic Defect Identification Method Based on Wavelet Packet and PCA

In ultrasonic testing, qualitative analysis of defects is the key content of nondestructive testing and evaluation. In this paper, a detection method of defect classification is proposed, in which the recognition of the defect type is realized by extracting characteristic quantities of different defect wave signals. Firstly, the air-coupled ultrasonic detection system is used to collect defect-free signals and three different types of defect-wave signals, and the time-domain dimensionless parameters and wavelet packet energy coefficients of the signals are extracted to form multi-dimensional feature vectors. Then principal component analysis (PCA) is used to reduce the dimensionality of multi-dimensional feature vectors to obtain the feature fusion quantity. Finally, BP neural network system is input to classify the defect types, and compare with the test results without PCA processing. The experimental results show that the PCA treatment has higher accuracy and shorter test time.