为了有效识别不同类型的超声缺陷信号，提出了一种基于小波包分解和主成分分析（Principal Component Analysis，PCA）的信号特征提取方法。首先，提取缺陷信号小波包分解后的能量系数组成多维特征向量集；然后，使用PCA方法对多维特征向量进行降维得到融合特征量；最后，输入BP神经网络对不同类型的缺陷信号进行分类测试，并与未经PCA处理的特征量分类测试结果进行对比。试验结果证明，该特征量提取的方法能够有效地对缺陷进行分类，且测试速度明显得到提高。

In order to effectively identify different types of ultrasonic defect signals, a method based on wavelet packet decomposition and principal component analysis was proposed to extract the characteristic quantities. Firstly, the multi-dimensional eigenvector set is composed of the energy coefficients of wavelet packet decomposition. Then the multidimensional feature vectors are reduced by PCA method to get the fusion feature quantity. Finally, BP neural network was used to classify and test different types of defect signals, and the obtained results were compared with the classification test results of characteristic quantities without PCA processing. The experimental results show that the feature extraction method can classify the defect types effectively, and the testing speed is obviously improved.