利用小波变换和RBF(Radius Basis Function)神经网络技术对漏磁检测系统中的缺陷信号进行分类。重点设计了试验系统，采集了四种缺陷信号，首先应用小波变换提取信号特征值，然后利用RBF神经网络训练，采用模糊聚类算法寻找基函数的中心，使缺陷的定性分类获得了很高的准确率。试验获得了较好的缺陷分类效果。

According to the non-stationary characteristics of pulse echo signals of flaw in magnetic flux leakage testing system, a method of flaw classification based on the wavelet transform and radius basis function(RBF) neural network was presented. An experiment system was designed to test the method, at first, the feature of flaw was extracted with wavelet transform, then the signal features were classified with RBF neural network, and the fuzzy clustering algorithm was used to find the center of basis function. Experiments showed that the result of recognition was satisfactory and high accuracy of flaw classification could be obtained.