基于PSO-LS-SVM的漏磁信号二维轮廓重构

漏磁检测技术被广泛应用于铁磁材料的无损评估中，用漏磁信号描述缺陷的几何特征一直是漏磁检测的难点。提出应用最小二乘支持向量机对缺陷轮廓重构的方法，并利用粒子群算法来优化LS-SVM的参数及核函数参数。支持向量机输入是漏磁信号，输出是缺陷轮廓数据，建立了由缺陷的漏磁信号到缺陷二维轮廓的映射关系。训练样本由试验数据与仿真数据组成，测试样本为人工裂纹缺陷。该方法实现了人工裂纹缺陷的二维轮廓的重构，并与BP神经网络、GA-LS-SVM两种方法进行了比较。试验结果表明，该方法具有速度快、精度高和很好的泛化能力，为漏磁检测定量化提供了一种可行的方法。

2-D Pipeline Defect Reconstruction from Magnetic Flux Leakage Signals Based on LS-SVM

Nondestructive evaluation of ferromagnetic material is most commonly performed by magnetic flux leakage(MFL) techniques,and the key element is to describe the characters of defects from MFL inspection signals. A novel method for the reconstitution of 2-D profiles is presented based on least squares support vector machines (LS-SVM) technique,and particle swarm optimization(PSO) is adopted to optimize the model parameter of LSSVM. The input data sets of SVM is MFL signals and output data sets is 2-D profiles ...