摘要
漏磁检测是铁磁材料常用的无损检测方法,检测的重点是根据测量的漏磁信号重构缺陷轮廓。提出了分别利用径向基神经网络和广义回归神经网络,建立由缺陷漏磁信号到缺陷深度的非线性映射。训练样本为三维有限元仿真数据,而测试样本为经过平滑滤波、小波消噪预处理,后经过径向基神经网络精确插值的漏磁检测数据。用训练数据对这两种神经网络分别进行逼近训练,并用训练好的神经网络反演测试数据,重构缺陷。试验结果表明,两种神经网络都能够实现三维缺陷漏磁检测的成像化及可视化,其中广义回归神经网络要优于径向基神经网络。
Magnetic flux leakage（MFL） testing is one of the most commonly used methods for nondestructive testing（NDT） of ferromagnetic materials.The key element is to reconstruct the defect profile based on the measured MFL signals.Both RBFNN（radial basis function neural network） and GRNN（generalized regression neural network）were proposed,by means of which two different kinds of nonlinear mapping between defect MFL signals and defect depths were respectively established.The training data samples were from the simulated data sets for 3-D finite element models while the testing data samples were from MFL testing data which were precisely interpolated by RBFNN after smooth filtering and wavelet denoising preprocessing.These two neural networks were first respectively trained to approximate the matrix of defect depth with the training data samples.Then each of them was applied to reconstruct defect with the testing data samples.The testing results demonstrated that the two neural networks could achieve 3-D imaging and visualization of defects in MFL testing,and especially GRNN was superior to RBFNN on defect reconstruction.
出处
《无损检测》
2011年第3期9-13,共5页
Nondestructive Testing
基金
陕西省"13115"科技创新工程重大科技专项项目资助(2007ZDKG-36)
西北大学研究生创新基金资助项目(08YSY03)
关键词
漏磁检测
径向基神经网络
广义回归神经网络
缺陷重构
电磁场有限元仿真
Magnetic flux leakage testing
Radial basis function neural network
Generalized regression neural network
Defect reconstruction
Electromagnetic field finite element simulation
