| Quantitative Interpretation for the Magnetic Flux Leakage Testing Data Based on Neural Network |
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| 作者姓名： | SONG Xiaochun~ |
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| 基金项目： | Funded by National Natural Science Foundation of China(50305017)
the Youth Chengguang Project of Science and Technology of Wuhan City of China(20045006071-27). |
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| 摘 要： | In order to interpret the magnetic flux leakage (MFL) testing data quantitatively and size the defects accurately, some defect profiles inversion methods from the MFL signals are studied on the basis of the neural network.Because the wavelet ba- sis function neural network (WBFNN) has good accuracy in the forward calculation and the radial basis function neural network (RBFNN) has reliable precision in the inversion modeling respectively,a new neural network scheme combining WBFNN and RBFNN is presented to solve the nonlinear inversion problem for the MFL data and reconstruct the defect shapes.And such details as the choice of wavelet basis function,the initialization of the weight value and the input normalization are analyzed,the train- ing and testing algorithm for the network are also studied.The inversion results demonstrate that the proposed network scheme has good reliability to interpret the MFL data for some defects.
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Quantitative Interpretation for the Magnetic Flux Leakage Testing Data Based on Neural Network |
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| SONG Xiaochun.Quantitative Interpretation for the Magnetic Flux Leakage Testing Data Based on Neural Network[J].Journal of Wuhan University of Technology,2006,28(Z2). |
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| Authors: | SONG Xiaochun HUANG Songling ZHAO Wei |
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| Affiliation: | 1. State Key Lab of Power Systems,Dept.of Electrical Engineering,Tsinghua University,Beijing 100084,China;School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China
2. State Key Lab of Power Systems,Dept.of Electrical Engineering,Tsinghua University,Beijing 100084,China |
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| Abstract: | In order to interpret the magnetic flux leakage (MFL) testing data quantitatively and size the defects accurately, some defect profiles inversion methods from the MFL signals are studied on the basis of the neural network.Because the wavelet ba- sis function neural network (WBFNN) has good accuracy in the forward calculation and the radial basis function neural network (RBFNN) has reliable precision in the inversion modeling respectively,a new neural network scheme combining WBFNN and RBFNN is presented to solve the nonlinear inversion problem for the MFL data and reconstruct the defect shapes.And such details as the choice of wavelet basis function,the initialization of the weight value and the input normalization are analyzed,the train- ing and testing algorithm for the network are also studied.The inversion results demonstrate that the proposed network scheme has good reliability to interpret the MFL data for some defects. |
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| Keywords: | neural networks magnetic flux leakage(MFL) quantitative interpretation nondestructive testing |
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