Threedimensional defect inversion from magnetic flux leakage signals using iterative neural network
Threedimensional defect inversion from magnetic flux leakage signals using iterative neural network
 Author(s): Junjie Chen ; Songling Huang ; Wei Zhao
 DOI: 10.1049/ietsmt.2014.0173
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 Author(s): Junjie Chen ^{1} ; Songling Huang ^{1} ; Wei Zhao ^{1}


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Affiliations:
1:
State Key Laboratory of Power System & Department of Electrical Engineering, Tsinghua University, Beijing 100084, People's Republic of China

Affiliations:
1:
State Key Laboratory of Power System & Department of Electrical Engineering, Tsinghua University, Beijing 100084, People's Republic of China
 Source:
Volume 9, Issue 4,
July 2015,
p.
418 – 426
DOI: 10.1049/ietsmt.2014.0173 , Print ISSN 17518822, Online ISSN 17518830
Defect inversion is of special interest to magnetic flux leakage (MFL) inspection in industry. This study proposes an iterative neural network to reconstruct threedimensional defect profiles from threeaxial MFL signals in pipeline inspection. A radial basis function neural network is utilised as the forward model to predict the MFL signals given a defect profile, and the defect profile gets updated based on a combination of gradient descent and simulated annealing in the iterative inversion procedure. Accuracy of the proposed inversion procedure is demonstrated in estimating the profile of different defects in steel pipes. Experimental result based on threeaxial simulated MFL data also shows that the proposed inversion approach is robust even in presence of reasonable noise.
Inspec keywords: magnetic leakage; mechanical engineering computing; signal reconstruction; signal detection; simulated annealing; magnetic flux; radial basis function networks; gradient methods; inspection; nondestructive testing; pipelines; steel
Other keywords: pipeline inspection; radial basis function neural network; simulated annealing; gradient descent method; magnetic flux leakage; iterative neural network; defect profile reconstruction; steel pipes; iterative inversion procedure; forward model; three axial MFL signal detection; MFL inspection; 3D defect inversion
Subjects: Testing; Inspection and quality control; Mechanical engineering applications of IT; Signal detection; Civil and mechanical engineering computing; Optimisation; Optimisation techniques; Digital signal processing; Optimisation techniques; Neural computing techniques; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Inspection and quality control; Engineering materials; Materials testing; Numerical analysis
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