NDLI: Three-dimensional defect inversion from magnetic flux leakage signals using iterative neural network

Content Provider	IET Digital Library
Author	Chen, Junjie Huang, Songling Zhao, Wei
Abstract	Defect inversion is of special interest to magnetic flux leakage (MFL) inspection in industry. This study proposes an iterative neural network to reconstruct three-dimensional defect profiles from three-axial 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 three-axial simulated MFL data also shows that the proposed inversion approach is robust even in presence of reasonable noise.
Starting Page	418
Ending Page	426
Page Count	9
ISSN	17518822
Volume Number	9
e-ISSN	17518830
Issue Number	Issue 4, Jul (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/9/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2014.0173
Journal	IET Science, Measurement & Technology
Publisher Date	2015-02-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Defect Inversion Civil And Mechanical Engineering Computing Defect Profile Reconstruction Digital Signal Processing Engineering Material Forward Model Gradient Descent Method Gradient Method Inspection Inspection And Quality Control Interpolation And Function Approximation Iterative Inversion Procedure Iterative Neural Network Magnetic Flux Magnetic Flux Leakage Magnetic Leakage Material Testing Mechanical Engineering Application of IT Mechanical Engineering Computing MFL Inspection Neural Computing Technique Nondestructive Testing Numerical Analysis Optimisation Optimisation Technique PipeLine Pipeline Inspection Radial Basis Function Network Radial Basis Function Neural Network Signal Detection Signal Reconstruction Simulated Annealing Steel Steel Pipes Testing Three Axial MFL Signal Detection
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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