Reconstruction of arbitrary defect profiles from three-axial MFL signals based on metaheuristic optimization method

This paper explores the property of three-axial magnetic flux leakage (MFL) signals in pipeline inspection. Then, metaheuristic optimization methods, including genetic algorithm (GA) and tabu search (TS) algorithm, are utilized to recon- struct defect profiles from three-axial MFL signals. Performances of the two methods are testified and compared, and a series of improving methods are proposed to minimize the time consumption while maintaining the accuracy of defect reconstruction. Experiments of defect reconstruction demonstrate that the proposed inversion methods have high performance in terms of both accuracy and robustness.

[1]  M. Nabi,et al.  Improved FEM model for defect-shape construction from MFL signal by using genetic algorithm , 2007 .

[2]  M. Kreutzbruck,et al.  Fast defect parameter estimation based on magnetic flux leakage measurements with GMR sensors , 2011 .

[3]  Gui Yun Tian,et al.  Numerical simulations on electromagnetic NDT at high speed , 2006 .

[4]  C. Magele,et al.  Fast Magnetic Flux Leakage Signal Inversion for the Reconstruction of Arbitrary Defect Profiles in Steel Using Finite Elements , 2013, IEEE Transactions on Magnetics.

[5]  Lalita Udpa,et al.  Neural network-based inversion algorithms in magnetic flux leakage nondestructive evaluation , 2003 .

[6]  Ameet V. Joshi.  Wavelet transform and neural network based 3D defect characterization using magnetic flux leakage , 2008 .

[7]  J.R. Hare,et al.  Characterization of Surface-Breaking Cracks Using One Tangential Component of Magnetic Leakage Field Measurements , 2008, IEEE Transactions on Magnetics.

[8]  Lalita Udpa,et al.  Electromagnetic NDE signal inversion by function-approximation neural networks , 2002 .

[9]  Wei Zhao,et al.  Three-dimensional defect reconstruction from magnetic flux leakage signals in pipeline inspection based on a dynamic taboo search procedure , 2014 .

[10]  N.K. Nikolova,et al.  Machine Learning Techniques for the Analysis of Magnetic Flux Leakage Images in Pipeline Inspection , 2009, IEEE Transactions on Magnetics.

[11]  J. Reilly,et al.  Sizing of 3-D Arbitrary Defects Using Magnetic Flux Leakage Measurements , 2010, IEEE Transactions on Magnetics.

[12]  S. Koziel,et al.  A Space Mapping Methodology for Defect Characterization From Magnetic Flux Leakage Measurements , 2008, IEEE Transactions on Magnetics.

[13]  Gerhard Kopp,et al.  Sizing limits of metal loss anomalies using tri-axial MFL measurements: A model study , 2013 .

[14]  R. C. Ireland,et al.  Finite element modelling of a circumferential magnetiser , 2006 .

[15]  Lalita Udpa,et al.  Use of higher order statistics for enhancing magnetic flux leakage pipeline inspection data , 2007 .

[16]  Satoru Kobayashi,et al.  Feasibility study of magnetic flux leakage method for condition monitoring of wall thinning on tube , 2010 .

[17]  Songling Huang,et al.  Reconstruction of 3-D defect profiles from MFL signals using radial wavelet basis function neural network , 2014 .

[18]  Gui Yun Tian,et al.  3D magnetic field sensing for magnetic flux leakage defect characterisation , 2006 .