Equivalent MFL model of pipelines for 3-D defect reconstruction using simulated annealing inversion procedure

In this paper, we propose an equivalent forward model of magnetic flux leakage (MFL) inspection in pipelines to reduce the computation effort to predict MFL signals. Then an iterative simulated annealing inversion procedure is developed to reconstruct the three-dimensional (3-D) defect profile from MFL signals. Experiments results, based on both simulated and measured MFL signals, demonstrate that the proposed inversion procedure using the equivalent forward model has high performance in terms of execution speed and accuracy.

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

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

[3]  Noritaka Yusa,et al.  Some advances in numerical analysis techniques for quantitative electromagnetic nondestructive evaluation , 2009 .

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

[5]  Wei Zhao,et al.  The effect of the defect location on the finite element modelling of defect MFL fields , 2006 .

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

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

[8]  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.

[9]  Changlong Wang,et al.  LS-SVMs-based reconstruction of 3-D defect profile from magnetic flux leakage signals , 2007 .

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

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