Defect Reconstruction from MFL Signals Using An Improved Genetic Local Search Algorithm

Abstract

This paper presents an improved GLSA (IGLSA) by incorporating the simulated annealing technique into the perturbation process of the genetic local search (GLSA), and proposes an IGLSA-based inverse algorithm for 2-D defect reconstruction from the magnetic flux leakage (MFL) signals. In the algorithm, radial-basis function neural network (RBFNN) is utilized as forward model, and the IGLSA is used to solve the optimization problem in the inverse problem. Experiments are presented to show the performance of the IGLSA-based inverse algorithm and to compare it with the canonical-genetic-algorithm based (CGA-based) inverse algorithm and the GLSA-based inverse algorithm, respectively. The results demonstrate that IGLSA-based inverse algorithm is more accurate and is robust to the noise.

Finite Element Analysis and Research on Corrosion Defect of Tank Floor

“…Defect depth changes respectively tank floor thickness (mm) of 20%, 40%, 60%, while ensuring the cylinder diameter is 10mm unchanged, through the finite element analysis, lists the forms of defects of different depth calculation results, are defects in different depth of the magnetic flux density distribution nephogram, magnetic flux density of the vertical component and the magnetic flux density of the horizontal component of the three magnetic flux leakage characteristic parameters were compared [6]. What were shown in the Figure 4 and Figure 5 are the horizontal component and the amplitude of the leakage magnetic field.…”