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Evaluation of the Size of a Defect in Reinforcing Steel Using Magnetic Flux Leakage (MFL) Measurements

This study aimed to evaluate 2D magnetic flux leakage (MFL) signals (B(x), B(y)) in D19-size reinforcing steel with several defect conditions. The magnetic flux leakage data were collected from the defected and new specimens using an economically designed test setup incorporating permanent magnets....

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Detalles Bibliográficos
Autores principales: Yousaf, Jamal, Harseno, Regidestyoko Wasistha, Kee, Seong-Hoon, Yee, Jurng-Jae
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303955/
https://www.ncbi.nlm.nih.gov/pubmed/37420540
http://dx.doi.org/10.3390/s23125374
Descripción
Sumario:This study aimed to evaluate 2D magnetic flux leakage (MFL) signals (B(x), B(y)) in D19-size reinforcing steel with several defect conditions. The magnetic flux leakage data were collected from the defected and new specimens using an economically designed test setup incorporating permanent magnets. A two-dimensional finite element model was numerically simulated using COMSOL Multiphysics to validate the experimental tests. Based on the MFL signals (B(x), B(y)), this study also intended to improve the ability to analyze defect features such as width, depth, and area. Both the numerical and experimental results indicated a high cross-correlation with a median coefficient of 0.920 and a mean coefficient of 0.860. Using signal information to evaluate defect width, the x-component (B(x)) bandwidth was found to increase with increasing defect width and the y-component (B(y)) amplitude rise with increasing depth. In this two-dimensional MFL signal study, both parameters of the two-dimensional defects (width and depth) affected each other and could not be evaluated individually. The defect area was estimated from the overall variation in the signal amplitude of the magnetic flux leakage signals with the x-component (B(x)). The defect areas showed a higher regression coefficient (R(2) = 0.9079) for the x-component (B(x)) amplitude from the 3-axis sensor signal. It was determined that defect features are positively correlated with sensor signals.