Magnetic flux leakage detection in non destructive tests performed on ferromagnetic pieces, using signal processing techniques and data mining

Aldair Barajas Aldana, Jaime Parra-Raad, Carlos Julio Arizmendi

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Resumen

In this paper we propose the use of Support Vector Machine classifiers (SVM) and linear discriminant analysis (LDA) to determine the existence of magnetic flux leakage (MFL) in non-destructive testing (NDT for its acronym in English) performed on ferromagnetic sheets. These signals were provided by the Corporation for Research in Corrosion (CIC) and were acquired on a dyno. The signals are preprocessed to; filter data (ie Wavelet Transform), remove the existing noise (ie thresholding), baseline correction (ie Least Squares Theorem (LST)) and normalize the data (ie First Normal Form). Within the aims of the project are design suitable classifier for each technical proposed for this phenomenon, and a comparison between them to determine which had the best performance.

Idioma originalInglés
Título de la publicación alojada2014 3rd International Congress of Engineering Mechatronics and Automation, CIIMA 2014 - Conference Proceedings
EditoresAndres G. Marrugo
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781479979325
DOI
EstadoPublicada - 11 dic. 2014
Evento2014 3rd International Congress of Engineering Mechatronics and Automation, CIIMA 2014 - Cartagena, Colombia
Duración: 22 oct. 201424 oct. 2014

Serie de la publicación

Nombre2014 3rd International Congress of Engineering Mechatronics and Automation, CIIMA 2014 - Conference Proceedings

Conferencia

Conferencia2014 3rd International Congress of Engineering Mechatronics and Automation, CIIMA 2014
País/TerritorioColombia
CiudadCartagena
Período22/10/1424/10/14

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