Diverse Time-Frequency Distributions Integrated to an ART2 Network for Non-Destructive Testing

 

Benítez–Pérez H.,¹ y Medina–Gómez L.²

 

¹ Departamento de Ingeniería de Sistemas Computacionales y Automatización, IIMAS, UNAM, Apdo. Postal 20-726, Del. A. Obregón, México D. F., CP. 01000, México. Email: (*) hector@uxdea4.iimas.unam.mx

² Departamento de Física, Facultad de Ciencias, UNAM, Apdo. Postal 20-726 Fax: ++52 55 5616 01 76, Tel: (*) ++52 55 5622 36 23

 

ABSTRACT

The use of several techniques for non-destructive testing is a common strategy for detecting and classifying flaws in aluminium material. Techniques like multiresolution data analysis and data classifiers are valuable for obtaining as much information as possible from the flaws. The combination of both techniques allows the clear definition of several characteristics like localization, size and form. In this study, localization using Time-Frequency Distribution feature extraction and an ART2 neural network as a classifier is the main goal.

Keywords: Non-destructive Testing, Neural Networks, Time Frequency Distribution Approach.

 

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8. ACKNOWLEDGMENTS

The authors thank the financial support of PAPIIT-UNAM (IN106100 and IN105303), Mexico in connection with this work. Furthermore, the authors gratefully acknowledge fruitful discussions with Dr. Arturo Juarez from CIATEQ, Mexico.

 

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