An Automated Algorithm for Constructing Maps of Defects in Active Thermal Testing

Opis bibliograficzny
Parent link:Russian Journal of Nondestructive Testing
Vol. 55, iss. 8.— 2019.— [P. 617-621]
1. autor: Chulkov A. O. Arseniy Olegovich
organizacja autorów: Национальный исследовательский Томский политехнический университет Инженерная школа неразрушающего контроля и безопасности Центр промышленной томографии Научно-производственная лаборатория "Бетатронная томография крупногабаритных объектов", Национальный исследовательский Томский политехнический университет Инженерная школа неразрушающего контроля и безопасности Центр промышленной томографии Научно-производственная лаборатория "Тепловой контроль"
Kolejni autorzy: Nesteruk D. A. Denis Alekseevich, Vavilov V. P. Vladimir Platonovich
Streszczenie:Title screen
The algorithm makes it possible to simplify the procedure for processing results of the thermal testing aimed at both revealing latent defects and evaluating their transverse dimensions and shape. Applying this algorithm requires certain participation and experience of the thermography operator, as well as preliminary preparation of initial data by using techniques that increase the signal-to-noise ratio. The algorithm includes selection of defective zones on the thermogram of the test object, automated identification of points with extreme signals, and a pixel-by-pixel threshold analysis of the zones adjacent to these points, culminating in the construction of binary defect maps.
Режим доступа: по договору с организацией-держателем ресурса
Wydane: 2019
Hasła przedmiotowe:
Dostęp online:https://doi.org/10.1134/S1061830919080035
Format: Elektroniczne Rozdział
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663950
Opis
Streszczenie:Title screen
The algorithm makes it possible to simplify the procedure for processing results of the thermal testing aimed at both revealing latent defects and evaluating their transverse dimensions and shape. Applying this algorithm requires certain participation and experience of the thermography operator, as well as preliminary preparation of initial data by using techniques that increase the signal-to-noise ratio. The algorithm includes selection of defective zones on the thermogram of the test object, automated identification of points with extreme signals, and a pixel-by-pixel threshold analysis of the zones adjacent to these points, culminating in the construction of binary defect maps.
Режим доступа: по договору с организацией-держателем ресурса
DOI:10.1134/S1061830919080035