Optimizing input data for training an artificial neural network used for evaluating defect depth in infrared thermographic nondestructive testing

Optimizing input data for training an artificial neural network used for evaluating defect depth in infrared thermographic nondestructive testing

Dades bibliogràfiques
Parent link:Infrared Physics and Technology
Vol. 102.— 2019.— [103047, 7 p.]
Autor corporatiu: 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"
Altres autors: Chulkov A. O. Arseniy Olegovich, Nesteruk D. A. Denis Alekseevich, Vavilov V. P. Vladimir Platonovich, Moskovchenko A. I. Aleksey Igorevich, Saeed N. Numan, Omar M. A.
Sumari:Title screen
Ten different sets of input data have been used for training and verification of the neural network intended for determining defect depth in infrared thermographic nondestructive testing. The input data sets included raw temperature data, polynomial fitting, principle component analysis, Fourier transform and others. A minimum error (up 0.02 mm for defects in CFRP at depths from 0.5 to 2.5 mm) has been achieved by using polynomial fitting in logarithmic coordinates with further computation of the first temperature derivatives (the TSR technique), and close results have been obtained by processing raw data with the PCA technique. Both techniques require no use of reference points.
Режим доступа: по договору с организацией-держателем ресурса
Publicat: 2019
Matèries:
электронный ресурс
труды учёных ТПУ
infrared thermographic testing
neural network
data processing
defect depth
composite material
инфракрасный контроль
нейронные сети
обработка данных
глубина
дефекты
композитные материалы
Accés en línia:https://doi.org/10.1016/j.infrared.2020.103289
Format: Electrònic Capítol de llibre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663088
Descripció
Sumari:Title screen
Ten different sets of input data have been used for training and verification of the neural network intended for determining defect depth in infrared thermographic nondestructive testing. The input data sets included raw temperature data, polynomial fitting, principle component analysis, Fourier transform and others. A minimum error (up 0.02 mm for defects in CFRP at depths from 0.5 to 2.5 mm) has been achieved by using polynomial fitting in logarithmic coordinates with further computation of the first temperature derivatives (the TSR technique), and close results have been obtained by processing raw data with the PCA technique. Both techniques require no use of reference points.
Режим доступа: по договору с организацией-держателем ресурса
DOI:10.1016/j.infrared.2020.103289

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