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Titolo:
Defect detection and classification using a SQUID based multiple frequencyeddy current NDE system
Autore:
von Kreutzbruck, M; Allweins, K; Ruhl, T; Muck, M; Heiden, C; Krause, HJ; Hohmann, R;
Indirizzi:
Univ Giessen, Inst Angew Phys, D-35392 Giessen, Germany Univ Giessen Giessen Germany D-35392 ngew Phys, D-35392 Giessen, Germany KFA Julich GmbH, Forschungszentrum, Inst Schicht & Ionentech, D-52425 Julich, Germany KFA Julich GmbH Julich Germany D-52425 onentech, D-52425 Julich, Germany
Titolo Testata:
IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY
fascicolo: 1, volume: 11, anno: 2001,
parte:, 1
pagine: 1032 - 1037
SICI:
1051-8223(200103)11:1<1032:DDACUA>2.0.ZU;2-F
Fonte:
ISI
Lingua:
ENG
Keywords:
feature extraction; nondestructive testing; SQUIDs;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
15
Recensione:
Indirizzi per estratti:
Indirizzo: von Kreutzbruck, M Univ Giessen, Inst Angew Phys, D-35392 Giessen, GermanyUniv Giessen Giessen Germany D-35392 Giessen, Germany
Citazione:
M. von Kreutzbruck et al., "Defect detection and classification using a SQUID based multiple frequencyeddy current NDE system", IEEE APPL S, 11(1), 2001, pp. 1032-1037

Abstract

The probability of detection (POD) of hidden fatigue defects in riveted multilayer joints, e.g. aircraft fuselage, can be improved by using sophisticated eddy-current systems which provide more information than conventional NDE equipment. In order to collect this information, sensor arrays or multi-frequency excitation schemes can be used. We have performed simulations and measurements with an eddy current NDE system based on a SQUID magnetometer. To distinguish between signals caused by material defects and those caused by structures in the sample, such as bolts or rivets, a high signal-to-noise ratio is required. Our system provides a large analog dynamic range ofmore than 140 dB/root Hz in unshielded environment, a digital dynamics of the ADC of more than 25 bit (>150 dB) and multiple frequency excitation. A large number of stacked aluminum samples resembling aircraft fuselage were measured, containing titanium rivets and hidden defects In different depthsin order to obtain sufficient statistical information for classification of the defect geometry. We report on flaw reconstruction using adapted feature extraction and neural network techniques.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 31/03/20 alle ore 03:32:30