Defect detection and classification using a SQUID based multiple frequency eddy current NDE system

IEEE Transactions on Applied Superconductivity

Volumn 11, Issue 1 I, 2001, Pages 1032-1037

  Kreutzbruck, M V ^a   Allweins, K ^a   Ruhl T ^a   Muck M ^a   Heiden, C ^c   Krause, H J ^b   Hohmann, R ^b  

^a JUSTUS LIEBIG UNIVERSITY   (Germany)

^b FORSCHUNGSZENTRUM JÜLICH   (Germany)

^c NONE

Author keywords

Feature extraction; Nondestructive testing; SQUIDs

Indexed keywords

DEFECTS; EDDY CURRENT TESTING; FATIGUE OF MATERIALS; FEATURE EXTRACTION; FUSELAGES; MAGNETOMETERS; NEURAL NETWORKS; SIGNAL TO NOISE RATIO; STATISTICAL METHODS;

DEFECT DETECTION; PROBABILITY OF DETECTION;

SQUIDS;

EID: 0035268608     PISSN: 10518223     EISSN: None     Source Type: Journal    
DOI: 10.1109/77.919525     Document Type: Conference Paper

References (15)

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13. An evaluation of artificial neural networks for the classification of eddy current signals
14. Bayesian methods for intelligent data analysis