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Classification of the Types of Defects in Steam Generator Tubes using the Quasi-Newton Method
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 Title & Authors
Classification of the Types of Defects in Steam Generator Tubes using the Quasi-Newton Method
Lee, Joon-Pyo; Jo, Nam-H.; Roh, Young-Su;
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 Abstract
Multi-layer perceptron neural networks have been constructed to classify four types of defects in steam generator tubes. Three features are extracted from the signals of the eddy current testing method. These include maximum impedance, phase angle at the point of maximum impedance, and an angle between the point of maximum impedance and the point of half the maximum impedance. Two hundred sets of these features are used for training and assessing the networks. Two approaches are involved to train the networks and to classify the defect type. One is the conjugate gradient method and the other is the Broydon-Fletcher-Goldfarb-Shanno method which is recognized as the most popular algorithm of quasi-Newton methods. It is found from the computation results that the training time of the Broydon-Fletcher-Goldfarb-Shanno method is much faster than that of the conjugate gradient method in most cases. On the other hand, no significant difference of the classification performance between the two methods is observed.
 Keywords
Eddy current testing;Steam generator;Neural network;Quasi-Newton method;
 Language
English
 Cited by
1.
자동차 엔진마운트의 내구성 해석,한문식;조재웅;

한국자동차공학회논문집, 2012. vol.20. 2, pp.141-147 crossref(new window)
1.
Durability Analysis on Automotive Engine Mount, Transactions of the Korean Society of Automotive Engineers, 2012, 20, 2, 141  crossref(new windwow)
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