• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.1
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• pp.9-15
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• 2008

A new ECT probe to separate the ECT signal distortion due to PVC (permeability variation clusters) and ordinary defects created in SG tubes has been developed. The hystersis loops of PVC which are extracted from retired SG (steam generator) tubes of Kori-1 NNP were measured. The tensile tests were performed to identify the mechanism of PVC creation. The conditions detecting the PVC created in 56 tubes were investigated using computer simulation, and the signal processing circuits were inserted in the probe for the digital signal transmission. The new Probe can measure and separate the PVC signal which is created in the SG tubes, and also measures the defects in Ni-sleeving part of SG tubes. furthermore the new ECT probe can measure the defects as fast as bobbin probe, and enhance the testing speed as well as reliability of the defect detection of SG tubes.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.13-19
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• 2010

In this paper, we study a new estimation system for the prediction of steam generator tube defects. In the previous research works, defect size estimators were independently designed for each defect types in order to estimate the defect size. As a result, the structure of estimation system is rather complex and the estimation performance gets worse if the classification performance is degraded for some reason. This paper studies a new estimation system that does not require the classification of defect types. Although the previous works are expected to achieve much better estimation performance than the proposed system since it uses the estimator specialized in each defect, the performance difference is not so large. Therefore, it is expected that the proposed estimator can be effectively used for the case where the defect type classification is imperfect.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.