• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.61-67
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• 2015

Recently, the initiation of outside diameter stress corrosion cracking (ODSCC) at the tube support plate region of domestic steam generators (SG) with Alloy600 HTMA tubes has been increasing. As a result, SGs with Alloy600 HTMA tubes must be replaced early or are scheduled to be replaced prior to their designed lifetime. ODSCC is one of the biggest threats to the integrity of SG tubes. Therefore, the accurate evaluation of tube integrity to determine ODSCC is needed. Eddy current testing (ECT) is conducted periodically, and its results could be input as parameters for evaluating the integrity of SG tubes. The reliability of an ECT inspection system depends on the performance of the inspection technique and abilty of the analyst. The detection probability and ECT sizing error of degradation are considered to be the performance indices of a nondestructive evaluation (NDE) system. This paper introduces an optimized evaluation method for ECT, as well as the sizing error, including the analyst performance. This study was based on the results of a round robin program in which 10 inspection analysts from 5 different companies participated. The analysis of ECT sizing results was performed using a linear regression model relating the true defect size data to the measured ECT size data.

• 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.6
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• pp.455-463
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• 1998

RCCA(rod cluster control assembly) End-Tip suffers from neutron irradiation and constant vibration due to high-speed internal flow of primary coolant during plant operation. Such operating conditions cause the RCCA end-tip crackings around tile circumferential weldment of the end-tip, and in some cases, the defective end-tips were completly broken loose. However, no reliable inspection techniques for end-tip crackings were developed in the past, although some techniques exist for inspecting RCCA control rod wears. Therefore, NDE group at KEPRI has developed an ECT technique for the detection and the sizing of the end-tip crackings. The technique uses a specially designed surface-riding probe that can detect size of circumferential crackings with an accuracy of ${\pm}5.31%$ RMS error. This paper describes the ECT instrumentation including the ECT probes, calibration bars, as well as technical approaches.