• 전기학회논문지
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• 제56권7호
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• pp.1224-1230
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• 2007

In this paper, we study the classification of defects at steam generator tube in nuclear power plant using eddy current testing (ECT). We consider 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. Through numerical analysis program based on finite element modeling, 400 ECT signals are generated by varying width and depth of each defect type. In order to improve the classification performance, we propose new feature extraction technique. After extracting new features from the generated ECT signals, multi-layer perceptron is used to classify the defect patterns. Through the computer simulation study, it is shown that the proposed method achieves 100% classification success rate while the previous method yields 91% success rate.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2603-2605
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• 2003

증기발생기(SG : Steam Generator)는 다수의 세관으로 구성되어 원자로에서 발생한 열을 이용하여 발전기 터빈을 구동시키는 원동력인 증기를 생성해 주는 기능을 하는 원자력발전소의 핵심 설비이다. 증기 발생기 세관의 건전성을 확보하기 위해 매주기 계획예방 정비, 즉 가동중검사마다 정기적인 와전류검사를 수행하고, 검사결과에 따라 전열관 보수 등과 같은 제반 조치를 취하고 있다. 현재 검사데이터 DB 구축은 일부 발전소에 개발되어 운영중에 있고, 세관 DB와는 별도로 통계정보만을 관리하는 증기발생기 성능관리시스템이 운영되고 있으며, 또한 각 발전소마다 수질을 계측하여 수화학 성분을 감시하는 수질관리시스템이 운용되고 있다. 이러한 이원화된 DB 및 시스템을 통합하고 연계하여 전 원전의 증기발생기를 종합적으로 관리 할 수 있는 시스템의 필요성이 대두되었다. 따라서 본 논문에서는 현장에 보관되어 있는 모든 세관 검사데이터를 취득하여 대용량 데이터베이스를 설계 및 구축하고 이기종의 분산된 수질관리시스템 DB를 연계하여, 증기발생기의 설계/제작부터 검사결과 Mapping, 추이 분석을 통한 수명평가에 이르는 전 과정을 통합 관리한 수 있는 시스템을 개발하고 그 구현방안을 제시한다.

• 전기학회논문지
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• 제58권12호
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• pp.2532-2537
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• 2009

For defect characterization in steam generator tubes in nuclear power plant, artificial neural network has been extensively used to classify defect types. In this paper, we study the effectiveness of Bagging for improving the performance of neural network for the classification of tube defects. Bagging is a method that combines outputs of many neural networks that were trained separately with different training data set. By varying the number of neurons in the hidden layer, we carry out computer simulations in order to compare the classification performance of bagging neural network and single neural network. From the experiments, we found that the performance of bagging neural network is superior to the average performance of single neural network in most cases.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.53-55
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• 2005

The signals of the eddy current testing(ECT) for the examination of the steam generator(SG) tubes in the nuclear power plant(NPP) determine the existence, size, and kind of defects using the variation of impedance signals when a testing coil, driven by alternating current, passes through the SG tube contains defects. The aim of this paper is building a database of the RPC probe signals on the basis of the sizes variation of defects and frequency variation of probe. In this paper 3-D numerical analysis of the ECT signals using the finite element method is performed. Through this study, it is shown variation of magnitude and phase of impedance according to variation of defect size and frequency. From the result of this paper, we can obtain the information which is useful in defect discrimination of SG tube in nuclear power plant.

• 비파괴검사학회지
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• 제27권5호
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• pp.383-392
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• 2007

본 논문에서는 신경회로망을 이용하여 원자력 발전소 증기발생기 세관의 결함 깊이와 폭을 예측하는 연구를 수행한다. 결함 크기 추정을 위하여 우선, I-In 형태, I-Out 형태, V-In 형태, V-Out 형태의 4가지 결함형상에 대한 와전류탐상시험(ECT) 신호를 생성한다. 특히, 유한요소법에 기반한 수치해석 기법을 이용하여 여러 가지 폭과 깊이를 갖는 결함 400개의 ECT 신호를 생성한다. 이와 같이 생성된 ECT 신호로부터, 결함 크기와 폭을 예측하기 위한 새로운 특징벡터를 추출하는데, 이 특징벡터에는 최대 임피던스 값을 갖는 점과 최대 임피던스값의 1/2의 값을 갖는 점 사이의 위상각이 포함된다. 추출된 특징벡터를 이용하여 결함의 크기를 예측하기 위해서 하나의 은닉층을 갖는 다층퍼셉트론을 이용하였다. 컴퓨터 모의실험 연구를 통하여 제안된 방법이 우수한 예측성능을 갖는다는 것을 보였다.

• 조명전기설비학회논문지
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• 제24권1호
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• pp.63-70
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• 2010

본 논문에서는 원자력발전소 증기세관 크기 예측을 위한 신경회로망 구조에 대해서 연구한다. 와류탐상 시험(ECT) 신호로부터 특징을 추출한 후, 결함크기 예측을 위해서 다층퍼셉트론 신경회로망을 이용한다. 결함크기 예측성능을 최대화하기 위해서는 신경회로망의 구조, 특히 은닉층 내의 뉴런의 개수를 신중히 결정하여야 한다. 본 논문에서는, 결함크기 예측을 위한 은닉층 내의 뉴런의 개수를 교차검증을 이용하여 매우 효과적으로 결정할 수 있음을 보인다.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2006년도 하계학술대회
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• pp.241-245
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• 2006

In the examination of Steam Generator (SG) tube in Nuclear Power Plant (NPP) Eddy Current Testing (ECT) probes play an Important role in detecting the defects. Bobbin probe and Rotating Pancake Coil (RPC) probe is usually used for the inspection of SG tube. Bobbin probe is good at high speed inspection, but ability of detection of circumferential defect is very weak. On the contrary RPC probe, which moves for inspection in the direction of axial and circumferential simultaneously, has very slow inspection speed, but it was excellent detection capability fur small cracks, which is hardly detected by bobbin probe. Many examinations of SG tube examination of NPP are achieved during short period. Therefore, solution about this must develop probe of new form for examination performance and examination time shortening of other probe. In this paper, analyzed technological present condition of Bob-bin probe and RPC probe been using in Nondestructive Testing (NDT) for SG tube defect detection and Appeared about background theory and view of developed probe newly.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 B
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• pp.1008-1010
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• 2005

In the examination of steam generator(SG) tube in nuclear power plant, eddy current testing probes play an important role in detecting the defects. Bobbin probe and MRPC probe is usually used for the inspection of SG tube. Bobbin probe is good at high speed inspection, but ability of detection of circumferential defect is very weak. On the contrary MRPC probe, which moves for inspection in the direction of axial and circumferential simultaneously, has very slow inspection speed, but it has excellent detection capability for small cracks, which is hardly detected by bobbin probe. In this paper, for the accurate analysis of experimental ECT signals, construction of MRPC probe signals database according to the variation of defect size is the main purpose. Using 3-D finite element method, ECT signals are analyzed, and signals analysis add according to frequency ingredient. The results, which are analysis and characteristics ion of electromagnetism simulation signals, is databased.

• 전자공학회논문지S
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• 제35S권2호
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• pp.137-145
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• 1998

In this paper, we propose a tube-hole center detection vision algorithm verifying the position of a tele-controlled robot and providing visual information for increasing reliability and efficiency in the diagnosis of steam generator (SG) tubes in nuclear power plant. A tele-controlled robot plays a role in carrying the probe used in inspecting the integrity of SG tubes. Thus accurately locating a tele-controlled robot on the desired tube-hole center is important issue for reliability of inspection. To do this work, we have to find the tube-hole center locations from the input image. At first, we apply the three-class segmentation method modified for this application. WE extract minimum bounding rectangles (MBRs) in the theresholded binary image. Second, for discriminating between MBR by tube and MBR by noise, we introduce the MBR rejection rules as knowledge-based rule set. MBRs are divided into the very dark region MBRs and the very bright region MBRs. In order to describe the region of complete tube-hole, the MBRs need a process of pairing each other. We then can find the tube-hole center from the paired MBR. For more accurately finding the tube-hole center in several sequential images, the centers of some frames need to be averaged. We tested the performance of our method using hundreds of real images.

• 전기학회논문지
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• 제57권11호
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• pp.2095-2101
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• 2008

In this paper, we consider a performance improvement of neural network for predicting defect size of steam generator tube using early stopping. Usually, neural network is trained until MSE becomes less than a prescribed error goal. The smaller the error goal, the greater the prediction performance for the trained data. However, as the error goal is decreased, an over fitting is likely to start during supervised training of a neural network, which usually deteriorates the generalization performance. We propose that, for the prediction of an axisymmetric defect size, early stopping can be used to avoid the over-fitting. Through various experiments on the axisymmetric defect samples, we found that the difference bet ween the prediction error of neural network based on early stopping and that of ideal neural network is reasonably small. This indicates that the error goal used for neural network training for the prediction of defect size can be efficiently selected by early stopping.