• Nuclear Engineering and Technology
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• v.43 no.4
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• pp.343-354
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• 2011

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.179-181
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• 2006

각 원자력 발전소에서는 정밀성, 안전성을 확인하는 검사의 중요성을 인식하여 LPMS(Loose Part Monitoring System)을 사용하여 사고 징후를 조기에 감지하여 이에 대한 예방조치를 가능케 함으로써 설계기준 사고 발생을 사전에 방지할 수 있게 한다. 또한 이 기술은 신호 측정 및 분석 등의 기반기술 개발을 통하여 건전성 감시 기술의 신뢰성을 향상 시키고 있다. LPMS(Loose Part Monitoring System)기술은 재료, 기기, 구조물 등의 성질과 내부조직을 변화시키거나 파괴하지 않고, 배관내부에 흐르는 금속 파편들을 찾아내어 정밀성, 안전성, 신뢰성을 확보하기 위하여 검사기술이 적용되고 있다. 그러나 이 방법은 배관내의 이물질의 충격이 발생해야 감지가 가능하고, 이물질의 모양이나 사이즈를 확인하기에는 어려움이 있다. 따라서 본 논문에서는 배관외부에서 자기장을 인가하여, 배관내의 이물질에 변화하는 자기장을 홀센서로 측정하여 기존의 LPMS 방식을 보완하는 시스템을 개발하기 위해, 배관에 필요한 자기장 발생장치를 설계하고, 이물질을 검출하기 위한 검출 감도향상에 대해 연구하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.650-657
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• 2006

The LPMS (Loose Part Monitoring Systems) are used widely for detecting the impact position in the nuclear reactor. There are some major methods to detect impact position in LPMS such as the triangular method, the rectangular method, the circular intersection method and so on. The time difference of these methods are calculated using S0-mode and A0-mode waves of each sensor. In this paper, we propose a method to detect impact position using the enveloped waves of acquired signals. The result of this paper show that the position detecting accuracy and reducing the processing time are proposed method is improved than traditional methods.

• Journal of IKEEE
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• v.3 no.1 s.4
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• pp.101-108
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• 1999

In this paper, a real time diagnostic algorithm for estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). First, ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. Second, IEM by use of the arrival time method estimates the impact location of loose parts. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.227-230
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect, locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal, Rising time, Half period, and Global time, they are used as the inputs to neural network. Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising Time, Half Period, Maximum amplitude. The result showed that the neural network would be applied to LPMS. Also, applying the neural network to the Practical false alarm data during startup and impact test signal at nuclear power Plant, the false alarms are reduced effectively. 1.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.14 no.1
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• pp.38-47
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• 2018

For safe operation of nuclear power plants, a loose-part monitoring system (LPMS) is used to detect and locate loose-parts within the reactor coolant system, and to estimate their mass and damage potential. There are several methods to estimate mass, such as the center frequency method based on the Hertz's impact theory, a frequency ratio method and so on, but it is known that these methods cannot provide accurate information on impact response for identifying the impact source. Thanks to increasing computing power, finite element analysis (FEA) method recently become an available option to calculate reliably impact response behavior. In this paper, a finite element analysis model to simulate the propagation behavior of the bending wave, generated by a metal ball impact, is validated by performing a series of impact tests and the corresponding finite element analyses for flat plate and shell structures. Also, a FEA-based metal sphere signal map is developed, and then blind tests are performed to verify the map. This study provides an accurate simulation method for predicting the metal impact behavior and for building a metal sphere signal map, which can be used to estimate the mass of loose-parts on site in nuclear power plants.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.741-742
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• 2012

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.5-19
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• 1996

This paper proposes a loose part monitoring system (LPMS) design with a signal processing method based on fuzzy logic. Considering fuzzy characteristics of metallic impact waveform due to not only interferences from various types of noises in an operating nuclear power plant but also complex wave propagation paths within a monitored mechanical structure, the proposed LPMS design incorporates the comprehensive relation among impact signal features in the fuzzy rule bases for the purposes of alarm discrimination and impact diagnosis improvement. The impact signal features for the fuzzy rule bases include the rising time, the falling time, and the peak voltage values of the impact signal envelopes. Fuzzy inference results based on the fuzzy membership values of these impact signal features determine the confidence level data for each signal feature. The total integrated confidence level data is used for alarm discrimination and impact diagnosis purposes. Through the perpormance test of the proposed LPMS with mock-up structures and instrumentation facility, test results show that the system is effective in diagnosis of the loose part impact event(i.e., the evaluation of possible impacted area and degree of impact magnitude) as well as in suppressing false alarm generation.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.295-300
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• 1998

In this paper, a real time diagnostic algorithm fur estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. When the decision from ADM is concluded as the impact signal, the beginning time of burst-type signal, which the impact signal has usually such a form in time domain, provides the necessary data fur IEM. IEM by use of the arrival time method estimates the impact location of loose parts. The overall results of the estimated impact location are displayed on a computer monitor by the graphical mode and numerical data composed of the impact point, and thereby a plant operator can recognize easily the status of the impact event. This algorithm can perform the diagnosis process automatically and hence the operator's burden and the possible operator's error due to lack of expert knowledge of impact signals can be reduced remarkably. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.