• 한국음향학회지
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• 제16권6호
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• pp.76-84
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• 1997

원전내 금속파편들을 조기에 탐지하기 위한 금속파편 감시계통(LPMS : Loose Parts Monitoring System)은 원전의 안전성 및 신뢰도 확보를 위하여 중요한 부분으로서, 대부분의 국내 원전들에서 설치 운영중이거나 운영예정이다. 하지만 이들 LPMS들은 외국에서 개발된 것들로서 고가이며 기술이전이 이루어지지 않아 기술종속의 우려와 함께 효과적인 금속파편 진단에 많은 어려움을 지닌다. 따라서 본 논문의 주된 목적은 고해상도를 가지며 분석방법이 간단한 효율적인 금속파편 위치평가를 위한 알고리즘을 제안, 실현하므로써, 빈전문적인 운전자도 컴퓨터를 사용한 간단한 조작을 통하여 정확하고 신속한 금속파편 진단을 수행할 수 있도록 하는 것이다. 본 논문에서 제안한 수정된 원교차법을 이용하여 작성된 금속파편 위치평가 프로그램을 이용하여 실제 원전상황을 고려한 모의실험을 실시한 결과, 제안된 평가기법이 약 3.4% 정도 오차를 가지는 우수한 위치평가를 수행함을 알 수 있었다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.839-841
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• 2014

The Nuclear Steam Supply System(NSSS) is designed to provide an integrated approach that includes areas of monitoring relevant to the integrity of the NSSS. LPMS is designed to function as an alarm system by providing sensor channel alarms for the associated subsystems. LPMS is equipped to provide analysis tools for new alarm events, historical events and for historical periodically stored channel data (e.g. waveforms) for most channels. This paper is intended to introduce the diagnosis principle and abnormal symptom of loose parts monitoring system as a monitoring tool in Nuclear Steam Supply System. And also, we are going to introduce signal analysis program in order to perform the actual diagnosis in power plants.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.670-678
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• 2000

Loose parts monitoring system(LPMS) is one of the important monitoring systems for the safe and efficient operation of the nuclear reactor, since it is LPMS that can early detect loose parts which may cause a significant damage in facilities or components of the plant. Nuclear power plants in Korea have recently experienced several loose part alarms due to the metallic impact and it is expected that the frequency of the loose part will be increased along the aging of the plants. In this paper, the status of loose parts monitoring technologies and facilities in Korean nuclear power plants is presented for the establishment of LPMS installation plan in some nuclear reactors which are not yet equipped with LPMS. Sensor specification, location and mounting method for loose parts monitoring were reviewed. As a result, the location and the mounting method of the properly chosen sensor was recommended. Data acquisition algorithms and discriminating rules of loose part impact signals were also reviewed. Actual alarm cases occurred by true impact signal and false impact signal were stated here.

• 한국소음진동공학회논문집
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• 제27권2호
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• pp.148-154
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• 2017

This paper discussed design for resonance avoidance of sensor plates of loose-parts monitoring systems (LPMS) in nuclear power plants (NPP). An LPMS monitors impact of loose parts in primary loop of NPP by using accelerometers, which is mounted on sensor plates. Resonance of the plates may cause false alarms at frequencies over 10 kHz, which can be misunderstood as impact signals of loose parts with small mass and cause unnecessary response of NPP operators. Modal analysis was carried out for the existing sensor plate and design parameters affecting natural frequencies were chosen. Frequency response functions of plates were analyzed by changing the parameters and the optimized plate design for avoiding resonance was determined. Experiments was carried out for the plate specimen with improved design and verified the proposed approach and design.

• Nuclear Engineering and Technology
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• 제49권7호
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• pp.1555-1562
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• 2017

A loose part monitoring system is used to identify unexpected loose parts in a nuclear reactor vessel or steam generator. It is still necessary for the mass estimation of loose parts, one function of a loose part monitoring system, to develop a new method due to the high estimation error of conventional methods such as Hertz's impact theory and the frequency ratio method. The purpose of this study is to propose a mass estimation method using a Markov decision process and compare its performance with a method using an artificial neural network model proposed in a previous study. First, how to extract feature vectors using discrete cosine transform was explained. Second, Markov chains were designed with codebooks obtained from the feature vector. A 1/8-scaled mockup of the reactor vessel for OPR1000 was employed, and all used signals were obtained by impacting its surface with several solid spherical masses. Next, the performance of mass estimation by the proposed Markov model was compared with that of the artificial neural network model. Finally, it was investigated that the proposed Markov model had matching error below 20% in mass estimation. That was a similar performance to the method using an artificial neural network model and considerably improved in comparison with the conventional methods.

• Nuclear Engineering and Technology
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• 제52권4호
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• pp.846-855
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• 2020

A loose part monitoring system is used to detect unexpected loose parts in a reactor coolant system in a nuclear power plant. It is still necessary to develop a new methodology for the localization and mass estimation of loose parts owing to the high estimation error of conventional methods. In addition, model-based diagnostics recently emphasized the importance of a model describing the behavior of a mechanical system or component. The purpose of this study is to propose a new localization and mass-estimation method based on finite element analysis (FEA) and optimization technique. First, an FEA model to simulate the propagation behavior of the bending wave generated by a metal sphere impact is validated by performing an impact test and a corresponding FEA and optimization for a downsized steam-generator structure. Second, a novel methodology based on FEA and optimization technique was proposed to estimate the impact location and mass of a loose part at the same time. The usefulness of the methodology was then validated through a series of FEAs and some blind tests. A new feature vector, the cross-correlation function, was also proposed to predict the impact location and mass of a loose part, and its usefulness was then validated. It is expected that the proposed methodology can be utilized in model-based diagnostics for the estimation of impact parameters such as the mass, velocity, and impact location of a loose part. In addition, the FEA-based model can be used to optimize the sensor position to improve the collected data quality in the site of nuclear power plants.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권2호
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• pp.95-99
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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 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 clime. Half Period amplitude. The result shored that the neural network would be applied to LPMS. Also, applying the neural network to thin practical false alarm data during startup and impact test signal at nuclear power plant, the false alarms are reduced effectively.

• 전기전자학회논문지
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• 제3권1호
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• pp.101-108
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• 1999

본 논문은 금속파편이 발생시 금속파편의 위치를 추정하기위한 위치 추정 알고리즘을 제안한다. 이 추정 알고리즘은 자동화된 알람 구별부와 위치 추정부로 구성된다. 알람 구별부는 충격 신호인지 거짓 충격 신호인지를 구별하는 기능을 수행하며, 위치 추정부는 충격신호로부터 충격신호의 시작점을 찾아내어 위치를 추정한다. 이 알고리즘의 타당성을 검증하기위해 원자로 Mock-up 상에서 실험한 결과 위치 추정값 과 실제 충격값이 약 10 % 이내의 오차율을 보였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.647-650
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• 1998

본 논문에서는 원자로의 몸통부위와 상,하부 헤드에 존재하는 금속파편에 의한 충격위치 예측을 위한 알고리즘에 금속파편의 질량을 동시에 판별하는 프로그램을 접목시켜 금속파편의 충격위치와 질량을 동시에 판별할 수 있는 통합 환경 LPMS(loose parts monitoring system)에 관한 연구를 수행하였다. 또한 모의실험을 통하여 본 연구에서 제안된 통합 환경 LPMS 알고리즘이 금속파편의 위치와 질량 예측을 함에 있어서 우수한 성능을 보임을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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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.