• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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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.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.27 no.2
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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.

• 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.

• 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.

• 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 Nuclear Society Conference
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• 1997.05a
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• pp.166-171
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• 1997

LPMS(Loose Part Monitoring System)는 원자로 및 냉각재계통내에서 발생하는 금속파편의 검출 및 분석을 위하여 사용되는 진단 장비이다. 본 논문에서는 RPV(Reactor Pressure Vessel)의 상부헤드(closure head)와 하부헤드(lower head)에서의 금속파편의 충격위치를 평가하는 LPMS를 위한 새로운 기법을 제안하고, Mock-up에서의 실험을 통하여 그 효용성을 검증하였다. 즉, 수정된 원교차법을 제안하고, 이를 반구로 모델링된 RPV의 상ㆍ하부헤드에 존재하는 금속파편의 위치평가에 적용하므로써 정확한 충격위치를 찾을 수 있음을 보였다. 이들 결과는 충격물질의 질량이나 에너지를 계산하는데 정확한 정보를 제공해 줄 수가 있다.

• 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.