• 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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• 제26권4호
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• pp.469-474
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• 2016

Abnormal sound was heard from a heat exchanger of condensate water system in a nuclear power plant, which was identified as impact sound of a loose part later. Nuclear power plants are normally equipped with loose part monitoring system for primary water system, but not for secondary water system. The abnormal sound was analyzed by using the impact signal-processing methodology based on the Hertz theory. The predicted results for impact location and size of the loose part showed good agreement with those of the actual loose part found during the overhaul period in the plant. So, this analysis methodology for the impact signal will be widely utilized for the primary and secondary side of the nuclear power plant.

• 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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 추계학술대회논문집
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• pp.687-692
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• 2008

In a nuclear power plant, loose part monitoring and its diagnostic technique is one of the major issues for ensuring the structural integrity of the reactor system. Typically, accelerometers are mounted on the surface of a reactor vessel to localize impact location caused by the impact of metallic substances on the reactor system. However, in some cases, the number of the accelerometers is not enough to estimate the impact location precisely. In such a case, one of alternative plan is to utilize another type sensors that can measure the vibration of the reactor structure even though the measuring frequency ranges are different from each others. The AE sensors installed on the reactor structure can be utilized as additional sensors for loose part monitoring. In this paper, we proposed a new method to estimate impact location by using both accelerometer signal and AE signal, simultaneously. The feasibility of the proposed method is verified by an experiment. The experimental results demonstrate that we can enhance the reliability and precision of the loose part monitoring.

• 한국소음진동공학회논문집
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• 제18권11호
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• pp.1143-1149
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• 2008

In a nuclear power plant, loose part monitoring and its diagnostic technique is one of the major issues for ensuring the structural integrity of the reactor system. Typically, accelerometers are mounted on the surface of a reactor vessel to localize impact location cavsed by the impact of metallic substances on the reactor system. However, in some cases, the number of the accelerometers is not enough to estimate the impact location precisely. In such a case, one of alternative plan is to utilize another type sensors that can measure the vibration of the reactor structure even though the measuring frequency ranges are different from each others. The AE sensors installed on the reactor structure can be utilized as additional sensors for loose part monitoring. In this paper, we proposed a new method to estimate impact location by using both accelerometer signal and AE signal, simultaneously. The feasibility of the proposed method is verified by an experiment. The experimental results demonstrate that we can enhance the reliability and precision of the loose part monitoring.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.522-522
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• 2000

In this paper, two pre-filtering techniques are presented for accurately estimating the impact location of a loose part. The reason why a pre-filterng technique Is necessary in a Loose Part Monitoring System is that the effects of background noise on the signal to noise ratio (SNR) can be reduced considerably resulting in improved estimation accuracy. The first method is to take d moving average operation in the time domain. The second one is to adopt band-pass filters designed in the frequency domain such as a Butterworth filter, Chebyshev filter I & II and an Elliptic Filter. To show the effectiveness, the impact test data (signals) from the YGN3 power plant are first preprocessed and then used to estimate the loose pan impact position. Resultantly. we observed that SNR is much improved and the average estimation error is below 7.5%.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 춘계학술대회논문집
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• pp.1306-1311
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• 2006

The root cause of abnormal sound from a heat exchanger of condensate water system in a nuclear power plant is investigated by using the impact signal-processing methodology based on the Hertz theory. The predicted results for the location of impact force and the loose part size meet good agreement with the identified materials during the overhaul period in the plant. Nuclear power plants have experienced several loose parts and the frequency of the loose part will be increased along the aging of the plants. So, this analysis methodology for the impact signal will be widely utilized for the primary and secondary side of the nuclear power plant.

• 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.