• Nuclear Engineering and Technology
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• 제36권3호
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• pp.210-218
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• 2004

In the interest of nuclear power plant safety, a self-diagnostic monitoring system (SDMS) is needed to monitor defects in safety-related components. An air-operated valve (AOV) is one of the components to be monitored since the failure of its operation could potentially have catastrophic consequences. In this paper, a model of the AOV is developed with the parameters that affect the operational characteristics. The model is useful for both understanding the operation and correlating parameters and defects. Various defects are introduced in the experiments to construct a fault library, which will be used in a pattern recognition approach. Finally, the validity of the fault library is examined.

• Nuclear Engineering and Technology
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• 제53권2호
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• pp.532-548
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• 2021

Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of a severe accident (SA). Though a lot of analysis and research is carried out on SA, right from the development of the nuclear industry, not all the possible circumstances are taken into consideration. Therefore, to improve the efficacy of the safety of nuclear power plants, additional analytical studies are needed that can directly monitor severe accident phenomena. This paper presents an interacting multiple model (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vessel phenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperature distribution during severe accidents in a nuclear power plant. The estimation of wall temperature is treated as a state estimation problem where the time-varying wall temperature is estimated using IMM employing three multiple models for temperature evolution. From the estimated RV out-wall temperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown, corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with five different types of SA scenarios and the results show that the proposed method has estimated the outer wall temperature with good accuracy.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1998년도 춘계학술발표회논문집(1)
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• pp.264-270
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• 1998

This research broadens the prime concern of nuclear power plant operations from safe performance to both economic and safe performance. First emergency diesel generator is identified as one of main contributors for the lost plant availability through the review of plants forced outage records. The framework of an integrated architecture for performing modern on-line condition for operational availability improvement is configured in this work. For the development of the comprehensive sensor networks for complex target systems, an integrated methodology incorporating a structural hierarchy, a functional hierarchy, and a fault-system matrix is formulated. The second part of our research is development of intelligent diagnosis and maintenance advisory system, which employs Bayesian Belief networks (BBNs) as a high level reasoning tool incorporating inherent uncertainty use in probabilistic inference. Our prototype diagnosis algorithms are represented explicitly through topological symbols and links between them in a causal direction. As new evidence from sensor network development is entered into the model especially, our advisory of system provides operational advice concerning both availability and safety, so that the operator is able to determine the likely modes, diagnose the system state, locate root causes, and take the most advantageous action. Thereby, this advice improves operational availability

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2488-2491
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• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1445-1449
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• 2005

The ram assembly is important equipment in fueling machine of PHWR(Pressurized Heavy Water Reactor) plant where fuel replacement is possible while the plant is in service. Troubles in the ram assembly can cause lots of difficulties in power plant operation. The ram assembly is typically composed of the B-ram, the L-Ram and the C-Ram. The B-ram is focused in this paper because it plays the most important role in the ram assembly. Among the ram fault phenomena, ram stuck phenomena are the most frequent cases in the B-ram, which has a ball screw mechanism driven by a hydraulic motor. Ram stuck phenomena are due to ball wear and damage in ball nut that increase in proportion to the number of fuel replacement. It is required to predict ram stuck phenomena before they occur. In this paper, a method is proposed for predicting ram stuck phenomena using a discrete wavelet transform. The discrete wavelet transform provides information on both the time and frequency characteristics of the input signals. The proposed method uses the frequency bandwidths of coefficients of discrete wavelet decompositions and detail coefficients of discrete wavelet transform to predict ram stuck phenomena. The signal used in this paper is a torque-related signal such as a hydraulic service outlet pressure signal in a hydraulic driving system or a current signal in a DC motor driving system. Finally, the validity of the proposed method is shown via experiment using ball nut characteristic test equipment that simulates ram stuck phenomena due to increased ball friction in ball nut.

• International Journal of Aeronautical and Space Sciences
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• 제9권1호
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• 융합정보논문지
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• 제9권10호
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• pp.101-107
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• 2019

본 논문은 화력 발전소 및 시멘트 산업에서 필요한 원자재의 운송 수단에 사용되는 컨베이어 시스템에서의 고장을 검출하는 방법을 제안하였다. 산업현장에서 사람이 접근하기가 힘들고 넓은 공간에 시스템이 동작 하는 점을 고려하여 소형 드론을 설계하였고 컨베이어의 이상을 감지하기 위하여 컨베이어에 내장된 모터의 이상 소음을 감지하는 방법을 임베디드 환경으로 설계하여 드론에 장착하는 구조로 제안하였다. 시스템을 임베디드 마이크로프로세서에 적용하기 위하여 제한된 메모리와 수행 시간을 고려한 하드웨어 및 알고리즘을 제안하였으며 주파수 분석을 통해 고장의 경향을 파악하여 알고리즘 화 하였다. 이때 고장 판별 방식은 측정을 통하여 피크주파수를 측정하고 고주파수의 연속성을 감지하는 방식으로 고장에 의한 소음의 높은 주파수를 분석하여 고장진단을 시행할 수 있었다. 제안된 시스템은 실제 화력 발전소에서 취득한 데이터를 바탕으로 실험 환경을 구성하였으며 드론에 설계된 시스템을 탑재하여 가상 환경 실험을 하여 제안된 시스템의 유용성을 확인하였다. 향후에는 드론의 비행 안정성 향상과 고장 주파수에 대한 좀 더 정밀한 방법을 사용하여 판별성능을 향상 시키는 연구가 요구된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.2019_2020
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• 2009

A recurrent surge oscillograph(RSO) test was performed at the Taean thermal power plant on #5 turbine generator. The test was conducted using a rotor reflectometer and digital oscilloscope. A DC voltage step is applied to each end of the rotor winding in turn. Each reflected wave, at the input end of the winding, is monitored and the two waveforms are superimposed automatically and monitored on a single channel oscilloscope. As the half windings in a rotor are identical, the two waveforms monitored at each end of the rotor will also be identical for a healthy winding. A winding with a fault will cause different voltages to be monitored at the two ends.

• Nuclear Engineering and Technology
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• 제28권1호
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• pp.36-43
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• 1996

본 논문은 원자력발전소 열성능 감시 시스템의 PC기반 구현에 관한 연구 내용이다. 이 시스템은 열성능 감시와 진단을 플랜트 운전중에 실시간으로 수행할 수 있다. 고리 원전2호기를 목적호기로 원형 시스템을 구성하여 시험해 보았다. 원자력발전소의 열 주기 시스템은 대단히 복잡하고 구성 요소간에 상호 영향이 커서, 그 분석과 고장 진단에 어려움이 많다. 본 연구에서는 열 주기를 효율적으로 표현하고, 계산시간을 단축하기 위해 성능 진단 변수를 설정하였다. 비정상 상태에서의 진단 변수의 특성 패턴 변화를 인식하기 위해 자기학습 신경망의 일종인 퍼지아트맵을 이용하였다. 시험을 통해 이 알고리듬이 비정상 상태를 감지하고 고장 원인을 성공적으로 규명하는 것을 보였으며, 운전원의 편의를 위해 그래픽 사용자 인터페이스를 구축하였다.

• 조명전기설비학회논문지
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• 제29권8호
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• pp.98-103
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• 2015

Power plant is that very high reliability when industrial and economic impact on the overall electric power system is required, it is essential to improve the reliability, especially the fault prediction diagnosis. Since an accident caused by the partial discharge in the power plant is above state has a faster response characteristic than the other indications in the case of any, the partial discharge generated in the power plant immediately detect the deterioration of insulation due to the accident of the power plant and the non-drawn It should prevent or reduce. Partial Discharge Measuring Systems for UHV SF6 Gas Insulated Switchgear and power transformer on site installed has some probability of abnormal recognition in case of non-flexible deal with on site noise. Many methode to eliminate these kinds of noises, UHF Detection System is chosen as purchase description in Korea, but this system having a bandwidth between 500MHz 1.5GHz wide band. Initial install periods(about 20 years ago), this band had no strong signal source, but in these days this wide band have strong signals, such as LTE. So, module described in this paper is designed as simultaneously use with wide and narrow band for solve this noise problem, and introduce this system.