• Nuclear Engineering and Technology
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• 제47권2호
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• pp.204-211
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• 2015

Probabilistic safety assessment (PSA) has had a significant role in quantitative decision-making by finding design and operational vulnerabilities and evaluating cost-benefit in improving such weak points. In particular, it has been widely used as the core methodology for risk-informed applications (RIAs). Even though the nature of PSA seeks realistic results, there are still "conservative" aspects. One of the sources for the conservatism is the assumptions of safety analysis and the estimation of failure frequency. Surveillance, diagnosis, and prognosis (SDP), utilizing massive databases and information technology, is worth highlighting in terms of its capability for alleviating the conservatism in conventional PSA. This article provides enabling techniques to solidify a method to provide time- and condition-dependent risks by integrating a conventional PSA model with condition monitoring and prognostics techniques. We will discuss how to integrate the results with frequency of initiating events (IEs) and probability of basic events (BEs). Two illustrative examples will be introduced: (1) how the failure probability of a passive system can be evaluated under different plant conditions and (2) how the IE frequency for a steam generator tube rupture (SGTR) can be updated in terms of operating time. We expect that the proposed model can take a role of annunciator to show the variation of core damage frequency (CDF) depending on operational conditions.

• 한국항공우주학회지
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• 제49권4호
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• pp.311-320
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• 2021

무인기용 터보제트엔진의 운전 중 발생하는 고장을 실시간으로 진단하기 위한 방안 및 성능 열화와 관련된 건정성 추정에 관해 연구하였다. 이를 위해서, 동적 열역학 가스경로해석을 통한 비선형 동특성 방정식으로부터 실시간 선형모델을 도출하였고, 연출된 운전상황과 고장 발생을 실시간으로 진단하기 위해 칼만필터와 가설 검증에 기초한 확률적 판단 기법을 적용하였다. 이 결과, 분명한 고장 검출과 분리 성능을 보임으로써 그 효용성을 확인하였다. 측정변수를 통한 건전성 추정과 관련하여, 실제 엔진 구성품의 성능 열화 추이를 모사하였고, 적응형 칼만필터를 적용하여 추정 기법의 타당성을 입증함으로써, 상태 기반 고장 진단 및 정비 기법에 효과적으로 사용될 수 있음을 보였다.

• Nuclear Engineering and Technology
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• 제48권5호
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• pp.1280-1290
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• 2016

This paper presents an advanced estimation method for obtaining the probability density functions of a damage parameter for valve leakage detection in a reciprocating pump. The estimation method is based on a comparison of model data which are simulated by using a mathematical model, and experimental data which are measured on the inside and outside of the reciprocating pump in operation. The mathematical model, which is simplified and extended on the basis of previous models, describes not only the normal state of the pump, but also its abnormal state caused by valve leakage. The pressure in the cylinder is expressed as a function of the crankshaft angle, and an additional volume flow rate due to the valve leakage is quantified by a damage parameter in the mathematical model. The change in the cylinder pressure profiles due to the suction valve leakage is noticeable in the compression and expansion modes of the pump. The damage parameter value over 300 cycles is calculated in two ways, considering advance or delay in the opening and closing angles of the discharge valves. The probability density functions of the damage parameter are compared for diagnosis and prognosis on the basis of the probabilistic features of valve leakage.

• 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

• Computers and Concrete
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• 제2권6호
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• pp.439-454
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• 2005

When deterioration of concrete is observed in a structure, it is highly desirable to determine the cause of such deterioration. Only by understanding the cause can an appropriate repair strategy be implemented to address both the cause and the symptom. In colder climates, bridge deck deterioration is often caused by chlorides from de-icing salts, which penetrate the concrete and depassivate the embedded reinforcement, causing corrosion. Bridge decks can also suffer from other deterioration mechanisms, such as alkali-silica reaction, freeze-thaw, and shrinkage. There is a need for a comprehensive and integrative system to help with the inspection and evaluation of concrete bridge deck deterioration before decisions are made on the best way to repair it. The purpose of this research was to develop a model to help with the diagnosis of concrete bridge deck deterioration that integrates the symptoms observed during an inspection, various deterioration mechanisms, and the probability of their occurrence given the available data. The model displays the diagnosis result as the probability that one of four deterioration mechanisms, namely shrinkage, corrosion of reinforcement, freeze-thaw and alkali-silica reaction, is at fault. Sensitivity analysis was performed to determine which probabilities in the model require refinement. Two case studies are included in this investigation.

• 공업화학전망
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• 제23권6호
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• pp.3-13
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• 2020

저출산 고령화 사회로의 진입은 대한민국뿐만 아니라 전 세계적으로 많은 사회 문제를 야기하고 있다. 그 중에서 고령 인구 증가로 인한 의료 수요 증가와 이를 뒷받침 할 의료인력 부족은 곧 다가올 사회문제이다. 4차 산업 혁명으로 인해 다양한 사회문제에 대한 혁신적인 해법들이 제시되고 있는데, 본 기고문에서는 다가올 고령화 사회에서 의료인력 부족 등에 의한 해결법으로 원격의료 지원을 위한 인공지능 활용을 다루고자 한다. 병 진단 및 예측을 위한 여러 가지 인공지능 알고리즘은 이미 많이 개발 되어 있으나, 일반적으로 딥러닝에 많이 쓰이는 인공신경망 구조인 합성곱 뉴럴네트워크(convolution neural network)나 기존 퍼셉트론(perceptron) 구조에서 벗어나 확률론적 인공신경망 중에 하나인 베이지안 뉴럴네트워크(Bayesian neural network)를 다루고자 한다. 그중에서 연산효율적이며 뉴로모픽 하드웨어로 구현 가능성이 높고 실제 진단 예측(diagnosis prediction) 문제 해결에 강점을 보이는 알고리즘으로써 naive Bayes classifer를 활용한 연구를 소개하고자 한다.

• 한국전기전자재료학회논문지
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• 제12권1호
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• pp.11-17
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• 1999

Aging characteristics of the crosslinked polyethylene have been measured after applying electrical, thermal and combined stresses. ICP and FT-IR measurements confirmed diffusion of low molecular weight components such as antioxidant and presence of carbonyl group. Carbonyl group of aged crosslinked polyethylene under combined stress was detected by FT-IR. As deterioration of the crosslinked polyethylene progresses, crystallinity degree and density decrease. Also, dielectric properties have been measured by tan $\delta$ and $\varepsilon$$_{r}$ measurements. The three-parameter Weibull distribution was found to be the best suited among other probabilistic distribution representing the dielectric breakdown strength of aged crosslinked polyethylene. The scale parameter and location parameter decreases as the applied stress increases. The shape parameter increases as the stress increases.s.

• 한국컴퓨터정보학회논문지
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• 제11권6호
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• pp.261-267
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• 2006

원자력발전소의 정량적 위험성 평가를 위해서 확률론적 안정성 평가 기법이 이용되고 있는데, 이를 위해서는 여러 가지 분야의 다양한 신뢰도 데이터가 필요하다. 이러한 신뢰도 자료 중에 인간의 지각 행위 및 수행 행위로부터 발생하는 인적 오류 확률은 그 특성상 실제 오류 확률을 얻기가 매우 어렵다. 따라서 인적 오류 확률을 구하기 위해서는 인간 신뢰도 분석 분야의 전문가들이 제안한 인간 신뢰도 분석 방법을 이용하여 인적 오류 확률을 추정한다. 한국 원자력 연구소에서는 이를 위해 인간의 지각 및 수행 행위에서 야기되는 인간 오류 사건을 관리하고 인적 오류 확률을 추정하기 위한 인간 신뢰도 분석 시스템을 개발하고 있다. 본 연구에서는 인간 신뢰도 분석 방법론 개발 및 이를 이용한 인간 신뢰도 분석 전산 지원 시스템의 개발 과정에 관하여 기술하였다.

• 한국구조물진단유지관리공학회 논문집
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• 제19권4호
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• pp.25-34
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• 2015

정밀안전진단시 콘크리트 강도를 추정하기 위한 방법으로 강도추정식을 이용한 방법이 많이 사용되며, 이용되는 추정식은 외국에서 이미 제안된 식이 대부분이고, 적용되는 추정식에 따라서 추정강도의 차이가 심하게 발생하며, 전반적으로 강도추정의 신뢰도가 낮아져 정밀 안전진단 결과의 신뢰성에도 상당한 영향을 미친다. 이런 문제점은 일부 국한된 부분에서 발생하게 되어 다수의 실험을 통하여 신뢰도를 높일 수 있다. 본 논문은 이와 같은 필요성을 포괄하기 위해 실내압축강도와 관련된 신뢰도 평가식을 제안하였다. 확률론적 기법을 이용하여 신뢰도 평가식의 유용성을 검증하였으며 실내압축강도와 추정압축강도의 추이 그래프를 비교하였다. 비교결과, 본 연구에서 제시된 신뢰도 평가식의 유용성을 확인하였다.

• Smart Structures and Systems
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• 제30권3호
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.