• 한국건설관리학회논문집
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• 제13권5호
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• pp.35-44
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• 2012

건축물 유지관리 단계에서의 안전점검 및 정밀안전진단은 1, 2종 시설물에만 국한되어 있으나 중소규모 건축물이 전체 건축물의 99.4%를 차지할 정도로 많다는 점을 고려할 때, 중소규모 건축물의 안전진단은 반드시 필요하다. 이 연구에서는 현행 안전점검 및 정밀안전진단 체계의 문제점을 고찰하고, 중소규모 건축물에 적용할 수 있는 안전진단 모델을 개발하였다. 현행 평가방식을 고찰하고 설문조사를 통해 기존 상태평가 항목을 중소규모 건축물에 적합하게 조정하였다. 안전진단 모델 개발에 퍼지이론이 적용되었으며, 평가를 용이하게 하기 위해 웹기반 프로그램을 개발하였다. 이 연구의 결과를 실무에 적용한다면 1, 2종 이외의 중소규모 건축물도 안전점검 및 정밀안전진단을 효율적으로 수행할 수 있을 것이다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.474-477
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• 2002

Safety is very important to operate nuclear power plant. To guarantee the safety, nuclear power plant should be run without trouble. This paper presents the application of a failure diagnosis approach based on discrete event system theory to the pressurizer pressure control system for Pressurized Water Reactor. Also, this paper shows a scheme of failure diagnosis by distributed diagnoser.

• 한국안전학회지
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• 제7권4호
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• pp.105-114
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• 1992

This paper presents an study on the knowledge based system for diagnosing the fire causes using the Fuzzy Possibility Measure( FPM ) about the electric-fire ignition. The Ignition values needed for causes diagnosis is computed as FPM for electric-fire ignition based on the internal scale technique that assigns numerically the characteristic difference of facts to the-tin-ear scale. For the convinience of inference, ignition sources are classified into seven types : short, ground fault, leakge of electricity, overcurrent, cord junction overheating, bad Insulation and spark. The system for causes diagnosis of electric-fire is composed of Knowledge Acquisition System, Inference Engine and Man-Machine Interface, The diagnosis system is wrritten in an artificial intelligence langusge “PROLOG” which uses depth-first search and backward chaining schemes in reasoning process.

• 전기학회논문지P
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• 제67권3호
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• pp.160-167
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• 2018

In this paper, a black box, which is provided the reliability and user safety of home battery energy storage system connected with solar energy generation, is developed. In the developed scheme, a status and diagnosis data of battery management system, power conditioning system, solar energy generation and grid is measured. This status and diagnosis data is stored and displayed in the developed black box. In addition, this status and diagnosis data is stored and displayed in a monitoring system and a smart phone of user. A performance evaluation of the developed black box is carried out using emulator of home battery energy storage system connected with solar energy generation. Consequently, the developed black box is proved its superiority of the reliability and user safety.

• 전기학회논문지
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• 제65권7호
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• pp.1295-1302
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• 2016

In this paper, a black box, which is provided the reliability and user safety of home battery energy storage system connected with solar energy generation, is developed. In the developed scheme, a status and diagnosis data of battery management system, power conditioning system, solar energy generation and grid is measured. This status and diagnosis data is stored and displayed in the developed black box. In addition, this status and diagnosis data is stored and displayed in a monitoring system and a smart phone of user. A performance evaluation of the developed black box is carried out using emulator of home battery energy storage system connected with solar energy generation. Consequently, the developed black box is proved its superiority of the reliability and user safety.

• 한국가스학회지
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• 제21권5호
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• pp.64-69
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• 2017

한국은 세계 2위의 LNG 수입국이고 2017년 기준 약 70기의 LNG 저장탱크가 상업운전 중에 있다. LNG산업의 핵심설비인 LNG 저장탱크는 1986년 상업운전 이후 설계보증 연한인 30년이 경과 되면서 도시가스사업법 개정을 통해 2014년부터 정밀안전진단을 수행하고 있다. 정밀안전진단 시 LNG 저장탱크의 외조 콘크리트에 대해서 상태평가를 할 수 있는 기준이 부재한 상태이다. LNG 저장탱크의 구조적 특성과 기존 토목구조물 상태평가 기준 분석을 통해 저장탱크의 대표부재에 대한 상태평가기준을 개발하였다. 개발된 객관적 상태평가기준은 LNG 저장탱크의 안전성 및 신뢰성을 향상시키고 보수기준을 제시할 수 있다.

• 한국전기전자재료학회논문지
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• 제24권3호
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• pp.208-214
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• 2011

In this thesis, the partial discharge according to applied voltage and variations of cross-sectional area and length of the conductor related to general condition for using cable was measured in order to study degradation diagnosis for 2-Core cable of the PVC insulator used in industrial fields for other safety installations. Also the thermal degradation conditions under various installation circumstances of cables were studied by assuming degradation conditions with each different degradation rate (50%, 67%, 100%) such as variation in degradated temperature, thermal exposure time, normal state, partially degradated state and overall degradated state for thermal degradation diagnosis. The quantity of electric discharge (V-Q) according to applied voltage was measured for measurement of inception voltage and extinction voltage. The quantity of electric discharge and the number of electric discharge (Q-N) were measured with applied voltage kept constantly. In addition, pictures were taken using SEM (scanning electron microscope) to compare the surface of external insulator to degradated state of internal insulator according to thermal degradation temperature and also compare the surface of external insulator to degradated surface state of internal insulator according exposure time of cables to thermal stress.

• Nuclear Engineering and Technology
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• 제50권8호
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.2859-2870
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• 2022

Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN.

• 전기학회논문지P
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• 제55권4호
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• pp.190-195
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• 2006

In this paper, safety algorithm of transformer is introduced for the sake of MV/LV distribution customers by using fuzzy theory. The current-carrying capacity of transformer is usually determined by the maximum temperature at which the transformer is permitted to operate. Overload of transformer has an effect on transformer utilization rate and maximum temperature rises as well as maximum ambient temperature of insulating materials. Therefore, we proposed the safety algorithm considering the overload of transformer and ambient temperature in this paper. We introduced the correlational equations between each parameters using experimental data of IEEE std C57.91-1995, and deduced the result using fuzzy reasoning. We guessed the safety algorithm making a diagnosis for the safety status of oil-immersed transformer.