• 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.

• 한국콘텐츠학회논문지
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• 제10권7호
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• pp.429-437
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• 2010

최근 웹의 기술과 기능이 사용자중심의 패러다임으로 변화되면서 e-러닝의 연구와 설계에서도 학습자 참여와 지속적인 학습이 가능한 동적인 학습콘텐츠를 구성하려는 새로운 연구가 진행되고 있다. 본 논문에서는 e-러닝 2.0에 적용할 수 있도록 효율적인 학습 환경을 제공하기 위해 학습자 중심의 동적인 학습 콘텐츠 난이도 조절에 관한 연구를 기술했다. 본 논문은 학습자 중심의 콘텐츠를 제공하기 위해 DLA(Dynamic Level Adjustment)를 제안한다. 제안된 시스템은 환경의 변화에 적응력이 강한 학습콘텐츠를 조절하고 적용할 수 있는 가이드라인이 되고, 더 깊이 있는 연구가 진행될 수 있도록 목표를 두고 있다. 성능평가 결과 학습자의 다양한 학습패턴을 인지할 수 있는 동적인 학습콘텐츠 모델을 만들 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.157.4-157
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• 2001

An effective approach to detect and diagnose multiple failures in a dynamic system is proposed for the case where the measurement noise is correlated sequentially in time. It is based on the modified interacting multiple-model (MIMM) estimation algorithm in which a generalized decorrelation process is developed by employing the autoregressive (AR) model for the correlated measurement noise. Numerical example for the nuclear steam generator is provided to illustrate the enhanced performance of the proposed algorithm.

• 한국가스학회:학술대회논문집
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• 한국가스학회 2005년도 춘계학술대회 및 특별심포지움
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• pp.212-212
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• 2005

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문초록집
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• pp.372.2-372
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• 2002

This paper presents an efficient modeling method fur open cracked beam structures. An equivalent bending spring model is introduced to represent the structural weakening effect in the presence of open cracks. The proposed method adopts the exact dynamic element method (EDEM) to avoid the difficulty and numerical errors in association with re-meshing the structure. The proposed method is rigorously compared with a commercial finite element code. (omitted)

• 정보과학회 논문지
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• 제42권12호
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• pp.1575-1583
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• 2015

사물인터넷 컴퓨팅의 등장으로 다양한 사물인터넷 디바이스를 통해 사용자에 대한 건강 컨텍스트의 수집과 수집된 건강 컨텍스트를 분석하여 노화진단이 가능해졌다. 하지만, 기존의 노화진단 기법들은 서로 다른 고정된 노화진단요소들을 사용하여 사용자에 따라 획득 가능한 건강 컨텍스트의 가변성을 고려하지 않아서 새로운 노화진단요소의 추가 및 삭제에 대해 동적 대응이 힘들다. 본 논문에서는 다양한 사물인터넷 디바이스를 기반으로 노화진단에 필요한 다양한 노화진단 요소를 수집하고, 사용자마다 가변적인 노화진단 요소의 구성에 따라 동적으로 적응 가능한 노화진단 프레임워크의 기법 및 설계를 제안한다. 제안된 노화진단 프레임워크를 이용하면 획득 가능한 건강 컨텍스트의 가변성과 관계없이 노화진단기법의 적용이 가능하며, 노화진단 요소의 동적 추가 및 삭제가 가능하다.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권9호
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• 한국수소및신에너지학회논문집
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• 제34권5호
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• pp.478-489
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• 2023

In this study, fault diagnosis and detection methods developed to ensure the reliability of polymer electrolyte membrane (PEM) hydrogen electrolysis systems have been proposed. The proposed method consists of model development and data generation of the PEM hydrogen electrolysis system, and data-driven fault diagnosis learning model development. The developed fault diagnosis learning model describes how to detect and classify faults in the sensors and components of the system.

• 전기학회논문지
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• 제59권9호
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• pp.1673-1679
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• 2010

A photovoltaic (PV) generator is significantly regarded as one important alternative of renewable energy systems recently. Fault detection and diagnosis of engineering dynamic systems is a fundamental issue to timely prevent unexpected damages in industry fields. This paper presents an intelligent monitoring approach and fault detection technique for PV generator systems by means of artificial neural network and statistical signal detection theory. We devise a multi-Fourier neural network model for representing dynamics of PV systems and apply a general likelihood ratio test (GLRT) approach for investigating our decision making algorithm in fault detection and diagnosis. We make use of a test-bed of ubiquitous sensor network (USN) based PV monitoring systems for testing our proposed fault detection methodology. Lastly, a real-time experiment is accomplished for demonstrating its reliability and practicability.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2504-2510
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• 2018

Actuator failures and the failures of controlled objects are often considered together. To overcome this limitation, a class of sliding mode observers for the fault diagnosis of nonlinear systems is designed in this paper. Due to the influence of the sliding mode function, the control strategy and the residual change of the observer exhibit certain trends governed by specific relations. Therefore, according to the changes in the control strategy and the observer residuals, the sensor and actuator faults in nonlinear systems can be determined. Finally, the effectiveness of the proposed method is verified based on simulations of a DC motor system.