• 한국산업융합학회 논문집
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• 제23권5호
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.

• 컴퓨터교육학회논문지
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• 제6권2호
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• pp.33-40
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• 2003

소프트웨어 개발 초기 단계의 문제점이 개발 후반부 산물의 품질에 심각한 영향을 미치기 때문에 설계 명세를 이용하여 결함경향성이 많은 부분을 예측하는 위험도 예측 모델은 전체 시스템 개발비용을 낮추는 데 중요한 역할을 하고 있으며, 이러한 예측 모델은 결과 산물이 매우 크고 실행 정확성이 요구되는 통신 소프트웨어 같은 실시간 시스템 설계에 더욱 필요하다. 판별분석, 인공신경망, 분류트리 등의 기법들을 이용한 모델들이 제안되었으나 이들은 결과에 대한 원인 분석의 어려움, 낮은 확장성 등의 문제점들을 지니고 있었다. 본 논문에서는 유전자 알고리즘을 이용한 새로운 모델인 GAM을 제안한다. GAM은 위험도 함수를 만들어 내므로 기존의 분류 모델들과는 다르게 설계 개체의 위험도 비교에도 사용가능하다. 여러 내부 특성들과 예측 정확도 비교를 통해 GAM을 잘 알려진 예측 모델인 역전파 신경망 모델(BPM)과 비교하였다.

• 반도체디스플레이기술학회지
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• 제18권4호
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• pp.87-91
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• 2019

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.

• 한국지반공학회논문집
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• 제15권4호
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• pp.99-112
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• 1999

막장 전방에 파쇄대 등의 불연속면이 존재할 경우, 이를 미리 예측하지 못한채로 굴진을 하게 되면 파쇄대로 인해 터널 굴진에 따라 발생된 종방향 아칭에 영향을 주어 막장면 전방에 응력이 집중하게 된다. 터널 및 지하공간의 설계시에는 불확실한 설계요소를 과다하게 내포하고 있으므로 경제적이고 안정성이 확보된 터널 시공을 위해서는 터널 막장면에서의 정확한 계측으로 막장 전방의 파쇄대를 예측하여 터널 지보체계에 신속히 대비함이 필요하다. 최근의 연구결과에 의하면 3차원 절대변위계측에 의해 터널의 시공 시 굴진에 따라 지반의 강도차이로 인해 발생된 종방향 변위의 변화를 측정하여 막장 전방의 불연속면을 미리 예측할 수 있다고 하였다. 본 연구는 혼합법을 사용한 3차원 수치해석으로부터 얻어지는 변위로부터 L/C (천단부의 종방향 변위[L]와 천단부의 침하량[C]의 비 )와 S/C (측벽의 수평방향 변위[S]와 천단부의 침하량[C]의 비), (Ll-Lr)/C (좌측벽의 종방향변위[Ll]와 우측벽의 종방향변위[Lr]의 차와 천단부의 침하량[C]의 비), 평사투영법을 중심으로 지반에 파쇄대가 존재할 경우에 대해 여러 가지 초기 지중응력조건에서 터널 굴착에 따른 3차원 절대 변위를 분석하여 그 존재를 예측할 수 있는 기법을 제시하였다.

• 한국IT서비스학회지
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• 제10권2호
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• 한국정보처리학회논문지
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• 제7권5호
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• pp.1401-1408
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• 2000

소프트웨어 개발 초기 단계의 문제점이 개발 후반부 산물의 품질에 심각한 영향을미치기 때문에 설계 명세를 이용하여 위험 부분을 예측하는위험도 예측 모델은 전체 시스템 개발비용을 낮추는 데 중요한 역할을 하고 있으며, 이러한 예측 모델은 결과 산물이 매우 크고 실행 정확성이 요구되는통신 소프트웨어 같은 실시간 시스템 설계에 더욱 필요하다. 판별분석, 인공신경망, 분류트리 등의 기법들을 이용한 모델들이 제안되었으나 이들은 결과에 대한 원인 분석의 어려움, 낮은 확장성 등의 문제점들을 지니고 있었다. 본 논문에서는 유전자 알고리즘에 의해 구축된 퍼지 규칙 베이스를 이용한 위험도 예측 모델을 제안한다. 제안 모델은 예측 결과에 대한 원인 분석이 용이하고 높은 확정성과 적용성을 지니고 규칙수에 대한 제안이 없다. 이러한 내부특성들 비교의 모의 실을 통한 예측 정확도 비교를 통해 제안 모델이 타 모델들보다 우수함을 보였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2008년도 추계학술대회 논문집
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• pp.363-366
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• 2008

It was accomplished that failure analysis not only failure numbers but also power system components every years. and these informations help power system operation considerably. power system equipment were occurred a break down by natural phenomenon and aging but it was not able to predict this failure number. But many papers and technical repots study for each equipment failure rate and reliability evaluation methods. so this paper show a failure number prediction whole power system component using Markov theory not each component failure probability. the result present a next month system failure number prediction.

• 대한전기학회논문지:전력기술부문A
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• 제54권6호
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• pp.315-322
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• 2005

In this article, 1 would like to explore the estimation method of time-varying voltage sags in large industrial systems considering the short circuit contributions of rotating machines. For the power distribution system of KEPCO(Korea Electric Power Corporation), the magnitude of initial symmetrical short circuit current is generally not changed. However, in industrial systems which contain a number of rotating machines, the magnitude of voltage sag is generally changed from the initial to the clearing time of a fault due to the decreasing contribution of rotating machines for a fault current. The time-varying characteristics of voltage sags can be calculated using a short circuit analysis that is considered the time-varying fault currents. For this, the prediction formulations of time-varying voltage sags are proposed using a foreign standard. The proposed method contains the consideration of generator and motor effects. For the test of proposed formulations, a simple system of industrial consumer is used for the comparison conventional and proposed estimation method of voltage sag characteristics.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 하계학술대회 논문집
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• pp.763-766
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• 2000

In this paper, stator form-winding sample coils based on silicone resin and polyimide were made for fault prediction and reliability estimation on the 200 Class insulation system of traction motors. The complex accelerative degradation was performed by periods during 10 cycles, which was composed of thermal stress, fast rising surge voltage, vibration, water immersion and overvoltage applying. After aging of 10 cycles, condition diagnosis test such as insulation resistance & polarization index, capacitance & dielectric loss and partial discharge properties were investigated in the temperature range of 20∼160$^{\circ}C$. Relationship among condition diagnosis test was analyzed to find an dominative degradation factor and an insulation state at end-life point.

• 한국철도학회논문집
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• 제8권2호
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• pp.170-175
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• 2005

This study presents a prediction of a failure rate in a safety required system that consists of a embedded control system, requiring a satisfaction of a quantitative safety requirement. International Standards are employed to achieve a regular procedures in the whole life cycle of a system, for the purpose of a prediction and a evaluation of a fault that might be able to be happened in a system. This International Standards uses SIL (Safety Integrity Level) to evaluate a safety level of a system. SIL is divided into 4 levels, from level 1 to level 4, and each level has functional failure rate and dangerous failure rate of a system. In this paper we describe the conventional method to predict the dangerous failure rate and propose a method using hazard analysis to predict the dangerous failure rate. The conventional method and the technique using hazard analysis to predict the dangerous failure rate are made a comparison through the control modules of the interlocking system in KTX. The proposed method verify better effectiveness for the prediction of the dangerous failure rate than that of the conventional method.