• Journal of the Korean Society of Industry Convergence
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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.

• The Journal of Korean Association of Computer Education
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• 제6권2호
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• pp.33-40
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• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development costs because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is mere important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing causes of the prediction results and low extendability. This paper builds a new prediction model, GAM, based on Genetic Algorithm. GAM is different from other models because it produces a criticality function. So GAM can be used for comparison between entities by criticality. GAM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering Internal characteristics and accuracy of prediction.

• Journal of the Semiconductor & Display Technology
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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.

• Journal of the Korean Geotechnical Society
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• 제15권4호
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• pp.99-112
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• 1999

When there exists a fault zone ahead of the tunnel face and a tunnel is excavated without perceiving its existence, it will cause stress concentration in the region between the tunnel face and the fault zone because of the influence of the fault zone on the arching phenomena. Because the underground structure has many unreliable factors in the design stage, the prediction of a fault zone ahead of the tunnel face by monitoring plans during tunnel construction and the rapid establishment of appropriate support system are required for more economical and safer tunnel construction. Recent study shows that longitudinal displacement changes during excavation due to the change of rock property, and if longitudinal displacement and settlement, which are measured in the field, are considered together in displacement analysis, the prediction of change in rock mass property is possible. This study provided the method for the prediction of fault zones by analyzing the changes of L/C and (Ll-Lr)/C ratio (L= longitudinal displacement at crown, C = settlement at crown, Ll = longitudinal displacement at left sidewall, Lr = longitudinal displacement at right sidewall) and the stereographic projection of displacement vectors which were obtained from the 3-D numerical analysis of hybrid method in various initial stress conditions.

• Journal of Information Technology Services
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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.

• The Transactions of the Korea Information Processing Society
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• 제7권5호
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• pp.1401-1408
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• 2000

Critical prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development cost because the problems in early phases largely affected the quality of the late products. Real-time systems such as telecommunication system are so large that criticality prediction is more important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing cause of the prediction results and low extendability. In this paper, we propose a criticality prediction model using fuzzy rulebase constructed by genetic algorithm. This model makes it easy to analyze the cause of the result and also provides high extendability, high applicability, and no limit on the number of rules to be found.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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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.

• Journal of the Korean Society for Railway
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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.