• 한국소음진동공학회논문집
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• 제23권9호
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• pp.814-822
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• 2013

For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• Journal of Electrical Engineering and Technology
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• 제4권3호
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• pp.389-395
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• 2009

Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

• 전기학회논문지
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• 제56권4호
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• pp.651-659
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• 2007

This paper proposes an improved fault indication algorithm in a distribution automation system. A conventional fault indication method, so called YES-NO algorithm, could generate wrong informations under certain conditions such as line to ground fault, large motor double circuit line. In order to prevent mal-operation of fault indicator, direction of fault current are used as well as magnitude. The feasibility of the proposed algorithm has been testified by computer simulation using Matlab power system toolbox.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2198-2202
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• 2005

In this paper we have presented a condition monitoring method of check valve using neural network. The acoustic emission sensor was used to acquire the condition signals of check valve in direct vessel injection (DVI) test loop. The acquired sensor signal pass through a signal conditioning which are consisted of steps; rejection of background noise, amplification, analogue to digital conversion, extract of feature points. The extracted feature points which represent the condition of check valve was utilized input values of fault diagnosis algorithms using pre-learned neural network. The fault diagnosis algorithm proceeds fault detection, fault isolation and fault identification within limited ranges. The developed algorithm enables timely diagnosis of failure of check valve’s degradation and service aging so that maintenance and replacement could be preformed prior to loss of the safety function. The overall process has been experimented and the results are given to show its effectiveness.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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• pp.136-144
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• 1999

This paper deals with the fault detection problem in uncertain linear multivariable systems and its application. A robust fault detection method presented by Kim et a. (1998) for MIMO (Multi Input/Multi Output) systems has been adopted and applied to the twin rotor MIMO experimental setup using industrial DSP. The system identification problem is formulated for the twin rotor MIMO system and its parameters are estimated using experimental data. Based on the estimated parameters, some fault detection simulations are performed using the robust fault detection method, which shows that the preformance is satisfied.

• 조명전기설비학회논문지
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• 제26권5호
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• pp.34-39
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• 2012

The DAS(Distribution Atomization System) determines a faulted section by using a FI(Fault Indicator) when the fault is occurred on the distribution networks. Sometimes FI is malfunction when the ground fault is occurred on a the distribution networks. As a result difficulties to make decision of faulted section. The cause of the FI malfunction is that the determination using the limited information of the installed area. In this study, a method is proposed to determine faulted section using the amount of the fault current instead of using the FI. This method is determinated faulted section using the fuzzy inference for the collected information from the all switches. The usefulness of the proposed algorithm is verified through the simulation test using PSCAD/EMTDC.

• 전력전자학회논문지
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• 제16권2호
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• pp.191-198
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• 2011

본 논문에서는 영구자석 동기전동기 구동용 인버터 스위치에서 개방성 고장이 발생하여도 구동 성능을 유지하기 위한 고장진단 기법이 제안 되었다. 제안한 고장진단 기법은 확장형 칼만필터에 의해 실시간으로 추정된 고정자 저항이 개방성 고장발생 시 고장발생 위치에 따라서 다르게 추정되는 것을 이용하여 고장을 진단한다. 고장진단을 위한 제어 알고리즘을 별도의 하드웨어 구성없이 기존의 제어 프로그램에 추가함으로써 비용을 저감 시킬 수 있으며 추정된 고정자 저항은 상수 변동에 영향을 받는 제어기의 전동기 상수로 사용함으로써 제어 성능을 향상 시킬 수 있다. 제안한 고장진단 기법의 타당성은 시뮬레이션과 실험을 통하여 검증하였다.

• 한국안전학회지
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• 제32권3호
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• pp.160-171
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• 2017

US national research laboratories developed the first Vital Area Identification (VAI) method for the physical protection of nuclear power plants that is based on Event Tree Analysis (ETA) and Fault Tree Analysis (FTA) techniques in 1970s. Then, Korea Atomic Energy Research Institute proposed advanced VAI method that takes advantage of fire and flooding Probabilistic Safety Assessment (PSA) results. In this study, in order to minimize the burden and difficulty of VAI, (1) a set of streamlined VAI rules were developed, and (2) this set of rules was applied to PSA fault tree and event tree at the initial stage of VAI process. This new rule-based VAI method is explained, and its efficiency and correctness are demonstrated throughout this paper. This new rule-based VAI method drastically reduces problem size by (1) performing PSA event tree simplification by applying VAI rules to the PSA event tree, (2) calculating preliminary prevention sets with event tree headings, (3) converting the shortest preliminary prevention set into a sabotage fault tree, and (4) performing usual VAI procedure. Since this new rule-based VAI method drastically reduces VAI problem size, it provides very quick and economical VAI procedure. In spite of an extremely reduced sabotage fault tree, this method generates identical vital areas to those by traditional VAI method. It is strongly recommended that this new rule-based VAI method be applied to the physical protection of nuclear power plants and other complex safety-critical systems such as chemical and military systems.

• 지질공학
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• 제33권1호
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• pp.121-136
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• 2023

Microstructural characterization, identification of mineral assemblages, and K-Ar age dating of fault gouges from five Quaternary fault sites segmented along the northern Yangsan Fault, SE Korea were performed to understand formation condition and multiple activity of faults. The mean and median sizes of particles of bulk gouges vary among the studied faults: 1.75 ㎛ and 1.43 ㎛ for the Danguri Fault, 1.94 ㎛ and 1.79 ㎛ for the Yukjae Fault, 5.57 ㎛ and 4.16 ㎛ for the Yugye Fault, and 5.55 ㎛ and 2.31 ㎛ for the Bogyeongsa Fault. Fault gouges contain abundant secondary minerals, including smectite, chlorite, illite, kaolinite, laumontite, and mordenite, which are found in association with quartz and feldspar. K-Ar dating of the fault gouges (both bulk samples and separate size fractions) yields ages ranging from 59.1 to 18.8 Ma, with bulk ages of 47.6 Ma for the Yukjae Fault, 59.1 Ma for the Ansim Fault, 39.4 Ma for the Yugye Fault, and 22.6 Ma for the Bogyeongsa Fault. The finer fractions generally have younger K-Ar ages compared with the coarser fractions, and the finest fraction (<0.2 ㎛) is the youngest for each fault. Hydrothermal alteration of the gouges is considered to have occurred under low-temperature (100~200℃) conditions during faulting. Microstructural features and clay mineral assemblages of fault gouges and brecciated rocks should be considered when interpreting fault events and reactivation, in addition to age dating of faulting.