• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 춘계학술발표대회
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• 전기학회논문지P
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• 제66권1호
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• pp.27-32
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• 2017

This paper presents a Daubechies wavelet-based fault detection method for fault identification in transmission lines. After the Daubechies wavelet coefficients are calculated, the proposed algorithm has been implemented difference equation using C language. We have modeled a 154kV transmission line using the ATPDraw software and have acquired test data. In order to evaluate effects of DC offset, simulations carried out while varying an inception angle of the voltage $0^{\circ}$, $45^{\circ}$, $90^{\circ}$. For performance evaluation, fault distance was varied. As we can see from the off-line simulation, the proposed algorithm shows rapid and accurate fault detection. Also we can see the proposed algorithm is not affected by the fault inception angle change.

• 대한전기학회논문지:전력기술부문A
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• 제48권1호
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• pp.16-21
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• 1999

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault test, which was carried in Korean electric power systems, it was found that a arcing phenomenon occurred during the high level portion of conductor voltage in each cycle. In this paper, we propose a new method for detection of high impedance faults, which uses the arcing fault current difference during high voltage and low voltage portion of conductor voltage waveform. To extract this difference, we diveded one cycle fault current into equal spanned four data windows according to the magnitude of voltage waveform and applied fast fourier transform(FFT) to each data window. The frequency spectrum of current wavefrom in each portion are used as the inputs of neural network and is trained to detect high impedance faults. The proposed method shows improved accuracy when applied to staged fault data and fault-like load.

• 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.

• Journal of Electrical Engineering and Technology
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• 제3권4호
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• pp.484-490
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• 2008

In this paper, an integrated fault section detection and isolation strategy is proposed based on the application of the Distribution Automation System(DAS) utilizing advanced IT and communication technologies. The Feeder Remote Terminal Unit(FRTU) has been widely used to collect data in the Korean distribution system. The achieved data is adopted in this method for detecting multiple fault types. Especially in the case of single phase-to-ground fault, the fault section is detected by comparison of the zero-sequence current phase angle. The test results have verified the effectiveness of the proposed method in a radial distribution system through extensive simulations in Matlab/Simulink. Furthermore, a communication-based demo system identical to the simulation model has been developed, and it can be applied as an online monitoring and control program for fault section detection and isolation.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.224-229
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• 2011

In this paper, in order to improve safety of hybrid electric vehicle a fault detection algorithm is introduced. The proposed algorithm uses SVDD techniques. Two methods for learning a lot of data are used in this technique. One method is to learn the data incrementally. Another method is to remove the data that does not affect the next learning. Using lines connecting support vectors selection of removing data is made. Using this method, lot of computation time and storage can be saved while learning many data. A battery data of commercial hybrid electrical vehicle is used in this study. In the study fault boundary via SVDD is described and relevant algorithm for virtual fault data is verified. It takes some time to generate fault boundary, nevertheless once the boundary is given, fault diagnosis can be conducted in real time basis.

• 한국정밀공학회지
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• 제26권11호
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• pp.47-54
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• 2009

Spin coaters are the essential instruments in micro-fabrication processes, which apply uniform thin films to flat substrates. In this research, a spin coater diagnosis system is developed to detect the abnormal operation of TFT-LCD process in real time. To facilitate the real-time data acquisition and analysis, the circular-buffered continuous data transfer and the short-time Fourier transform are applied to the fault diagnosis system. To determine whether the system condition is normal or not, a steady-state detection algorithm and a frequency spectrum comparison algorithm using confidence interval are newly devised. Since abnormal condition of a spin coater is rarely encountered, algorithm is tested on a CD-ROM drive and the developed program is verified by a function generator. Actual threshold values for the fault detection are tuned in a spin coater in process.

• 제어로봇시스템학회논문지
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• 제17권1호
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• pp.72-77
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• 2011

The checkpointing scheme is a well-known technique to cope with transient faults in digital systems. This paper proposes an adaptive checkpointing scheme for the reliability improvement of real-time control systems with concurrent fault detection capability. With concurrent fault detection capability the effect of transient faults are assumed to be detected with no latency. The proposed adaptive checkpointing scheme is based on the reliability analysis of an equidistant checkpointing scheme. Numerical data show the proposed adaptive scheme outperforms the equidistant scheme from a reliability point of view.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.107_108
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• 2009

In this paper, a fault section detection algorithm to be considered variety measurement devices is proposed in ungrounded diatribution network. Ungrounded network is different from grounded netork. It's that a fault current doesn't generate when a single grounded fault by characteristic of ungrounded network. So, a fault section detection is very difficult. Thus, in this paper, a fault section detection method is proposed by data from variety measurement devices. The method is proved by matlap simulink.

• 자동차안전학회지
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• 제9권4호
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• pp.32-37
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• 2017

For automated vehicles, the integrity and fault tolerance of environment perception sensor have been an important issue. This paper presents radar, vision, lidar(laser radar) fusion-based fault detection algorithm for autonomous vehicles. In this paper, characteristics of each sensor are shown. And the error of states of moving targets estimated by each sensor is analyzed to present the method to detect fault of environment sensors by characteristic of this error. Each estimation of moving targets isperformed by EKF/IMM method. To guarantee the reliability of fault detection algorithm of environment sensor, various driving data in several types of road is analyzed.