• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(2)
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• pp.137-140
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• 2000

In this paper, we have proposed an efficient test generation method for asynchronous circuits. The test generation is based on specification level, especially on Signal Transition Graph(STG)〔1〕 which is a kind of specification method for asynchronous circuits. To conduct a high-level test generation, we have defined a high-level fault model, called single State Transition Fault(STF) model on STG and proposed a test generation algorithm for STF model. The effectiveness of the proposed fault model and its test generation algorithm is shown by experimental results on a set of benchmarks given in the form of STG. Experimental results show that the generated test for the proposed fault model achieves high fault coverage over single input stuck-at fault model with low cost. We have also proposed extended STF model with additional gate-level information to achieve higher fault coverage in cost of longer execution time.

• 전기학회논문지P
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• 제58권3호
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• pp.362-368
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• 2009

Electrical fires have been occurred continuously in spite of installing ELB. Therefore the concern with the electrical arc-fault that cause the fire has growing. This paper measured series arc fault currents by the method of arc generator test in UL standard 1699. The used analysis methods in this paper are three different ways using DWT(discrete wavelet transform) those are frequently used for the arc fault current signal analysis. The arc fault detection probability is 100 % by method using noise-energy/shoulder-duration ratio of approximation coefficient. As these results, the variation of noise-energy and shoulder-duration ratio of approximation coefficient are founded important factors for the analysis of arc fault.

• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.500-504
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• 2007

In this paper, the induction motor rotor fault diagnosis system using current signals, which are measured using axis-transformation method, and speed, which is estimated using current information, are presented. In inverter-fed motor drives unlike line-driven motor drives the stator currents have numerous harmonics components and therefore fault diagnosis using stator currents is very difficult. The current and speed signal for rotor fault diagnosis needs to be precise. Also, high resolution information, which means the diagnosis system, demands additional hardware such as low pass filter, high resolution ADC, encoder and etc. Therefore, the proposed axis-transformation and speed estimation method are expected to contribute to low cost fault diagnosis systems in inverter-fed motor drives without the need for an encoder and any additional hardware. In order to confirm validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation using Park transformation and speed estimation method are compared with the results obtained from fast Fourier transforms.

• 조명전기설비학회논문지
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• 제28권12호
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• pp.30-37
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• 2014

Recently, Concerns of imbalance for power supply has been raised by the increase of electric power consumption, but the interest of electric safety is still lacking due to the incompleteness of regulations. Particularly, Arcing fault, direct cause of electric fire accidents, is difficult to detect, interrupt due to unformal signal behaviour in previous research and apply to real system. In this paper, Arcing fault simulator device was fabricated to investigate the characteristics of series arcing fault and simulated the faults in various real load. Also, this study are analysed arcing fault signal by using the harmonic analysis of the acquired data from the current of the power supply and proposed the methods to prevent the series arcing fault accidents in advance.

• 대한전기학회논문지
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• 제45권2호
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• pp.167-174
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• 1996

In this paper, a fault location algorithm is presented, which uses novel signal processing techniques and takes a new paradigm to overcome some drawbacks of the conventional methods. This new method for fault location on electric power transmission lines uses only one-terminal fault signals. The main feature of the method is hat it uses the high frequency components in fault signal and considers the influence of the source network by using a traveling wave propagation characteristics. As a result, we can develop a high speed, good accuracy fault locator.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1850-1851
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• 2011

The faults of a electric motor cause to rise the maintenance and repair cost and to reduce the reliability of the electric power system. In this paper, the auto fault detection system for a induction motor is developed using the torque signal spectrum analysis. The spectrum of motor torque signal is used for finding a bearing fault feature frequency. A threshold value, for detecting the motor bearing fault is set by the difference of torque signal spectrum(FFT signal) between normal condition and faulted condition of the motor.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2008년도 춘계학술대회 논문집
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• pp.572-578
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• 2008

As the railway transportation is getting faster and its operation speed has increased rapidly, its signal control has been complicated. For real time signal processing it is very important to prohibit any critical error from causing the system to malfunction. Therefore, handling complicated signals effectively while maintaining fault-tolerance capability is highly expected in modern railway transportation industry. In this paper, we suggest an SoC (Sytem-on-Chip) design method to integrate these complicated signal controlling mechanism with fault tolerant capability in a single chip. We propose an SoC solution which contains a high performance 32-bit embedded processor, digital filters and a PWM unit inside a single chip to implement ATO's, ATC's, ATP's and ATS's digital signal-processing units. We achieve an enhanced reliability against the calculation error by adding fault tolerance features to ensure the stability of each module.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2106-2117
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• 2017

An effective statistical feature extraction approach of data sampling of fault in the combined transmission system is presented in this paper. The proposed algorithm leads to high accuracy at minimum cost to predict fault location and fault type classification. This algorithm requires impedance measurement data from one end of the transmission line. Modal decomposition is used to extract positive sequence impedance. Then, the fault signal is decomposed by using discrete wavelet transform. Statistical sampling is used to extract appropriate fault features as benchmark of decomposed signal to train classifier. Support Vector Machine (SVM) is used to illustrate the performance of statistical sampling performance. The overall time of sampling is not exceeding 1 1/4 cycles, taking into account the interval time. The proposed method takes two steps of sampling. The first step takes 3/4 cycle of during-fault and the second step takes 1/4 cycle of post fault impedance. The interval time between the two steps is assumed to be 1/4 cycle. Extensive studies using MATLAB software show accurate fault location estimation and fault type classification of the proposed method. The classifier result is presented and compared with well-established travelling wave methods and the performance of the algorithms are analyzed and discussed.

• 한국전자통신학회논문지
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• 제3권4호
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• pp.260-265
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• 2008

수용가 시스템에서 발생하는 아크고장은 전기화재의 직접적인 원인이 되지만 기존의 고장검출 메커니즘으로는 확인이 어렵다. 따라서 본 연구에서는 전기화재의 주 원인이 되고 있는 아크고장의 원인을 분석하고 아크고장 기본 검출 메커니즘을 설계하였다. 또한 아크고장 판단의 정확도를 제고시킴으로써 오동작을 최소화할 수 있는 신호처리 기반의 아크고장 판단 방법론을 제안하였다. 끝으로, 하나의 돌입전류에 대해 이산 프리에 변환(DFT)과 이산 웨이블릿 변환(DWT)을 적용, 그 특성을 분석함으로써 신호척리 기반 고장판단 기법의 적용 방법론을 보였다.

• 정보처리학회논문지B
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• 제18B권3호
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• pp.147-156
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• 2011

본 논문에서는 유도전동기 고장 검출 및 분류를 위한 3-단계 (고장 신호의 전 처리, 고장 신호의 특징 추출, 고장 신호의 고장 유형별 분류) 알고리즘을 제안한다. 먼저 전 처리 단계에서는 저역 통과 필터를 통해 취득한 신호의 고주파 대역에 영향을 미칠 수 있는 잡음 성분을 제거하며, 다음으로는 이산 코사인 변환(discrete cosine transform)과 통계적 방법을 이용하여 고장 유형별 신호의 특징을 추출하고, 마지막 단계에서는 추출된 특징을 입력으로 하는 역 전파 신경 회로망(back propagation neural network)를 이용하여 신호를 고장 유형별로 분류한다. 시스템의 성능을 평가하기 위해 모의실험에 사용된 신호는 유도전동기의 진동 신호로, 정상 및 각종 이상 상태에 대해 8kHz의 샘플링율을 갖는 1초 길이의 데이터를 사용하였다. 모의실험 결과, 제안한 알고리즘은 학습된 상황의 고장 분류에서는 100%의 정확도를 보였으며, 기존의 공분산을 이용한 고장 검출 및 분류 알고리즘과 비교하여 약 50%의 정확도 향상을 보였다. 또한 고장 신호 취득 시 사용하는 센서의 종류나 주변 환경으로 인해 잡음이 추가될 수 있는 상황을 고려하여 취득한 데이터에 백색 가우시안 잡음을 인위적으로 추가한 모의실험에서도 98%이상의 고장 분류 정확도를 보였다. 더불어, 본 논문에서는 TI사의 TMS320F2812 디지털 신호 처리기에 제안한 고장 검출 및 분류 알고리즘을 탑재하여 실제 산업현장에서의 사용여부를 검증하였다.