• 대한전자공학회논문지
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• 제22권1호
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• pp.7-13
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• 1985

쉽게 시험할 수 특성을 가진 SLA 의 설계에 관해 논하였다. 제안된 SLA는 hardware를 부가함으로써 회로의 상태를 쉽게 조절하고 점검할 수 있게 하였다. 제안된 SLA는 test pattern과 응답이 SLA에 구현된 함수에 관계없고 단지 SLA의 크기에 따라 유일하게 결정되는 매우 짧은 보편적인 test sequence를 갖는다. 여기서 고려된 SLA의 고장은 단일 및 다중 stuck faults, crosspoint faults 및 bridge faults이다. 또한 고장의 위치 판별 및 그 처리에 관해서도 고찰하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2013년도 추계학술대회 논문집
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• pp.63-64
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• 2013

Induction motors' faults detection is almost a popular topic among researchers. Monitoring the output of motors is a key factor in detecting these faults. (Short-time) Fourier, (continuous, discrete) wavelet, and extended Park vector transformations are among the methods for fault detection. One major deficiency of these methods is not being able to detect the severity of faults that carry low energy information, e.g. in ball bearing system failure, there is absolutely no way to detect the severity of fault using Fourier or wavelet transformations. In this paper, the authors have applied the Discrete Wavelet Transform (DWT) frequency-domain analysis to detect bearing faults in an induction motor. In other words, in discrete transform which the output signal is decomposed in several steps and frequency resolution increases considerably, the frequency-band analysis is performed and it will be verified that first of all, fault sidebands become more recognizable for detection in higher levels of decomposition, and secondly, the inner race bearing faults turn out easier in these levels; and all these matter because of eliminating the not-required high energy components in lower levels of decomposing.

• Journal of Power Electronics
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• 제8권2호
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제51권6호
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• pp.321-329
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• 2002

A protection scheme for series active compensator is presented and analyzed in this paper. The proposed series active compensator operated as a high impedance K($\Omega$) to the fundamentals when short-circuit faults occur in the power distribution system, and two control strategies are proposed in this paper The first is the method by detecting the fundamental source current through the p-q theory, the second is the method by detecting the fundamental component of load current in Synchronous Reference Frame(SRF). When the short-circuit faults occur in the power distribution system, the proposed scheme can protect the series active compensator without additional protection circuits. The validity of the Proposed Protection scheme was investigated through experimental results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.2075-2076
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• 2011

The stator winding faults diagnosis technique based on MCSA is as follows. Firstly, collecting the 3 phase motor currents, that signal is transformed by (d-q transform, $i_d$, $i_q$). Park's vector pattern, the circle that is down by d-q transformed currents($i_d$, $i_q$). The circle is widely used for stator winding faults detection. The current distortion ratio(DR), defined by the ratio of max-axis and min-axis of ellipse of Park's vector's pattern. In this study, distortion ratio of Park's vector pattern is suggested for Auto diagnosis of stator winding short fault and usefulness of distortion ratio is verified through simulation using LabVIEW program.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2012년도 전력전자학술대회 논문집
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• pp.617-618
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• 2012

Recently as the inactive response characteristics of the existing RCD(Residual Current protective Device) used on low voltage power distribution system, so control of overload and electric short circuit faults, major causes of electrical fires, are not enough. Therefore, this paper is proposed a prevention apparatus using neutral line voltage and semiconductor switching devices for the prevention of electrical disasters in low voltage power distribution system caused by overload or electric short circuit faults. The proposed prevention apparatus confirms the excellent characteristics in response velocity and accuracy in comparison with the conventional circuit breaker(RCD) through various operation performance analysis.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 추계학술대회 논문집
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• pp.478-479
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• 2007

Many electrical fires are occurred by leakage currents and sparks generated by a short circuit. Earth leakage circuit breakers (ELCBs) should be tripped at the moment of the faults mentioned above. In this paper, we described the tripping characteristics of ELCBs against parallel arcing faults. A diesel engine generator with the capacity of 375 kVA source was adopted to provide enough large current when a parallel arcing occurred. The experimental results showed that most ELCBs we experimented were not tripped against short-duration pulse currents produced by parallel arcing because the ELCBs are designed to be tripped by a large current with long duration similar to power frequency.

• 한국지반환경공학회 논문집
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• 제16권5호
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• pp.43-53
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• 2015

본 연구에서는 빅데이터 범주에 포함되는 각종 계측 데이터를 대상으로 각종 이상치를 판단하기 위한 기법을 고안하고, 인공 데이터 및 실 계측 데이터를 이용한 이상치 분석을 수행하였다. 계측결과에 대한 1차 차분 값 및 오차율을 적용한 규칙기반 방법은 큰 규모의 Short fault 분석 및 일정 기간 계측값에 변화가 발생하지 않는 경우의 Constant fault 분석에 효과적으로 적용될 수 있었으나, 독립적인 단일 데이터셋만을 이용하는 관계로 큰 변화폭을 보이는 실 계측 데이터의 정상 데이터를 이상치로 오판하는 문제점이 있었다. 규칙기반 방법을 이용한 Noise fault 분석은 적정 데이터 윈도우 사이즈의 선택 및 이상치 판정용 한계값 선정상의 문제로 인해 실 계측 데이터 적용에 한계가 있었다. 이종 데이터 간 상관분석 방법은 학습 데이터의 적정범위 선정이 선행된다면 장단기 계측 데이터의 이상 거동 및 국부적 이상치 판정에 매우 효과적으로 이용될 수 있음을 알 수 있었다.

• 마이크로전자및패키징학회지
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• 제13권1호통권38호
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• pp.31-35
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• 2006

본 논문에서는 전력용 반도체 스위칭 소자인 SCR(Silicon Controlled Rectifier)과 적층형 PTC 서미스터의 전기적 특성을 이용하여 각종 전기화재 및 전기사고를 예방 보호하는 제어시스템에 대해 연구된다. PTC(Positive Temperature Coefficient) 서미스터는 온도변화에 따른 저항특성이 정(+)온도계수를 갖는 특징이 있다. 이 PTC는 정온도계수를 갖는 $BaTiO_3$계 세라믹스의 정방정계-입방정계 구조를 가지고 상변이점인 큐리(Curie)온도 이상으로 온도가 증가하면 저항이 급격히 증가하는 현상을 보인다. 본 논문에서는 이러한 정저항 온도특성과 자체 발열특성을 갖는 적층형 PTC 서미스터를 전기단락사고나 과부하사고 등의 전기사고의 감지센서로 이용하여 전기화재사고로부터 보호하는 제어시스템에 대해 제안한다. 또한 제안된 보호제어시스템의 다양한 실험결과를 통해 이론적 해석의 타당성을 입증시킨다.

• Structural Monitoring and Maintenance
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• 제5권1호
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.