• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1558-1565
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• 2015

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in three-phase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.

• 한국지능시스템학회논문지
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• 제18권4호
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• pp.518-523
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• 2008

본 논문에서는 저압 배선 진단 시스템 개발에서 핵심 파트 중 하나인 지능형 차단 시스템 구축을 목표로 한다. 제안된 진단 시스템은 TFDR (Time-Frequency Domain Reflectometry) 알고리즘을 바탕으로 하여 실제 전압이 흐르는 배선에 대해 이상 거리 측정을 하게 된다. 그리고 배선으로부터 얻은 정보를 바탕으로 배선 이상의 종류를 분석하는 것이 지능형 차단 시스템의 목표이다. 효율적인 분석을 위해, 본 논문에서는 퍼지-베이시안 (Fuzzy-Bayesian) 알고리즘을 바탕으로 하여 시스템을 구성하였다. 실제 저압 배선에서 실험된 데이터를 바탕으로 한 실험을 통해 제안된 기술의 우수성을 입증하고자 한다.

• 전기전자학회논문지
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• 제24권1호
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• pp.132-139
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• 2020

공기청정기에 사용되는 이오나이저 모듈은 권선형 transformer를 사용하여 방전전극인 HV+/HV-에 3.5KV/-4KV의 고전압을 공급하여 carbon fiber brush의 전계 방사에 의해 양이온과 음이온을 발생시킨다. 기존의 MCU를 이용한 이오나이저 모듈 회로는 PCB 사이즈가 크고 가격이 비싼 단점이 있고, 기존의 ring oscillator를 이용한 게이트 구동 칩은 oscillation 주기가 PVT(Process-Voltage-Temperature) 변동에 민감하고 HV+와 GND, HV-와 GND의 단락에 의한 fault detection 기능이 없으므로 화재나 감전의 위험이 있다. 그래서 본 논문에서는 7bit binary UP counter를 이용하여 PVT 변동이 있더라도 oscillation 주기를 조절하여 HV+ 전압이 목표 전압에 도달하게 한다. 그리고 HV+와 GND 사이의 단락을 검출하기 위한 HV+ short fault detection 회로, HV-와 GND 사이의 단락을 검출하기 위한 HV- short fault detection 회로와 HV+가 과전압 이상으로 올라가는 것을 검출하기 위한 OVP(Over-Voltage Protection) 회로를 새롭게 제안하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권6호
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• pp.396-402
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• 2005

The software reliability growth model(SRGM) has been developed in order to evaluate such measures as remaining fault number, fault rate, and reliability for the developing stage software. Most of the study literatures assumed that this detecting efficiency was perfect. However the actual fault detecting is generally imperfect, and widely known to many persons. It is not easy to develop and remove the fault existing in the software because the fault finding is difficult, and the exact solving method also not easy, and new fault may be introduced depending on the tester's capability. There, the fault removing efficiency influences the software reliability growth or developing cost of software. It is a very useful measure throughout the developing stage, much helpful for the developer to evaluate the debugging efficiency, and evaluate additional workload. Hence, the study for the imperfect debugging is important in point of software reliability and cost. This paper proposes that the fault debugging is imperfect and new fault may be introduced for the developing software during the developing stage.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 전기설비전문위원
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• pp.205-207
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• 2009

In 2007, 9,128 fires are attributed to electrical equipments. These fires resulted in 29 deaths and 262 injuries. Arc-faults were one of the major causes of these fires. When an unintended arc-fault occurs, it generates intense heat that can easily ignite surrounding combustibles. Conventional circuit breakers only respond to overloads, short circuits, and leakage currents. Therefore, the breakers do not protect against arcing conditions. This paper presents results obtained in experiments on ignition behavior of wire by series arc fault currents and techniques developed to detect the arc-faults. The developed technique was tested after installation to make sure they are working properly and protecting the circuit. If the developed arc detecting technique is applied, the electrical fires caused by an arc-fault can be reduced.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2016년도 전력전자학술대회 논문집
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• pp.471-472
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• 2016

Since photovoltaic generating system is significantly important among renewable energy sources, photovoltaic plants are installed more than past. As a result, accidents of photovoltaic system are also increased, so the additional hardware which includes monitoring system and periodic inspection are required for safety. In addition, a photovoltaic system is installed where a person can't approach to detect a fault, so a number of devices are required to detect it. This paper proposes that drone and thermo-graphic camera are used for detecting a fault of photovoltaic plant and suggests efficiency to control a drone for detecting a photovoltaic plant.

• 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.

• 조명전기설비학회논문지
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• 제29권5호
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• pp.71-79
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• 2015

Recently, new Low Voltage DC (LVDC) power distribution systems have been constantly researched as uses of DC in end-user equipment are increased. As in conventional AC distribution system, High Impedance Fault (HIF) which may cause a failure of protective relay can occur in LVDC distribution system as well. It, however, is hard to be detected since change in magnitude of current due to the fault is too small to detect the fault by the protective relay using overcurrent element. In order to solve the problem, this paper presents an algorithm for detecting HIF using accumulated energy in LVDC distribution system. Wavelet Singular Value Decomposition (WSVD) is used to extract abnormal high frequency components from fault current and accumulated energy of high frequency components is considered as the element to detect the fault. LVDC distribution system including AC/DC and DC/DC converter is modeled to verify the proposed algorithm using ElectroMagnetic Transient Program (EMTP) software. Simulation results considering various conditions show that the proposed algorithm can be utilized to effectively detect HIF.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1765-1772
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• 2015

The integrity of the gas insulated switchgear (GIS) is vital to the safety of an entire power grid. However, there are some limitations on the techniques of detecting and diagnosing partial discharge (PD) induced by insulation defects in GIS. This paper proposes a joint electro-chemical detection method to resolve the problems of incomplete PD data source and also investigates a new unique fault diagnosis method to enhance the reliability of data processing. By employing ultra-high frequency method for online monitoring and the chemical method for detecting SF6 decomposition offline, the acquired data can form a more complete interpretation of PD signals. By utilizing DS evidence theory, the diagnostic results with tests on the four typical defects show the validity of the new fault diagnosis system. With higher accuracy and lower computation cost, the present research provides a promising way to make a more accurate decision in practical application.

• 한국지형학회지
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• 제26권2호
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• pp.69-84
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• 2019

The purpose of this study is to extract lineaments in the southeastern part of the Gaeum Fault System, and to understand their characteristics and a relationship between them and fault activation. The lineaments were extracted using a multi-layered analysis based on a digital elevation model (5 m resolution), aerial photos, and satellite images. First-grade lineaments inferred as an high-activity along them were classified based on the displacement of the Quaternary deposits and the distribution of fault-related landforms. The results of classifying the first-grade lineaments were verified by fieldwork and electrical resistivity survey. In the study area of 510 km², a total of 222 lineaments was identified, and their total length was 333.4 km. Six grade lineaments were identified, and their total length was 11.2 km. The lineaments showed high-density distribution in the region along the Geumcheon, Gaeum, Ubo fault, and a boundary of the Hwasan cauldron consisting the Gaeum Fault System. They generally have WNW-ESE trend, which is the same direction with the strike of Gaeum Fault System. Electrical resistivity survey was conducted on eight survey lines crossing the first-grade lineament. A low-resistivity zone, which is assumed to be a fault damage zone, has been identified across almost all survey lines (except for only one survey line). The visual (naked eyes) detecting of the lineament was evaluated to be less objectivity than the automatic extraction using the algorithm. However, the results of electrical resistivity survey showed that first-grade lineament extracted by visual detecting was 83% reliable for inferred fault detection. These results showed that objective visual detection results can be derived from multi-layered analysis based on tectonic geomorphology.