• 한국소음진동공학회논문집
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• 제18권1호
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• pp.47-56
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• 2008

This paper presents a detecting technique for the improvement in quality by appling the various vibrational characteristics theory. The object of this study is to objectively point out faulty gear by developing the program which can be used to analyze and predict the vibrational characteristics caused by gear wear, deformation and nick of auto-transmission. The fault detection methods by vibrational signal analysis of gear have been progressed in the various fields of industry. These methods have the advantage of being easy to attach the accelerometer without discontinuance of the structure. But not all the methods are efficient for finding early faults. So in the thesis, we completed development of the inspection system of vibration by appling the most efficient detecting methods and verified the system's reliability through experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 춘계학술대회 논문집 전기설비전문위원
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• pp.166-168
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• 2007

The DGA (Dissolved Gases Analysis) technique has been widely using for fault diagnosis of the power transformers. This technique has a high reliability than other techniques. KEPCO (Korea Electric Power Cooperation) has been using DGA technique since KEPCO established the criteria of DGA in 1985. In this paper, we introduce the DGA criteria of KEPCO and analyze the result of DGA. Also we introduce the cases of thoroughgoing inspection of the power transformers caused by DGA and compare the judgment of DGA with the result of throughgoing inspection.

• 산업경영시스템학회지
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• 제40권1호
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• 한국기계가공학회지
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• 제19권4호
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• pp.1-8
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• 2020

Mechanical components that support structures in aerospace and power generation industries require high-strength materials. Particularly, in the aerospace industry, aluminum alloys, titanium alloys, and composite materials are increasingly used due to their high maneuverability and durability to withstand low temperature extreme environments; however, ultra-high-strength steel is still used in key components under heavy loads such as landing gears. In this paper, the fault cause analysis and troubleshooting of aircraft parts made of ultra-high-strength steel (300M) broken during normal operation are described. To identify the cause of the defect, a temporary inspection of the same aircraft was performed, and material testing, non-destructive inspection, microstructure examination, and fracture area inspection of the damaged parts were performed. Fracture analysis results showed that a crack in the shape of a branch developed from the tool mark in the direction of the intergranular strain. Based on the results, the cause of fracture was confirmed to be stress corrosion.

• 전기전자학회논문지
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• 제24권1호
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• pp.354-359
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• 2020

전력용 변압기의 결함을 검출하고, 고장을 예방하기 위해 유중가스분석 방법이 정기적으로 수행되고 있다. 유중가스분석방법에 의해 변압기 내부의 과열 또는 방전 현상의 발생은 확인할 수 있지만 결함 부위는 내부점검에 의하여 판별하여야 한다. 하지만, 권선 또는 철심 결함은 내부점검으로 확인할 수 없어 DGA만으로는 조치방안을 수립하기가 불가능하다. 본 논문에서는 변압기 내부점검 보고서를 바탕으로 내부 결함 모드를 분석하여 제시하고, DGA에 의한 판별법을 고려하여 내부 결함을 예측할 수 있도록 하였다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2005년도 추계학술대회 논문집
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• pp.682-687
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• 2005

High quality power supplying of power distribution system in electric railway system is the important function. Power feeding system is complicated witch is compose with distribution line, circuit break, protection facilities and so on. Among this components, role of substation is most important for elevation of reliability in electric power system. Therefore, the enhanced reliability considering the preventive inspection, repair work, replacement is necessary. In this study, a proposed the enhanced reliability method through a calculation of fault probability in power feeding system.

• 한국측량학회지
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• 제34권6호
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• 조명전기설비학회논문지
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• 제22권2호
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• pp.51-56
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• 2008

철도에서의 고압배전선로는 역사조명 및 동력설비, 그리고 열차의 안전운행을 위한 신호설비의 전력을 공급해주는 설비로 열차의 안전운행 및 철도 이용자의 서비스 향상을 위한 중요한 역할을 담당하고 있다. 또한 각종 전력기기의 전자화, 자동화로 인해 양질의 전력공급이 요구되며, 철도 이용자에 대한 보다 향상된 서비스를 제공과 역사내 전기에너지 사용의 급증으로 인한 전력공급의 신뢰성 확보가 요구된다. 본 논문에서는 철도고압배전계통의 고장현상을 분석하기 위하여 철도고압배전 설비 중 접지계통과 비접지계통에 대해서 사고시 전압강하가 발생 여부와 사고를 제거하기 위해 차단기를 동작시켰을 때의 서지전압에 의한 과도특성을 PSCAD/EMTDC를 이용하여 모의하였으며, 이 사고모의 결과를 토대로 비접지 고압배전계통의 보호방법에 대한 대안을 제시하였다.

• 한국산학기술학회논문지
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• 제19권8호
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• pp.37-43
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• 2018

유도탄 점검 중 결함이 검출되면 개발자는 고장을 식별하여 문제 조치 후 시험을 재개한다. 고장 식별을 위해 장비에서 나오는 데이터를 분석해야 하는데, 점검 환경에 따라 수신한 정보가 충분하지 않을 수 있다. 이러한 경우 개발자는 문제가 재현될 때 까지 반복 시험을 수행하던지 연계된 장비를 찾아 각 성능 점검을 수행 하여 정상 여부를 확인한다. 해당 업무가 추가되면 일정 관리에 문제가 생기고 개발비용이 상승한다. 이를 해결하기 위해 체계적인 절차로 유도탄 점검 장비를 설계하여 결함 검출률을 높여 요구 신뢰도를 만족해야 한다. 절차마다 필요한 프로세스를 설계하여 운용 중 결함검출 시 고장식별에 대한 시간을 줄일 수 있다. 하지만 결함 검출률 100%를 만족할 수 없기에 비용대비 효과를 분석 해 설계해야 한다. 본 논문은 유도탄 점검 장비의 신뢰성 향상을 위한 개발 방법과 적용 시 기대효과 및 한계점에 대한 내용을 기술한다.

• 한국화재소방학회논문지
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• 제25권6호
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• pp.136-145
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• 2011

화재발생 원인 중 가장 많은 부분을 차지하는 것 중 하나가 전기적 요인이며 이 전기적 요인 중 일정 비율 이상을 차지하는 것이 누전 지락사고이다. 이러한 누전 지락사고를 줄이기 위해 최근 국제규격인 IEC 60364-4-41을 반영한 저압전로의 지락보호에 관한 기술지침(KECG 1091-2011)이 개정되었으며 설비의 상시사용 운용 및 고장 결함 등을 회복시켜 신뢰성을 향상시키는 보전방식이 대두되고 있다. 따라서 본 논문에서는 최근 10년간의 화재발생현황을 파악하여 전기화재의 위험성을 분석한 후 일정비율 이상 차지하고 있는 누전 지락사고를 개선시키기 위한 방법으로 KECG 1091-2011의 개정사항을 검토한 다음 누전 지락보호검출의 신뢰성을 향상시키기 위한 예방보전 방식을 적용하여 시간계획보전 방식으로 정기점검제도와 내용연수제도에 대한 문제점을 나열한 후 그 해결방안을 제시하고 상태감시보전 방식으로 누설전류 통합감시 장치와 디지털 누전경보기를 제안하고자 한다.