• 제목/요약/키워드: Facility Diagnosis

검색결과 201건 처리시간 0.03초

사회기반 시설물의 안전점검 및 정밀안전진단결과 DB 구축방안 연구 (Database Construction Plan of Infrastructure Safety Inspection and In-depth Inspection Results)

  • 유종모;신은철
    • 한국지반신소재학회논문집
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    • 제13권4호
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    • pp.133-141
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    • 2014
  • 본 연구에서는 교량, 터널, 댐, 상수도 등 사회기반 시설물의 안전점검 및 정밀안전진단결과를 DB화하고 업무프로세스를 자동화하기 위해 각 시설물별로 점검진단에 필요한 항목을 표준화해서 분류체계를 마련하였고 사전조사에서부터 현장조사, 시설물평가, 보고서작성에 이르는 전 과정에서 걸쳐 데이터베이스 기반의 자료구조를 정의하고 파일럿시스템을 구현하였다. 또한 점검진단결과 DB가 효과적으로 활용될 수 있도록 개선방안을 제시하고, 시설물정보관리종합시스템 운영규정, 점검진단 세부지침 등 관련 규정/지침에 대한 개선방안을 제시하였다.

열화상 이미지를 이용한 배전 설비 검출 및 진단 (Detection and Diagnosis of Power Distribution Supply Facilities Using Thermal Images)

  • 김주식;최규남;이형근;강성우
    • 대한안전경영과학회지
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    • 제22권1호
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    • pp.1-8
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    • 2020
  • Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

교통안전진단 결과분석을 통한 교통사고 요인분석 - 사고자 요인을 중심으로 - (A Factor Analysis of Traffic Accidents Through Traffic Safety Diagnosis Results - Driver Factor -)

  • 이환승;안병준
    • 한국안전학회지
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    • 제21권2호
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    • pp.128-137
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    • 2006
  • Traffic accidents occur due to complex influences of transportation companies, drivers, and road environment. This study found that education and surroundings for transportation companies, driving habits of drivers, and road safety facilities and accessory facilities were main factors that affected strongly traffic accidents. Also, it found that driving habits of drivers could affect accidents heavily more than two other factors. Road safety and accessory facilities were analysed that they affected traffic accidents independently with transportation companies and their own drivers. Therefore, in order to achieve a traffic accident prevention as our main target, those companies need to produce atmosphere that their own drivers can have safety awareness, and related institutions for the above target should run parallel with policy assistance and strict traffic enforcement. In the end, this study suggests that transportation companies should secure manpower wholly being charged with traffic safety and financial resources investing in it.

스마트 팩토리에서 설비 장애 진단 및 조치 시스템 구조 (A System Architecture for Facility Fault Diagnosis and Repair Action in Smart Factory)

  • 조재형;이재오
    • KNOM Review
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    • 제23권1호
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    • pp.18-25
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    • 2020
  • 최근 스마트 팩토리(Smart Factory)에 대한 연구는 단순히 공장 자동화(Factory Automation, FA)의 개념에서 데이터를 수집하고 분석하는 형태로 발전하고 있다. 이것은 통신 기술의 발전(5G)과 IoT 장치(device)들이 현장 상황에 맞춰 다양하게 개발되면서 가속화 되고 있다. 또한, 기업 경쟁력 강화로 디지털트랜스포메이션(Digital Transformation)이 활발히 이루어지고 있으며, 이를 각종 IoT 장비로 부터 수신한 데이터와 자동화된 설비를 결합시켜 공정 재조정을 통한 최적화 연구가 다양하게 진행되고 있다. 따라서 본 논문에서는 관련 연구 중 하나인 예측 시스템을 활용한 설비 장애 진단 및 조치 시스템 구조 및 요소를 제안한다.

안전정보와 보전관리정보를 연계한 Web 기반 지식베이스 진단시스템 구현 (A Study on the Development of a Web Based Knowledge-Based Diagnosis System through a Combination of SIS and MMIS)

  • 박주식;이선태;박상민;남호기
    • 대한안전경영과학회지
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    • 제2권4호
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    • pp.59-70
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    • 2000
  • To keep enterprise's competitiveness on condition of the automatic manufacturing system such as FA, FMS and CIM, all the maintenance problems should be considered seriously in not only production and maintenance but also related industrial safety. As we analyze in the surveys for the maintenance management of domestic enterprises and the causes of Industrial accident, there will be necessity of drawing up countermeasures for prevention of industrial accidents and for ensuring expertise maintenance technologies. Based on these analyses, this study studied the safety information system, maintenance management information system, and the machinery condition diagnosis technique by using of the knowledge-based system under the internet environment. This web based knowledge-based diagnosis system can easily provide not only the knowledge of expert about deterioration phenomenon of industrial robot, but also the knowledge of relating safety and facility on everywhere, everytime. Therefore, when we use this system, it is expected to improve the efficiency of business processes in the production and safety.

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생산과 안전의 효율화를 위한 Web 기반 지식베이스 진단시스템 구현 (A Study on the Development of a Web Based Knowledge-Based Diagnosis System for Production and Safety Efficiency)

  • 이선태;박상민;남호기
    • 대한안전경영과학회:학술대회논문집
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    • 대한안전경영과학회 2000년도 추계학술발표논문집
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    • pp.269-279
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    • 2000
  • To keep enterprise's competitiveness on condition of the automatic manufacturing system such as FA, FMS and CIM, all the maintenance problems should be considered seriously in not only production and maintenance but also related Industrial safety. As we analyze in the surveys for the maintenance management of domestic enterprises and the causes of industrial accident, there will be necessity of drawing up countermeasures for prevention of industrial accidents and for ensuring expertise maintenance technologies. Based on these analyses, this study studied the safety information system, maintenance management information system, and the machinery condition diagnosis technique by using of the knowledge-based system under the internet environment. This web based knowledge-based diagnosis system can easily provide not only the knowledge of expert about deterioration phenomenon of industrial robot, but also the knowledge of relating safety and facility on everywhere, everytime. Therefore, when we use this system, it is expected to improve the efficiency of business processes in the production and safety.

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발전설비의 회전기기 고장진단을 위한 전문가 시스템의 구현 (Development of aFailure Diagnosis Expert System for Rotational Equipment of Generation Facilities)

  • 김창종
    • 조명전기설비학회논문지
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    • 제12권4호
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    • pp.47-54
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    • 1998
  • 발전설비에 있어 고신뢰성이 요구됨에 따라 진행되는 고장을 조기에 발견하는 예방 보전적 진단 시스템이 요구되고 있다. 본 논문에서는 발전설비 중에서 회전체의 절연진단과 진동진단에 대한 지식을 지식 베이스로 구성하고 이것을 다표정 언어를 이용하여 전문가 시스템을 구성하였다. 이 전문가 시스템은 진단 룰의 수정과 추가가 용이하며, 사용자와의 인터페이스도 양호한 구조를 취하고 있다.

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지중 배전용케이블 고장통계 분석 및 고장률 활용 진단대상 우선순위 선정방법 (MV Cable Failure Statistics Analysis and Failure Rate Utilization Method of Prioritization of Diagnosis Targets)

  • 조종은;이온유;김상봉;김강식
    • KEPCO Journal on Electric Power and Energy
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    • 제7권2호
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    • pp.263-268
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    • 2021
  • This paper statistically analyzes the time required for each failure cause and describes a diagnostic method for 159 reports of failure analysis of MV cables that occurred in the distribution system of KEPCO over the past 18 years. In addition, the manufacturer's failure rate compared to 100C-km was calculated using 381 cases of MV cable deterioration failure between 2008 and 2020. It is hoped that this paper will help those in charge of maintaining underground facilities at the business office to use the failure rate to prioritize facility diagnosis.

전이 학습과 진동 신호를 이용한 설비 고장 진단 및 분석 (Fault Diagnosis and Analysis Based on Transfer Learning and Vibration Signals)

  • 윤종필;김민수;구교권;신우상
    • 대한임베디드공학회논문지
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    • 제14권6호
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    • pp.287-294
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    • 2019
  • With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

필 댐의 특성을 고려한 농업용 저수지 정밀안전진단체계 개선 연구 (A Study on the Safety Inspection System Improvement of Agricultural Reservoir Considering Fill-Dam Characteristics)

  • 이창범;정남수;박승기;전상옥
    • 한국농공학회논문집
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    • 제58권4호
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    • pp.1-8
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    • 2016
  • In 2008, 17, 596 dams and reservoirs are scattered across South Korea, and 17, 505 of them (99.5 %) are used for agriculture and 99.3 % are fill dam types. This study aimed to review literature related to the precise safety diagnosis system for agricultural reservoirs established by Korea Rural Community Corporation (KRCC) and analyze problems of its evaluation method. And then, it proposed ways to improve the system including a modified diagnosis system, which was applied to pilot districts in order to verify the utility. For assessment model development of agricultural reservoir, we reviewed status of precision safety inspections systems of agricultural reservoir. There are many problems such as assess agricultural reservoir not by sheet which used in fill dam but by block which used in concrete dam construction and diversion tunnel which main element in reservoir levee is treated as water intake facility. For considering diversion tunnel in reservoir levee, previous precision safety inspection systems which summed in separated phenomenon, separated element, separated site, separated facility was change to new systems which summed in site, phenomenon, element, and facility. Compared results of previous inspection system calculated total assessment index (Ec) with new system calculated total assessment index (Ec) are not show statistical difference.