• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.565-570
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• 1996

국내 원자력발전소에서 발생한 발전정지 사건사례를 분석한 결과로써 획득한 고장원인, 문제점, 유사한 문제점 재발방지 방안 등의 분석정보를 효과적으로 공유하기 위한 발전정지사례 분석정보 시스템을 개발하고 있다. 이 시스템의 기반구조인 관계형 데이타베이스 화일들은 입력작업 지원 분류정보, 발전정지 사례분석정보, 검색작업 지원정보 둥의 저장을 위한 세가지 화일 그룹으로 나눌 수 있다. 각 그룹의 화일간에 정의된 상관관계성을 기반으로 발전정지사례 분석정보시스템의 입력, 검색, 출력작업이 수행된다. 시스템의 사용자는 제공되는 메뉴를 사용하여 관심있는 주제별로 데이타베이스에 저장된 발전정지사례 분석정보를 검색하고 출력할 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• Journal of the Korea Institute of Building Construction
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• v.23 no.4
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• pp.417-428
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• 2023

The demolition of domestic infrastructures mirrors other significant construction initiatives in presenting a markedly high accident rate. A comprehensive investigation into the origins of such accidents is crucial for the prevention of future incidents. Upon detailed inspection, the causes of demolition construction accidents are multifarious, encompassing unsafe worker behavior, hazardous conditions, psychological and physical states, and site management deficiencies. While statistics relating to demolition construction accidents are consistently collated and reported, there exists an exigent need for a more foundational cause categorization system based on accident type. Drawing from Heinrich's Domino Theory, this study classifies the origins of accidents(unsafe behavior, unsafe conditions) and human errors(human factors) as per the type of accidents experienced during demolition construction. In this study, a three-step model of QFD-FMEA(Quality Function Deployment - Failure Mode Effect Analysis) is employed to systematically categorize accident causes according to the types of accidents that occur during demolition construction. The QFD-FMEA method offers a technique for cause classification at each stage of the demolition process, including direct causes(unsafe behavior, unsafe environment), and human errors(human factors) through a tri-stage process. The results of this accident cause classification can serve as safety knowledge and reference checklists for accident prevention efforts.

• Journal of Fisheries and Marine Sciences Education
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• v.6 no.2
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• pp.190-197
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• 1994

In this paper, 2513 marine casualties occurred in Korean waters, during 1989-1993, were analysed by the Multivariate Data Analysis Method. The main results obtained were as follows : 1. Moat of marine casualties resulted from the human factors such as careless operation and insufficient engine maintenance. Engine trouble accounted for main patten of accidents and great number of accidents occurred in fishing vessels. 2. From the point of view of the damage of human life and properties, accidents took place in cargo ships, passenger ships and tankers were serious, but in fishing vessels, those were not so serious. 3. Grounding, collision mainly resulted from careless operation, however flooding and capsizing were much affected by bad weather and material defect.

• Proceedings of the KIEE Conference
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• 2008.04b
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• pp.203-206
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• 2008

전력용 변압기의 고장 원인이 과도 진동에 의한 기계적 결함이 대부분을 차지하고 있으며, 그 원인 120Hz배수 조합으로 이루어진 진동이다. 전력용변압기에 작용되는 기계적 가진력을 유형별로 분류하고, 기계나 구조물에 미치는 진동전달 경로를 통해 전력용변압기의 기계적 손상 메커니즘을 규명한다. 일반적으로 120Hz 성분의 진동이 변압기변의 외함, 부싱, 부흐홀쯔, 방압변 및 탭체인쳐의 진동영향에 대해 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.367-372
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• 2008

In this paper we proposed a model of a fault diagnosis expert system with high reliability to compare identical well-functioning motors. The purpose of the survey was to determine if any differences exit among these identical motors and to identify exactly what these differences were, if in fact they were found. Using measured data for many identical brushless dc motors, this study attempted to find out whether normal and fault can be classified by each other. Measured data was analyzed using the State Transition Model (STM). Based on a proposed STM method, the effect of a different normal state is minimized and the detection of fault is improved in identical motor system. Experimental results are presented to prove that STM method could be a useful tool for diagnosing the condition of identical BLDE motors.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.409-415
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• 2016

Recently several techniques for disaster prevention based on IoT(Internet of Things) are being developed. In this paper, a new smart pattern classification method for electric loads is proposed. CT(Current Transformer) data are extracted from electric loads, and then the sampled CT data are converted using FFT and MFCC. FFT and FMCC data are used for the input data of neural networks. Experiments were conducted using FFT and MFCC data for 7 kinds of electric loads. Experiments results indicate the superiority of MFCC in comparison to FFT.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.230-231
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• 2019

This study is a development on condition based maintenance(CBM) technology which is a core item of future autonomous ships. It is developing to design & installation of condition monitoring system and acquisition & processing of data from ongoing ships for fault prediction & prognosis of engine in operation. The ultimate goal of this study is to develop a predicts and decision support software for marine engine faults. To do this, the FMEA and fault tree analysis of the main engine should be accompanied by the analysis of classification of system, identification of the components, the type of faults, and the cause and phenomenon of the failure. Finally, the CBM system solution software could predict and diagnose the failure of main engine through integrated analysis for bid-data of ongoing ships and engineering knowledge. Through this study, it is possible to pro-actively cope with abnormal signals of engine and to manage efficiently, and as a result, expected that marine accident and ship operation loss during navigation will be prevented in advance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.7-8
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• 2018

본 논문에서는 CNN 기반의 소음을 이용한 원동 구동장치 진단시스템(PHM)을 제안한다. 이 시스템은 구동장치로부터 발생된 소리로부터 특징데이터를 추출하여 이를 학습한 후 실시간으로 구동장치의 상태를 진단하는 것을 목적으로 하며, 딥러닝 기술을 이용하여 특정 장치에 종속되지 않고 학습할 데이터에 따라 적용 대상이 쉽게 가변 할 수 있도록 설계하였다. 본 논문에서는 실제 적용될 현장에서 발생할 수 있는 예측외의 소음환경에 유연하게 대처하기 위해 딥러닝 모델 중 CNN을 적용한 시스템을 설계하였으며, 제안된 시스템과 이전 연구에서 제안된 DNN 기반의 기계진단시스템을 학습데이터의 환경과 다른 처리배제가 필요한 소음환경에서 비교 실험하여 제안된 시스템이 새로운 환경적응 성능향상에 대하여 우수한 결과를 얻었음을 확인하였다.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.186-188
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• 2019

현재 신재생에너지 발전원은 계속 증가되고 있으며, 발전기의 용량 또한 점점 증가하고 있다. 늘어나는 신재생에너지 발전원에 의해 계통 연계의 중요성이 증대되고 있고 아울러 계통에서 발생할 수 있는 여러 가지 사고로 인한 발전기의 PCS의 고장에 대한 문제 또한 중요해지고 있다. 본 논문에서는 신재생발전원의 용량이 증가함에 따라서 각 스위치의 전기적 스트레스를 줄일 수 있는 3-level NPC 타입의 컨버터회로를 기반으로 이중 전류 제어기를 이용하였고, 계통 사고시에도 강인한 위상 추종 특성을 가지는 DDSRF(Decoupled Double Synchronous Reference Frame 이하 DDSRF)방식의 PPL을 채택하여 시뮬레이션을 진행하였다. 현재 계통의 사고에 의한 사고전압은 ABC 분류에 의해서 크게 A~G 타입으로 나타내고 있다. 본 논문에서는 각 타입별 사고전압의 불평형 지수(Imbalance Factor, 이하 IF)에 따른 중성점 전류의 고조파 성분을 분석하여 도식화 하고자 한다. 이는 계통사고 발생 시 계통연계형 컨버터의 제어 및 계통탈락 여부에 활용 할 수 있을 것으로 예상된다.