• Journal of the KSME
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• v.53 no.7
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• pp.44-52
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• 2013

이 글에서는 고장진단 및 예지기술(PHM: Prognostics and Health Management)의 에너지산업 분야 적용 사례를 상세히 소개하고, PHM기술의 접목을 통한 에너지산업에의 기여와 예상되는 기술적 어려움, 그리고 향후 연구방향을 제시하고자 한다.

• Journal of the KSME
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• v.53 no.7
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• pp.26-34
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• 2013

이 글에서는 최근 관심을 모으고 있는 고장예지 및 건전성관리(PHM: Prognostics and Health Management) 기술을 소개하고, 항공우주분야의 적용사례를 중심으로 PHM 기술을 어떻게 활용하고 있는지를 설명하고자 한다.

• Journal of the KSME
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• v.56 no.11
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• pp.46-49
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• 2016

이 글에서는 최근 3년간(2013-2015) PHM society 학회에서 발표된 논문에 기반하여 분석된 고장예지기술의 연구 동향 및 도전과제에 대해 소개하고자 한다.

• Journal of the KSME
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• v.57 no.3
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• pp.34-37
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• 2017

이 글은 공학시스템 고장예지 및 건전성 관리 기술을 소개하고, 최근 주목받고 있는 인공지능기술인 딥러닝과 접목한 연구 사례를 제시한다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.265-267
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• 2000

가공물을 한 개씩 작업하는 단일작업기계와 여러 개의 가공물을 한꺼번에 작업하는 배치 기계를 포함한 재진입 라인 시스템에서 각 기계가 고장이 일어날 수 있고, 불량품이 발생할 때, 이 시스템에서의 가공물의 생산주기를 구하는 방법을 Mean Value Analysis(MVA)를 이용하여 제시하고자 한다. 배치 기계와 고장 발생, 불량품 발생 등의 이유의 이런 시스템은 승법형 대기행렬 모형으로 모형화 할 수 없다. 본 논문에서는 각 기계에서의 체류 시간을 구하여 전 시스템의 생산 주기를 구하는 방법을 MVA 를 이용하여 제시한다.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.200-206
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• 2022

This study is to develop database by an experimental method for the development of condition based maintenance for auxiliary equipment in marine engine systems. Existing ships have been performing regular maintenance, so the actual measurement data development is very incomplete. Therefore, it is best to develop a database on land tests. In this paper, a database developed by an experimental method is presented. First, failure case analysis and reliability analysis were performed to select a failure mode. For the failure simulation test, a test bed for land testing was developed. The failure simulation test was performed based on the failure simulation scenario in which the failure simulation test plan was defined. A 1.5TB failure simulation database has been developed, and it is expected to serve as a basis for ship failure diagnosis and prediction algorithm model development.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.78-83
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• 2014

Recently, a rapid growth of wind power system as a leading renewable energy source has compelled a number of companies to develop intelligent monitoring and diagnostic system. Such systems can detect early mechanical faults, which prevents from costly repairs. Generally, fault diagnostic system for wind turbines is based on vibration and process signal analysis. In this work, different type of mechanical faults such as mass unbalance and shaft misalignment which can always happen in wind turbine system is considered. The proposed intelligent fault diagnostic algorithm utilizes artificial neural network and Wavelet transform. In order to verify the feasibility of the proposed algorithm, mechanical fault generation experimental system manufactured by Gaon corporation is utilized.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.4
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• pp.8-15
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• 2021

The effects on system availability, operation, and support costs were analyzed using the M&S system (MPS). The failure frequency items of current armored vehicles were identified and the MTBF of the identified items was improved. The results of this study suggest that when we reduce the frequency of failure, the availability increases, and the operation and support costs decrease. By improving the reliability of the failure frequency items, it becomes possible to upgrade or develop the weapons systems. Through this study, we confirmed that improving reliability will enhance combat readiness and reduce operation and support costs.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.139.1-139.1
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• 2005