• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.57-60
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• 2006

The purpose of this study is to explain the importance of Vibration Monitoring Device by introducing an example of Predictive Maintenance System using Condition Monitoring System of Hydro-turbine generator. Confirming vibration of generation equipment is commissioning procedure during equipment completion for checking guaranteed items. Data from Generator output range are used to determine output band to continue the performance of equipment. The Vibration Monitoring System is not absolute method of maintenance, but if it is used well with expert, it will be visible, data-analyzed, scientific maintenance more than others. And also, Condition Monitoring System is very important for remote controlled small hydro-power plant although most of it is installed in Large hydro-power plant.

• The KSFM Journal of Fluid Machinery
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• v.13 no.1
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• pp.29-34
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• 2010

The purpose of this study is to explain the importance of Vibration Monitoring Device by introducing an example of Predictive Maintenance System using Condition Monitoring System of Hydro-turbine generator. Confirming vibration of generation equipment is commissioning procedure during equipment completion for checking guaranteed items. Data from Generator output range are used to determine output band to continue the performance of equipment. The Vibration Monitoring System is not absolute method of maintenance, but if it is used well with expert, it will be visible, data-analyzed, scientific maintenance more than others. And also, Condition Monitoring System is very important for remote controlled small hydro-power plant although most of it is installed in Large hydro-power plant.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.04a
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• pp.109-115
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• 1994

지난 날에는 생산공장 기기들의 정비에 진동분석 이용은 거의 전무한 상태이었으며 과학이 발달된 이 즈음에도 고장이 날 때까지 기기를 혹사하고 고장이 난 후에야 많은 시간과 경비를 들여 기기를 재가동 함은 물론 공장가동 중단으로 인한 생산성 상실이 산업계에 주는 영향은 크다. 경우에 따라서는 기기전체를 교체하는 큰 대형사고로 이어질 수 있기 때문에 공장 전면 조업에 큰 차질을 빚게된다. 시태크(Time tech)와 Re-Engineering과 같은 최첨단 경영방침에 부응하기 위해서는 구태의연한 가동파괴정비나 정기점검 정비방법을 탈피하여 최신 진동분석 기술을 이용한 예방정비(predictive maintenance)를 채택하는 것이 바람직하다. 과학기술 발전에 힘입어 정확한 진동자료를 수집할 수 잇는 주파수분석기(FFT analyzer)나 자료수집기 (data collector)와 진동자료를 심층분석하여 정확한 진동해결방안을 제시할 수 있는 software가 개발되어 사용화 되어 있는바 관계기술 요원들의 진동에 대한 이해와 기술습득으로 한차원 높은 기기정비를 통해 효율적인 생산성증가, 정비비용감소, 안전사고 미연방지등 많은 것을 함께 얻을 수 있다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.407-412
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• 2008

원자력발전소의 안전성 확보를 위해서, 설비의 유지 관리 기법은 고장이 발생했을 때 조치하는 고장정비에서부터 고장을 미연에 방지하기 위한 예방정비, 예측정비 그리고 상태기반정비로 진행되어 가고 있다. 국내 원자력발전소에서는 고장정비와 예방 정비가 적용되고 있으며, 예측정비와 상태기반정비에 대한 연구가 수행되고 있다. 본 논문에서는 모터구동밸브, 공기구동밸브, 역지밸브 그리고 펌프에 대한 예방정비 현황과 기술 개발 동향에 대해 살펴보았다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.643-644
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• 2012

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1305-1310
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• 2008

Traditional maintenance planning is based on a constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is desirable to be scheduled by RCM(Reliability-Centered Maintenance) evaluation. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system and avoiding or removing maintenance actions that are not strictly necessary. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by the stepwise Markov model in detail. Also, because the system is not continuously monitored, the inspection is considered. In case study, simulation results about RCM will be shown using the real historical data of combustion turbine generating unit in Korean power systems.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2004.10a
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• pp.45-51
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• 2004

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.914-919
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• 2017

While industry understands the importance of keeping equipment operational and well maintained, the importance of tracking maintenance information in reliability models is often overlooked. Prognostic models can be used to predict the failure times of critical equipment, but more often than not, these models assume that all maintenance actions are the same or do not consider maintenance at all. This study investigates the influence of integrating maintenance information on prognostic model prediction accuracy. By incorporating maintenance information to develop maintenance-dependent prognostic models, prediction accuracy was improved by more than 40% compared with traditional maintenance-independent models. This study acts as a proof of concept, showing the importance of utilizing maintenance information in modern prognostics for industrial equipment.