• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.405-407
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• 2016

최근, ICT/loT 기술과의 융합은 다양한 산업분야에 적용되고 있으며, 안정적인 전력공급 및 지능형전력망 구축에 대해 다양한 연구가 이루어지고 있다. 특히, 수요라인과 직접적으로 연관된 배전계통의 효율적인 운영 및 배전설비의 유지/관리 기술에 대한 연구에 많은 연구를 수행하고 있다. 본 논문에서는 다양한 배전설비에 대한 환경정보를 loT 센서를 통해 수집함으로써 실시간으로 정전상황을 불러올 수 있는 기자재의 고장감지 및 예측을 위한 시스템 모델을 제안한다. 제안하는 시스템 모델은 실시간으로 수집되는 정보들에 대해 시계열 기반의 필터링 및 이상점 판단을 위한 성분 분석을 실시하고, 고장진단 및 예측을 위해 기계학습 기반의 데이터 분석실시하여 기자재들의 고장감지 및 고장 발생 여부를 예측한다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.83-92
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• 2022

In this paper, we designed an algorithm that can control the state of PM by learning the chain failure pattern of PHM based air conditioning facility. It is an inevitable spread of PM due to the downtime caused by the failure of the air conditioning facility. The algorithm developed by us is to establish a PM management system through PHM, and it is an algorithm that maintains a constant stabilization state through learning the stop/operation pattern of the air conditioner and manages PM based on this. As a result of the simulating at a subway station for the performance qualification of the algorithm, it was verified that the concentration of PM reduces by 30% on average. In the case of stations with many passengers using the subway, the concentration of PM exceeded the Ministry of Environment Standards(100 ㎍/m³), but it was verified that the concentration of PM was improved at all stations where the simulation was conducted. In the future research is to expand the system to comprehensively manage not only PM but also pollutants such as CO₂, CO, and NO₂ in subway stations.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.51-51
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• 2017

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.223-226
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• 2009

Various database and analysis system has been used for the cost effective maintenance of distribution facility but it is not effective because of the lack of interconnection among these systems. In order to overcome this problem this paper proposes reliability centered maintenance system based on the on-line monitoring of distribution system through intelligent distribution equipment. This system is made by the interconnection of distribution automation system, asset management system, failure analysis system and failure mode effect analysis system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.57-60
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• 2021

본 논문에서는 기지 설비 중 주요 회전기기인 펌프의 이상탐지 알고리즘을 제안한다. 현재 인공지능을 활용하여 생산현장을 혁신하고자 하는 시도가 진행되고 있으나 외산 솔루션에 대한 의존도가 높은 것에 비해 국내 실정에 맞지 않는 경우가 많다. 이에 따라, 선행 연구를 통해 국내 실정에 맞는 인공지능 기술 도입이 필요하다. 본 연구에서는 VAE(Variational Auto Encoder) 알고리즘을 활용해 회전기기의 고장을 진단하는 알고리즘을 개발하였다. 본 연구 수행을 통한 회전기기의 고장 예지·진단 시스템 개발로 설비의 이상 징후 포착, 부품의 교환 시기 등 보수 일정을 예측하고 최종적으로 이를 통한 설비 가동의 효율 증대와 에너지 비용 감소의 효과를 기대한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.625-629
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• 2015

In this study, we develop a prognostic method of assessing the stator windings of power generators against water absorption through statistical data analysis and degradation modeling. The 42 windings of the generator are divided into two groups: the absorption and normal groups. A degradation model of a winding is constructed using Fick's second law to predict the level of absorption. By analyzing data from the normal group, we can determine the distribution of the data of normal windings. The health index of a winding is estimated using the directional Mahalanobis distance (DMD) method. Finally, the probability distributions of the failure time of the windings are determined.

• 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.

• Journal of IKEEE
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• v.24 no.1
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• pp.170-177
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• 2020

This paper presents an open-fault tolerance control method for active neutral point clamped (ANPC) inverter with high frequency/low frequency (HF/LF) modulation. By applying the ANPC inverter with SiC MOSFETs and Si IGBTs, the system efficiency and performance can be improved compared to a Si-based inverter. HF/LF modulation is used for a megawatt-scale inverter to minimize the commutation loop. The open-switch failure in megawatt-scale inverter causes severe damage to load and huge expenses when the inverter has been shut-down. The proposed tolerance control of open-switch failure provides continuous operation and improved reliability to the ANPC inverter. The effectiveness of the proposed fault tolerance control is verified by simulation results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.69-71
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• 2018

Looking at Korea's manufacturing industry, there are many old manufacturing plants. In fact, the manufacturing process of the product inventory management and the unit price of the product are all created by using Excel, and the factory is operated by using it. Also, the operator can not predict the failure of the equipment in order to produce the product at work. Problems related to this may result in the loss of the documents during the instruction and work process between the manager and the worker, and the communication between the manager and the worker can not be properly performed, There is appear a situation in which the operation is continued by using the equipment without recognizing in the failure. In this paper, we propose a method for upgrading the aging manufacturing plant to improve the productivity and productivity of the product by predicting the efficient inventory management, unit price management, production volume, and the operator's failure prediction.