• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.265-267
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• 2022

선박 기관시스템이 효율적이고 안이정적인 운용을 위해서는 실시간 상태 모니터링 기반의 이상탐지, 고장진단 더 나아가 고장예측에 따른 대응조치를 할 수 있는 기술이 필요하며 이를 상태기반 유지관리(Condition Based Maintenance, CBM)이라 지칭한다. 해당 기술을 개발 및 확보하기 위해서는 가장 우선적으로 기관시스템에 대한 다양한 고장 데이터가 확보되어야 하며 이후, 확보된 데이터에 대한 특징추출 등 전처리 알고리즘, 고장 진단 및 예측 알고리즘 등을 개발하여야 한다. 본 연구에서는 선박 추진용 엔진 및 발전기 엔진에 대한 상태기반 유지관리 기술의 개발현황과 향후 지속적인 연구 추진방향을 소개하고자 한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.54-56
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• 2021

다양한 소스로부터 수집되고 연동되는 데이터를 모델링하는 기술로 그래프 데이터베이스를 활용한 분석 기법이 각광받고 있다. 이 연구에서는 항로표지에서 관측되는 상태 및 주변 정보를 모델링하고, 고장진단 및 예측에 적용할 수 있는 기계학습 기법을 소개한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.224-226
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• 2022

다양한 소스로부터 수집되고 연동되는 데이터를 모델링하는 기술로 그래프 데이터베이스를 활용한 분석 기법이 각광받고 있다. 이 연구에서는 항로표지에서 관측되는 상태 및 주변 정보를 모델링하고, 고장진단 및 예측에 적용할 수 있는 기계학습 기법을 소개한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.89-92
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• 2021

In a manufacturing fields, an abnormality or breakdown of equipment is a factor that causes product defects. Recently, with the spread of smart factory services, a lot of research to predict and prevent machine's failures is actively ongoing. However, there is a big difficulty in developing a classification model because the number of abnormal or failure data of the machine is severely smaller than normal data. In this paper, we present an algorithm for detecting abnormalities in an MCT at manufacturing work site depending on the differences between inputs and outputs of Autoencoder model and analyze its performance. The algorithm detects abnormalities using only features of normal data from manufacturing data of the MCT in which abnormal data does not exist.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.17-18
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• 2006

최근 전기설비의 용량이 커짐에 따라 전기설비와 송전설비 및 배전설비 등에서 발생되는 사고는 '2003년 코로나 방전에 의한 미국 동북부 정전사고와 같은 대형사고로 직결될 수 있기 때문에 전기설비에서 발생되는 코로나방전 검출을 통한 기간시설 및 송배전 설비에 대한 사고 원인을 사전 도출하여 전기설비의 장기간에 걸친 원활한 운용과 신뢰성 확보가 매우 중요하다. 이를 위해서 최적의 무정전 첨단계측장비의 필요성이 대두되고 있다. 현재 전력공급의 중단없이 설비의 이상유무를 진단, 감시하기 위한 기술이 활발히 진행되고 있으며 전기설비의 예고 없는 고장발생시 파생되는 악영향은 매우 심각하며, 국내의 경우 전기설비의 노후화로 대형 사고의 위험성이 매우 높아 이러한 사고의 예방을 위한 예지보전(예측보전)을 위한 기술에 대한 도입이 필요하다. 최근 미국전기연구원(EPRI)의 주도로 코로나가 전기설비에 미치는 부정적 영향에 대한 연구가 활발하게 진행되었으며 그 결과 코로나 방전으로부터 전기설비의 안정성과 신뢰성을 확보하고 사고를 방지하기 위한 진단기술로서 OFIL사(社)의 DayCorII가 개발되었다. 이 논문에서는 전력설비와 송전 및 배전분야에 있어 발생하는 코로나 방전의 영향과 이를 탐지하는 진단기술에 대하여 초점을 맞추고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.103-111
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• 2023

This paper proposes a new data correction technique that transforms anomalies in time series data into normal values. With the recent development of IT technology, a vast amount of time-series data is being collected through sensors. However, due to sensor failures and abnormal environments, most of time-series data contain a lot of anomalies. If we build a predictive model using original data containing anomalies as it is, we cannot expect highly reliable predictive performance. Therefore, we utilizes the LSTM-GAN model to detect anomalies in the original time series data, and combines DTW (Dynamic Time Warping) and GAN techniques to replace the anomaly data with normal data in partitioned window units. The basic idea is to construct a GAN model serially by applying the statistical information of the window with normal distribution data adjacent to the window containing the detected anomalies to the DTW so as to generate normal time-series data. Through experiments using open NAB data, we empirically prove that our proposed method outperforms the conventional two correction methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.216-223
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• 2019

Fault detection during production processes is one of the required operational tasks to run production processes both safely and consistently. Unexpected operational events or undetected process faults can have a serious impact on the production systems and subsequently on the final products' quality. In addition, such situations may lead to malfunctions or breakdowns of production processes. To reliably detect such abnormalities, a new one-class classification-based detection scheme has recently been developed The proposed method consists of four steps:1) noise filtering, 2) feature selection, 3) nonlinear representation and 4) outlier detection. The performance of the proposed scheme was demonstrated using the multivariate data obtained from a simulation process. The results have shown that the proposed method produced reliable monitoring results and outperforms any existing methods with an average improvement of 25.4%. The use of proper feature selection in the proposed framework yielded better detection performance.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.1
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• pp.45-51
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• 2004

Boiler tubes in power plants are often leaked due to various material degradations including creep and thermal fatigue damage under severe operating conditions such as high temperature and high pressure over an extended period of time. To monitor and diagnose the tubes on site and in real time, the acoustic emission (AE) technology was applied. We developed an AE leak detection system, and used it to study the variation of AE signal from the on-site tubes in response to the changes in the boiler operation condition and to detect the locations of leakage based on it. Detection of leak was performed by acquiring and evaluating the signals in separate regimes of high and low frequency signal. As a result of these studies, we found that on-line monitoring and detection of leak location for boiler tubes is possible using the developed system. Thus, the system is expected to contribute to the safe operation of power plants, and prevent economic losses due to potential leak.