• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.157-160
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• 2003

발전소에서 운전중인 기계들의 안전운전과 예지 보전을 위하여 발전설비의 고장 감지 및 진단과 상태 모니터링은 중대한 역할을 담당하고 있다. 이 연구에서는 설비의 안전하고 신뢰적인 운전을 위한 기계의 작동상태를 사운드 정보로 획득하고 분석하는 시스템을 제안하였다. 사운드 정보의 사용은 적은 양의 채널의 사용으로 많은 기계 및 설비의 이상 유무의 판별을 가능케 하며, 이를 획득하기 위하여 3개의 마이크로폰, 다채널 A/D변환기, 다채널 I/O Sound Card(Soundtrack DSP24) 및 PC로 시스템을 구성하였다. 소프트웨어 개발언어로서 Microsoft Visual C＋＋ 및 MATLAB을 이용하였다. 화력 발전소에 운전중인 주요기계들의 사운드 정보를 취득하여 취득한 기계별 사운드 정보를 이용하여 주파수 특성을 파악하고, 이를 이용하여 기기의 운전 상태진단을 가능하게 한다.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.1
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• pp.78-85
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• 2013

Since the basic built-in-test, prognostic health management (PHM) has evolved into more sophisticated and complex systems with advanced warning and failure detection devices. Aerospace and military systems, manufacturing equipment, structural monitoring, automotive electronic systems and telecommunication systems are examples of fields in which PHM has been fully utilized. Nowadays, the automotive electronic system has become more sophisticated and increasingly dependent on accurate sensors and reliable microprocessors to perform vehicle control functions which help to detect faults and to predict the remaining useful life of automotive parts. As the complication of automotive system increases, the need for intelligent PHM becomes more significant. Given enormous potential to be developed lays ahead, this paper presents findings and discussions on the trends of automotive PHM research with the expectation to offer opportunity for further improving the current technologies and methods to be applied into more advanced applications.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.187-195
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• 2019

In the fourth industrial revolution era, various commercial smart platforms for smart system implementation are being developed and serviced. However, since most of the smart platforms have been developed for general purposes, they are difficult to apply / utilize because they cannot satisfy the requirements of real-time data management, data visualization and data storage of smart factory system. In this paper, we implemented an open source based smart manufacturing big data platform that can manage highly efficient / reliable data integration for the diagnosis diagnostic system of manufacturing robots.

• Journal of KIISE
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• v.42 no.7
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• pp.840-845
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• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.1-7
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• 2024

Weather is one of the main causes of aircraft accidents, and among the phenomena caused by weather, icing is a phenomenon in which an ice layer is formed when an object exposed to an atmosphere below a freezing temperature collides with supercooled water droplets. If this phenomenon occurs in the rotor blades, it causes defects such as severe vibration in the airframe and eventually leads to loss of control and an accident. Therefore, it is necessary to foresee the icing situation so that it can ascend and descend at an altitude without a freezing point. In this study, vibration data in normal and faulty conditions was acquired, data features were extracted, and vibration was predicted through deep learning-based algorithms such as CNN, LSTM, CNN-LSTM, Transformer, and TCN, and performance was compared to evaluate blade icing. A method for minimizing operating loss is suggested.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.165-172
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• 2020

In this paper we propose an uses on-device-based edge computing technology and big data analysis methods through the use of on-device-based edge computing technology and analysis of big data, which are distributed computing paradigms that introduce computations and storage devices where necessary to solve problems such as transmission delays that occur when data is transmitted to central centers and processed in current general smart factories. However, even if edge computing-based technology is applied in practice, the increase in devices on the network edge will result in large amounts of data being transferred to the data center, resulting in the network band reaching its limits, which, despite the improvement of network technology, does not guarantee acceptable transfer speeds and response times, which are critical requirements for many applications. It provides the basis for developing into an AI-based facility prediction conservation analysis tool that can apply deep learning suitable for big data in the future by supporting intelligent facility management that can support productivity growth through research that can be applied to the field of facility preservation and smart factory industry with integrated hardware technology that can accommodate these requirements and factory management and control technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1403-1412
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• 2019

Effect of damage and loss cost for downtime is huge, if physical devices such as turbines, pipe, and storage tanks are in the abnormal state originated from not only aging, but also cyber attacks on the control and monitoring system like PLC (Programmable Logic Controller). To improve availability and dependability of the physical devices, we design and implement an indirect health monitoring system which sense temperature, acceleration, current, etc. indirectly, and put sensor data into Influx DB in real-time. Then, the actual performance of detecting abnormal state is shown using the indirect health monitoring system. Analyzing data are acquired using the real-time indirect health monitoring system, abnormal state and security threats can be double-monitored and lower maintenance cost utilizing prognostics and health management.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.101-101
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• 2012

본 논문은 현재 제품화 단계로 진행 중인 터보 분자펌프(turbo-molecular pump, TMP)와 극저온 펌프(cryopump)의 고장 방지 및 예지 보수를 위한 상태 진단 시스템에 대하여 소개를 한다. 본 상태 진단 시스템은 고진공 펌프들의 다중 상태변수 즉 흡/배기부의 진공 압력, 부위별 온도, 소비 전류(혹은 전력), 그리고 부위별 진동 신호들을 실시간으로 측정하는 상태변수 수집장치, 수집된 시계열 상태변수들이 저장된 database, 그리고 저장된 상태변수를 이용한 고진공펌프의 상태진단 프로그램으로 구성되어 있다. 금번 연구에서 구축한 상태변수 체계의 특징 중 하나는 진동신호를 상태변수로 측정하여 이를 상태진단에 활용하는 점이 기존의 접근방법과 상이한 점이다. 실시간 신호 수집장치는 NI사 PXI 시스템 기반의 16채널 24-bit 동시 전압신호 측정 모듈, 8부위의 온도 측정장치(Lakeshore 218S, RS-232C 통신), 그리고 펌프의 소비전류/전력 측정장치(Hioki 3169, RS-232C), 그리고 고진공 펌프의 흡입 및 배기구의 진공도 측정장치로 구성하였다. 신호 수집용 프로그램은 NI사 Labview를 이용하여 작성하였다. 본 장치는 Nano-Fab 센터의 협조 하에 turbo-molecular 펌프와 cryopump측정 단에 각각 1대를 설치 완료하였으며 현재까지 운용 중이다. PC에 저장된 시계열 상태변수 database는 기 개발된 적응형 인자모델을 이용한 매개변수로 변환되며, 상태진단은 변환된 매개변수를 이용하여 수행할 예정이다.