• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2007년도 동계학술발표대회 논문집
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• pp.149-154
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• 2007

Now, in Korea, the performances of the building service equipment relay on the individual superintendent's share for the assessment of performance, fault detection, deterioration diagnosis of the building service equipment. As the result, very different quality of the performance or the durability of equipment is being obtained with his skill and effort and it is also not easy to assess that quality. This finally lead to the waste of labor force and the operating cost due to the high-cost, low-efficiency system. How to construct the N-BMS was considered to save energy, resource and to conserve performance of building service equipment. The FEMIS, facility, energy/environmental management & information system, for building service offer management process integrated with BAS, FMS and EMS and so on.

• 한국가스학회지
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• 제12권3호
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• pp.1-6
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• 2008

갈수록 성장하고 있는 LNG 산업에 있어서 안전관리는 필수적인 요소로 자리잡아가고 있다. 이에 따라 기존의 LNG 저장/수송 시설에 대한 안전 관리를 보다 발전시킬 수 있는 것이 필요하며 최근 발전된 IT 산업은 LNG 저장/수송 시설에 대한 통합 안전관리 시스템을 가능케 했다. 위험성 평가/분석 기술, 폭발, 누출 및 확산 모델 구축 기술, 실시간 모니터링 및 이상 진단 기술, Data reconciliation을 통한 공정 정보 신뢰성 향상 기술 등을 집약하며 웹 환경을 통하여 구축될 통합 안전관리 시스템은 LNG 산업의 안전성 향상과 향후 기술 수출에 큰 기여를 할 것이다.

• ETRI Journal
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• 제43권3호
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• pp.459-470
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• 2021

This paper presents a low-cost prototype for monitoring online the maximum power produced by a domestic photovoltaic (PV) system using Internet of Things (IoT) technology. The most common tracking algorithms (P&O, InCond, HC, VSS InCond, and FL) were first simulated using MATLAB/Simulink and then implemented in a low-cost microcontroller (Arduino). The current, voltage, load current, load voltage, power at the maximum power point, duty cycle, module temperature, and in-plane solar irradiance are monitored. Using IoT technology, users can check in real time the change in power produced by their installation anywhere and anytime without additional effort or cost. The designed prototype is suitable for domestic PV applications, particularly at remote sites. It can also help users check online whether any abnormality has happened in their system based simply on the variation in the produced maximum power. Experimental results show that the system performs well. Moreover, the prototype is easy to implement, low in cost, saves time, and minimizes human effort. The developed monitoring system could be extended by integrating fault detection and diagnosis algorithms.

• 한국항공우주학회지
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• 제45권5호
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• pp.409-416
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• 2017

공중의 비행체에 필요한 정보를 전달하는 광대역 송신시스템은 근거리에서 동시에 운용될 수 있도록 다수의 부채널 주파수를 확보하고 있다. 취약한 외부환경에 노출되는 송신기의 경우, 시스템의 신뢰성을 높이기 위하여 안테나를 포함하여 다른 내부의 하위 구성품들의 자체고장진단을 할 수 있도록 설계되어야 한다. 안테나 자체고장진단은 보통의 경우 안테나 반사세기를 기준으로 판정 내리는데, 증폭단과 안테나가 긴 길이의 케이블로 연결될 경우는 안테나 반사세기가 주파수마다 많은 차이가 발생된다. 본 논문에서는 증폭단과 안테나의 연결에 사용되는 케이블 길이에 따라 안테나 반사세기가 주기성을 갖는 현상을 이론적으로 살펴보고, 반사세기 반복주기를 기반으로 점검주파수 범위설정, 다수 주파수 설정 점검 등을 이용하여 효과적인 안테나 고장진단 방법에 대하여 제시한다.

• 한국가스학회지
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• 제28권2호
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• pp.93-104
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• 2024

산업현장에서 사용되는 회전기기의 원활한 작동 및 수명연장을 위해서는 설비진단이 필수적이다. 다양한 설비진단의 방법 중 진동진단은 다른 진단방법과 비교하여 불평형(unbalance), 축정렬 불량(misalignment), 베어링 결함(bearing fault), 기어 손상(worn gears), 소음(noise), 공진(resonance) 등 대부분의 초기 결함을 발견할 수 있다. 따라서, 진동분석은 산업현장에서 가장 범용적으로 사용되는 설비진단 방법이며, 설비의 상태를 기반으로 관리하는 예지보전(PdM) 기술로 유용하게 활용된다. 하지만, 진동진단 방법은 기준을 근거로 경험에 의존하여 수행되기 때문에 전문가에 의하여 진행된다. 따라서, 기존에 경험에 의존하여 수행하는 진동진단 방법을 지식화된 코드체계로 구축하여 누구나 쉽게 결함을 판단할 수 있는 시스템을 구축하여 설비의 신뢰성 구축에 기여하고자 한다. 진동측정에 대한 ISO-20816 기준을 근거로 알고리즘을 개발하였고, 석유화학공장 압축기, 수소충전소, 산업용 기계 등 다양한 실증현장에서 진동을 측정한 결과와 개발 시스템을 활용하여 분석한 결과를 비교하여 신뢰성을 검증하였다. 개발된 알고리즘을 통하여 산업현장에서 누구나 회전기기의 상태를 진단하고 결함을 조기에 파악하여 정확한 교체시점에 부품을 교체할 수 있는 예측유지보수(PdM)기술에 기여할 수 있다. 나아가, 정유산업시설, 운송, 생산 시설, 항공시설 등 다양한 산업현장에 적용 시 회전기기의 고장으로 인한 유지보수 비용과 다운타임(down time)의 절감에 이바지할수 있를 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1014-1035
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• 2013

With recent advances in wireless communication and low-power miniaturized biomedical sensor and semiconductor technologies, wireless body area networks (WBAN) has become an integral part of the disaster aid system. Wearable vital sign sensors can track patients' status and location, thus enhancing disaster rescue efficiency. In the past few years, most of the literatures in the area of disaster aid system based on WBAN have focused on issues concerning wireless sensor design, sensor miniaturization, energy efficiency and communication protocols. In this paper, we will give an overview of disaster aid, discuss about the types of network communication as well as outline related issues. We will emphasize on analyzing six key issues in employing the disaster aid system. Finally, we will also highlight some of the challenges that still need to be addressed in the future in order to help the disaster aid system be truly and widely accepted by the public.

• Environmental Engineering Research
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• 제11권2호
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• pp.63-76
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• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

• Transactions on Electrical and Electronic Materials
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• 제14권2호
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• pp.71-77
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• 2013

Advanced semiconductor manufacturing technology requires methods to maximize tool efficiency and improve product quality by reducing process variability. Real-time plasma process monitoring and diagnosis have become crucial for fault detection and classification (FDC) and advanced process control (APC). Additional sensors may increase the accuracy of detection of process anomalies, and optical monitoring methods are non-invasive. In this paper, we propose the use of a chromatic data acquisition system for real-time in-situ plasma process monitoring called the Plasma Eyes Chromatic System (PECS). The proposed system was initially tested in a six-inch research tool, and it was then further evaluated for its potential to detect process anomalies in an eight-inch production tool for etching blanket oxide films. Chromatic representation of the PECS output shows a clear correlation with small changes in process parameters, such as RF power, pressure, and gas flow. We also present how the PECS may be adapted as an in-situ plasma arc detector. The proposed system can provide useful indications of a faulty process in a timely and non-invasive manner for successful run-to-run (R2R) control and FDC.

• 한국기계가공학회지
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• 제20권7호
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• pp.80-88
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• 2021

The behavior of the injection system was determined through FFT and PSD analysis of the pressure data of the common rail, and when the diesel fuel is mixed with water, the pressure data of the common rail, depending on the water content and engine rotation speed, represent a different frequency component distribution. Recently, a theory has been suggested that mixing diesel fuel with water controls engine overheating, fuel efficiency, NOx, CO, etc., but if water content exceeds 10%, it can have a fatal adverse effect on the engine's injection system. In the future, it is necessary to promote fault diagnosis and prediction studies of diesel engines using FFT and PSD results from common rail pressure data.

• 한국기계가공학회지
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• 제19권11호
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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.