• 사물인터넷융복합논문지
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• 제6권1호
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• pp.23-30
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• 2020

본 논문에서는 주택이나 건물의 에너지 손실 요인을 발견하고 이를 개선하기 위한 방안을 도출하기 위한 에너지 관리 방향을 제시하고 있다. 대중성과 편의성이 큰 강점인 스마트폰 탈부착형 열화상 카메라를 주택 전문가들만이 아닌 일반인들이 생활 속에서 활용할 수 있도록 생활 속 활용 진단 콘텐츠 앱을 설계하였다. 이 앱을 활용한 진단 결과를 에너지 효율 진단 솔루션인 에너지 파인더를 이용하여 분석하였다. 에너지 복지 사각지대 주민 및 시설 중 특히 지역 노인들이 자주 이용하는 경로당을 대상으로 진단 조사하였다. 진단 대상은 전라남도 나주시에 위치한 경로당 600여개소 중 15년 이상 노후 시설 30개소를 선정하여 에너지 효율 진단을 실시하였다. 진단 결과는 전반적으로 양호한 편이었지만, 7등급으로 분류된 곳은 에너지 효율 개선을 위한 보완이 필요하다고 판단된다.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1998년도 춘계학술발표회논문집(1)
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• pp.273-278
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• 1998

Belief network(or Bayesian network) based on Bayes' rule in probabilistic theory can be applied to the reasoning of diagnostic systems. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.613-618
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• 2018

We are developing a shared remote control mobile robot for aerial work in nuclear power plants (NPPs); a robot consists of a mobile platform, a telescopic mast, and a dual-arm slave with a working tool. It is used at a high location operating the manual operation mechanism of a fuel changer of a heavy water NPP. The robot system can cut/weld a pipe remotely in the case of an emergency or during the dismantling of the NPP. Owing to the challenging control mission considering limited human operator cognitive capability, some remote tasks require a shared control scheme, which demands systematic software design and integration. Therefore, we designed the architecture of the software systematically.

• 한국물환경학회지
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• 제38권5호
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• pp.231-239
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• 2022

In this study, we applied ENERWATER to evaluate the energy consumption labeling of wastewater treatment plants in Korea using the Korea sewerage statistics data. The results showed that the energy label status was excellent in the SBR process for small and medium-scale wastewater treatment plants and the A2O process for large-scale wastewater treatment plants. The energy labeling of wastewater treatment plants of 50,000 tons capacity was excellent. The statuses of metropolitan cities and Jeollanam-do province were excellent. We analyzed the effects of renewable energy on wastewater treatment plants' energy consumption and found out that digestion gas for large-scale plants and photovoltaic energy for small-scale plants were effective in improving energy labeling. In addition, we compared the energy labels of four wastewater treatment plants in "Z" city and wastewater treatment plant "X" had the best energy label, and the wastewater treatment plants "V" and "Y" had to be selected as priorities for the energy diagnosis and improvement project. In a comprehensive conclusion, the applicability of ENERWATER was confirmed based on sewage statistics data and labeling can be used to set priorities for the energy diagnosis and improvement project.

• KIEAE Journal
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• 제16권4호
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• pp.71-77
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• 2016

Purpose : The energy saving in a residential building (apartment) sector is known as one of the effective solution of energy reduction. In South Korea, the government has recently reinforced regulations associated with the energy performance of buildings. However, there is a lack of research on the methods for the energy performance diagnosis that is used to analyze the wall thermal performance of the existing apartments. Because a reliable diagnosis is necessary to save the building energy, this study analyzed wall thermal performance of an existing apartment in Seoul. Method : This paper applied two methods for analysis of the thermal insulation performance; HFM(Heat Flow Meter) method and ASTR(Air-Surface Temperature Ratio) method. The HFM method is suggested by ISO9869-1 code to measure the thermal performance. The ASTR method is proposed by this study for the simplified In-situ measurement and it uses three temperature data (interior wall surface, interior and exterior air) and the overall heat transfer coefficient. This study conducted the experiment of an existing apartment in Seoul using these methods and analyzed the results. Furthermore, the energy simulation tool of the building was used to suggest retrofit of the building based on the results of measurements. Result : The error rate of HFM method and ASTR method was analyzed in about 17 to 20%. As the results of comparison between the initial design values of the wall and the measured values, the 26% degradation of insulation thermal performance was measured. Lastly, the energy simulation tool of the building shows 10.8% energy savings in accordance with the construction of suggested retrofit.

• 한국수소및신에너지학회논문집
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• 제28권3호
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• pp.252-272
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• 2017

Fuel cell systems have to satisfy acceptable operating reliability, sufficient lifetime and price to enter the market in competition with existing products. Fuel cells are made up of complex element technologies and various problems related to the failure of the components can affect the reliability and safety of the system. This problem can be overcome by introducing a monitoring and supervisory control system in addition to automatic control to detect the failure of the fuel cell quickly and properly diagnose the performance degradation. For the fault detection and diagnosis of polymer electrolyte fuel cells, the model based method using the theoretical superposition value and the non-model based method of checking the signal tendency or the converted signal characteristic can be applied. The methods analyzed in this paper can contribute to the development of integrated monitoring and control technology for the whole system as well as the stack.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.345-361
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• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• 전기학회논문지P
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• 제56권4호
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• pp.168-172
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• 2007

The global environment deteriorating which originated from using of fossil fuel is an serious problem for human being to solve. The photovoltaic energy has been considered as a solution. In advanced countries, research and development for photovoltaic(PV) energy system is carrying on. Once installed, a PV array requires maintenance and fault diagnosis other than an occasional cleaning. In this research, the proposed system monitor and diagnosis the output of PV array by on-line for maintenance of PV power plant. The validity of proposed system is verified using sample system.

• 한국태양에너지학회 논문집
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• 제38권3호
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• pp.21-28
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• 2018

The objects of PV (Photovoltaic) monitoring system is to reduce the loss of system and operation and maintenance costs. In case of PV plants with configured of centralized inverter type, only 1 PV module might be caused a large loss in the PV plant. For this reason, the monitoring technology of PV module-level that find out the location of the fault module and reduce the system losses is interested. In this paper, a fault diagnosis algorithm are proposed using thermal and electrical characteristics of PV modules under failure. In addition, the monitoring system applied with proposed algorithm was constructed. The wireless sensor using LoRa chip was designed to be able to connect with IoT device in the future. The characteristics of PV module by shading is not failure but it is treated as a temporary failure. In the monitoring system, it is possible to diagnose whether or not failure of bypass diode inside the junction box. The fault diagnosis algorithm are developed on considering a situation such as communication error of wireless sensor and empirical performance evaluation are currently conducting.

• Nuclear Engineering and Technology
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• 제35권5호
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• pp.454-470
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• 2003

In this paper, a preliminary study for development of a fault diagnosis is presented for monitoring and diagnosing faults in the agitator driving system of a high temperature reduction reactor. In order to identify a fault occurrence and classify the fault cause, vibration signals measured by accelerometers on the outer shroud of the agitator driving system are firstly decomposed by wavelet transform (WT) and the features corresponding to each fault type are extracted. For the diagnosis, the fuzzy ARTMAP is employed and thereby, based on the features extracted from the WT, the robust fault classifier can be implemented with a very short training time - a single training epoch and a single learning iteration is sufficient for training the fault classifier. The test results demonstrate satisfactory classification for the faults pre-categorized from considerations of possible occurrence during experiments on a small-scale reduction reactor.