• Transactions of the Korean hydrogen and new energy society
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• v.28 no.3
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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.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.589-596
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• 2019

X-rays used for diagnosis have a continuous energy distribution. However, photons with low energy not only reduce image contrast, but also contribute to the patient's radiation exposure. Therefore, clinics currently use filters made of aluminum. Such filters are advantageous because they can reduce the exposure of the patient to radiation. However, they may have negative effects on imaging quality, as they lead to increases in the scattered dose. In this study, we investigated the effects of the scattered dose generated by an aluminum filter on medical image quality. We used the relative standard deviation and the scatter degradation factor as evaluation indices, as they can be used to quantitatively express the decrease in the degree of contrast in imaging. We verified that the scattered dose generated by the increase in the thickness of the aluminum filter causes degradation of the quality of medical images.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.2
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• pp.15-20
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• 1998

The furfural, generated by decomposition of insulating paper, the amount of metal in insulating oil, and tano(electrical properties of insulating oil)have been studied for the insulating oil in pole transformer with accelarated thermal aging test. It has been found that tan $\delta$ is affected by adding components of the transformer. The examination of amount of metal, which exhibits catalytic behavior to oxidation of insulating oil, suggested that the amounts of copper increase with degradation time. A comparison between tano and copper amount suggested that the amounts of copper for attention are above 0.2[pp]). Finally, the examination of amount of furfural revealed that the amounts of furfural increase with degradation time. As a consequence, these results could be used for diagnosis of pole transformer.former.

• Korean Chemical Engineering Research
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• v.49 no.6
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• pp.775-780
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• 2011

5-cell DMFC stack was fabricated and operated with the load of 4 A for 4000 hrs. After 4000 hrs operation peak power density of the stack reduced by 27.3%. Two of the five cells did now show performance degradation, the performance of other two was reduced by 40% and the performance of the other decreased by 60%. The amount of performance degradation of each cell by long-term operation did not correlate with the position in the stack. Platinum particle size in the anode catalyst layer of the MEA with the strongest degradation increased and the increase was severer on the position of methanol inlet than on the position of methanol outlet. However, platinum particle size in the cathode catalyst layers did not changed for all the MEA'. Ruthenium crossover from the anode catalyst layer to the cathode catalyst layer through the membrane was observed after 4,000 hrs operation by SEM-EDX and it occurred for all MEA' regardless of the degree of performance degradation. Atomic ratio of ruthenium to platinum in the cathode catalyst layer was the highest in the MEA with the strongest performance degradation.

• KIEAE Journal
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• v.16 no.4
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4576-4582
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• 2010

Because most of vehicle provide users with the very limited information regarding the performance of vehicle, it is quite difficult for users to drive vehicles safe, and to maintain and repair vehicles properly. In order to solve the above-mentioned problems, several ways of research and development for the vehicle control and diagnosis system have been recently carried out. However, a lot of complicated problems and difficulties were arising due to the complexity of the developed system, degradation of the reliability for the vehicle performance control system, operational malfunction and so on. In this paper, for the purpose of solving the difficult problems and technical limitations, a system for vehicle performance which might be able to diagnose the reliability of vehicle performance by measuring and analyzing the real time performance of vehicle using the satellite telemetry technology was conmance oly defined and deehiced.hihe results derived from the cormance ofdvehiclactivities in this study shall be used as not only fundamental data but also materials for the detailed design for the implementation of vehicle performance diagnosis system in the near future.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.4
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• pp.311-320
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• 2021

A study is performed for the real time fault diagnosis during operation and health estimation relating to performance deterioration in a turbojet engine used for an unmanned air vehicle. For this study the real time dynamic model is derived from the transient thermodynamic gas path analysis. For real fault conditions which are manipulated for the simulation, the detection techniques are applied such as Kalman filter and probabilistic decision-making approach based on statistical hypothesis test. Thereby the effectiveness is verified by showing good fault detection and isolation performances. For the health estimation with measurement parameters, it shows using an assumed performance degradation that the method by adaptive Kalman filter is feasible in practice for a condition based diagnosis and maintenance.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11b
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• pp.270-273
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• 2001

Electrical tree is studied widely by manufacture state of insulating material fare and blazing fire diagnosis system of use in phenomenon of part discharge that happen for main cause of dielectric breakdown of equipment for electric power. Use process that draw tree pattern here measuring above zero to study special quality of this electricity tree, real-time processing by image processing is proposed because reproduction of tree blazing fire process drops and pattern of tree is difficult correct quantification of tree growth by existent visual observation by involution. This research presents general process that need in image processing of tree blazing fire, and that remove various noises that happen in above zero by measuring electrical tree dividing background and complete view in measured above zero taking advantage of specially proposed complete view object abstraction techniques effectively and quantification of tree becomes easy naturally, can apply to dielectric breakdown estimate because can chase growth process of tree.