• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.5
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• pp.108-120
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• 2017

The technology for health management of gas turbine engine has grown with engine development itself for 60 years and regarded as important area for performance monitoring and maintenance of the system. This technology which is based on several areas such as advanced measurement technology, electronics, software technology and reliable system modeling is realized. This paper analyzed the past, current and future technical trend of a technically advanced country and compared with domestic research status. Based on the analysis, the key research topics for the realization of technology is suggested.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.203-210
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• 2019

Most cars today use electronic control to control the state of the engine to achieve optimum performance. This study developed a device for maintaining fault diagnosis and optimal vehicle status by displaying the engine information of a car on the screen in real time using can communication. This system displays information generated from the engine to the driver in real time such as engine intake and exhaust temperature, current battery voltage, tire pressure, RPM, DPF collection amount, torque, and horsepower through the OBD2 socket. You can check immediately. It can help you to drive safely by measuring tire pressure and displaying it on the screen, and it provides a mode to set the shift timing to suit your taste. In particular, in the case of diesel engine cars, the problems caused by smoke can adversely affect the performance and environmental pollution. Therefore, the system was developed to display the DPF collection amount on the system screen to prevent environmental pollution and to manage the vehicle efficiently.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.6
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• pp.299-306
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• 2000

The primary cause of air pollution by vehicles is imperfect combustion of fuel. One of the most usual causes of this imperfect combustion is the misfire in IC(Intenal Combustion) engine. Recently it is obligated for an ECU to monitor the emission level and warn the driver in case of exceeding specified emission standards. Therefore, in order to comply with this OBD-II regulations, car makers are investing a considerable amount into technology which would enable the detection of misfire and the particular cylinder in which misfire is taking place. So far, it has been able to detect misfire using engine speed, which can be obtained crank angle. However, such a method posed a problem in analyzing at high speed and in recognizing the misfire from the load impact at bumpy road. In this paper, misfire detection is made possible by simple arithmetic using WDFT, especially at high engine speed. In addition, the moving window method of a Walsh function is applied to determine the cylinders under misfire in case of multiple misfires. An actual experiment was conducted to prove that WDFT is applicable to effective in computation speed and to same result in misfire detection and cylinder determination at idle, part load and bumpy road conditions.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.34-42
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• 2009

Gas turbine performance diagnostics is a method for detecting, isolating and quantifying faults in gas turbine gas path components. On-line precise fault diagnosis can promote greatly reliability and availability of gas turbine in real time operation. This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module are used for reducing computer calculating time and a signal generation module for simulating real time performance data. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. Evaluation of the proposed on-line diagnostic program is performed through application to the helicopter engine health monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1767-1772
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• 2008

As the interest on healthcare area in pursuit of well-being, health etc is increasing, u-Healthcare service that can monitor the condition of oneself's health anytime & anywhere is getting an attention. The existing health information system is consist of structure supporting the storage of estimation value and just simple display. But this needs to get a harmonious interaction to provide the health information exactly and speedily for efficient condition confirmation and diagnosis in health information system, In relation to this, this study proposed the developmental contents about visualization engine providing the beuer information discrimination and more user-friendly in various service of u-Healthcare.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.158-161
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• 2005

In this study, the defect diagnosis of the gas turbine engine was tried using Support Vector Machine(SVM). It is known that SVM can find the optimal solution mathematically through classifying two groups and searching for the Hyperplane of the arbitrary nonlinear boundary. The method for the decision of the gas turbine defect quantitatively was proposed using the Multi Layer SVM for classifying two groups and it was verified that SVM was shown quicker and more reliable diagnostic results than the existing Multi Layer Perceptron(MLP).

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.275-278
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• 1996

This fault diagnosis system consists of qualitative models, qualitative interpreter, and inference engine. Qualitative models are formed by analysis of the relationships between faults and behaviors of sensor trends, which are described by state transition trees. Qualitative interpreter outputs confidence factors with three qualitative quantities which represent the states of sensor trends. And then, the possible faults are detected by inference module which matches the states of trends within a window size with the qualitative models using the well-known min-max operation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.706-709
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• 1996

In this paper, an algorithm for PLC(Programmable Logic Controller) fault diagnosis system is proposed and experimentation is conducted with a PLC and a virtual plant. Wrong output backward tracking algorithm is proposed in order to find the external faults of PLC. And query with keywords of the fault systems and specially designed test sequence programs are used. We lay emphasis on the backward tracking algorithm to diagnose the faults of PLC. It is shown experimentally that the proposed algorithm can find the faults which a typical self diagnostics in the-commercially available PLC cannot.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.350-353
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• 2007

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideriation of the performance deterioration is consist of the compressor, the gas generation turbine and the power turbine, repectively. Compared to the on-design point on the sea-level condition, the learning data has been increased 200 times in case of the off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimum division has been proposed to decrease learning time as well as to obtain high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been estimated under 5 %.