• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2002.04a
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• pp.25-26
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• 2002

가스터빈 엔진의 성능모사는 개발단계에서의 개발위험도 감소나 개발비용 절감의 목적뿐만 아니라 운용단계에서 최적의 정비를 위한 엔진의 상태 진단을 위해서도 그 필요성이 매우 크다. 가스터빈 엔진의 성능모사를 위해서는 "각 구성품의 성능은 구성품 성능선도를 따라야 한다"는 제약조건을 만족해야 하며 이를 위해서는 구성품의 특성을 나타내는 성능선도가 매우 중요하다. 구성품 성능선도는 다양한 조건에서의 성능시험을 통해 얻는 것이 당연하나 이는 엔진 개발사에서나 가능하며 대부분 공개를 기피한다. 따라서 일반 구매자나 가스터빈 엔진의 성능을 연구하는 사람들은 일부 공개된 성능도를 축척하여 이용하고 있다.여 이용하고 있다.

• Journal of Aerospace System Engineering
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• v.7 no.1
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• pp.8-18
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• 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 %.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.515-516
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.306-307
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.706-708
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• 2011

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.29-34
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• 2012

Nowadays, the problem of ETCS terminal in becoming popular gradually is that there is no services except for ETC. Therefore, we need new system that provide many type of additional services at one-terminal. In this paper, we study the additory function of ETCS terminal to afford many type of the vehicle administration beside collection and provider of traffic information. We descrived the method of Eco-driving function beside to save fuel signing instant and mean fuel-efficiency, measurement of section fuel-efficiency on OLED and then brings out the best driving habit in people and to prevent dangerous at the wheel as diagnosing engine oil, cooling water, fan belt, the point of changing consumables, diagnoses to an overheated engine, charges on generator through ECU. The multi-services terminal consist of the vehicle diagnosis module base on OBD-II and ETCS terminal.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.1
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• pp.105-113
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• 2002

In this study, the steady state performance analysis program was developed for a turboprop engine, and its performance was analyzed at uninstalled and installed conditions. For the purpose of evaluation the developed program was compared with the performance data provided by the engine manufacturer and analysis results of GASTURB8.0 program. It was confirmed that the developed program was reliable because the results by the developed program were well agreed with those by GASTURB8.0 within %%. The linear and non-linear GPA(Gas Path Analysis) programs for performance diagnotics were developed, and selection of optimal measurement variables was studied. Furthermore, in order to investigate effects of the number and the kind of measurement variables, the linear and non-linear GPAs were analyzed with various measurement set. If the measurement parameters were properly selected, the reliable and economic faults detection might be possible even thought the small number of measuring parameters were used.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.289-292
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• 2006

In this paper, the hybrid algorithm of Support Vector Machine md Artificial Neural Network is used for the defect diagnostics algorithm for the aircraft turbo-shaft engine. The results of learning of ANN, especially, accuracy or speed of convergence are sensitive to the number of data, so a comparison between design point and off-design area, especially, Mach number and fuel flow variable area, is essential research. From application results for diagnostics of gas turbine engine, it was confirmed that the hybrid algorithm could detect well in the of-design area as well as design point.