• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.556-561
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• 2009

The UAV(Unmanned Aerial Vehicle) that is remotely operating in various and long flight environments must have a very reliable propulsion system. Precise fault diagnosis of the turbo shaft engine for the Smart UAV that has the vertical take-off, landing and forward flight behaviors can promote reliability and availability. This work proposes a new diagnostic method that can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. The proposed diagnostic method can detect not only single fault but also multiple faults.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.2
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• pp.247-253
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• 2015

The components of an aero-engine gas path cannot be monitored in a timely way due to a lack of real-time monitoring technologies. As an attempt to address this problem, we have conducted research on a condition monitoring technology based on the charging characteristics of particles in an aero-engine gas path, and emphatically analyze the formation of particles in an aero-engine gas path, the charging mechanism of carbon particles and the factors that influence the charge quantity and polarity. The verification experiments are performed on the simulated experiment platform and a turbo-shaft engine test bench. The results show the carbon particles' carry charge, and an obvious change in the total electrostatic charge level in the aero-engine gas path due to the increased carbon particles produced by burning or abnormal metal particles; the charge number is related to the size of particles, and the bigger carbon particles carry a negative charge and metal particles carry a positive charge; the change in engine power can lead to an obvious change in the level of electrostatic charge in the gas path, and the change in electrostatic charge results from the extra carbon particles formed in the rich-oil burning process. The research provides a reference for establishing the baseline of electrostatic charge while the engine runs on different power. The study also demonstrates the validity of the electrostatic monitoring technology and establishes a base for developing the application of electrostatic monitoring technology in aero-engines.

• Aerospace Engineering and Technology
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• v.8 no.1
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• pp.98-107
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• 2009

Vibration analysis of a turbo compressor test rig was carried out in order to investigate the vibrational characteristics of the compressor facility in KARI before conducting the compressor performance test of 5MW-class gas turbine engine for generation. The overall compressor test facility consists largely of inlet and exit ducts, a test section and a driving part. Vibration was measured with accelerometers at the test section and the driving part, especially at a main housing, a collector, a bearing carrier, a torquemeter, a gearbox, and an electric motor. Gap sensors are also installed to measure the rotordynamic characteristics of compressor shaft.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.149-153
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• 2005

In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be needed. In this study a component map generation method which may identify compressor map conversely from a performance deck provided by engine manufacturer using genetic algorithms was newly proposed. As a demonstration example for this study, the PW 206C turbo shaft engine for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle). In ordo to verify the proposed method, steady-state performance analysis results using the newly generated compressor map was compared with them performed by EEPP(Estimated Engine Performance Program) deck provided by engine manufacturer. And also the performance results using the identified maps were compared with them using the traditional scaling method. In this investigation, it was found that the newly proposed map generation method would be more effective than the traditional scaling method.

• Journal of the Korean Society of Propulsion Engineers
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• v.9 no.4
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• pp.81-88
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• 2005

In order to estimate the gas turbine engine performance precisely, the component maps containing their own performance characteristics should be needed. In this study a component map generation method which may identify compressor map conversely from a performance deck provided by engine manufacturer using genetic algorithms was newly proposed. As a demonstration example for this study, the PW 206C turbo shaft engine for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle). In order to verify the proposed method, steady-state performance analysis results using the newly generated compressor map was compared with them performed by EEPP(Estimated Engine Performance Program) deck provided by engine manufacturer. And also the performance results using the identified maps were compared with them using the traditional scaling method. When the performance analysis is performed at far away operation conditions from the design point, in case of use of e component map by the traditional scaling method, the error of the performance analysis results is greatly increasing. In the other hand, if in case of use of the compressor map generated by the proposed GAs scheme, the performance analysis results are closely met with those by the performance deck, EEPP.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1997.11a
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• pp.23-23
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• 1997

다목적으로 활용할 수 있는 분리축 방식의 터보축 엔진 개발을 위한 정상상태 해석 프로그램의 개발과 함께 동일 형식의 가스터빈엔진 시험장치를 이용한 실험을 통해 프로그램의 해석결과와 비교, 그 타당성을 입증하였다. 실험에 이용된 시험장치는 1단 원심형 압축기, Can형 연소기, 1단 Radial형 압축기 터빈 및 동력터빈으로 구성되어 있으며 출력은 3상 교류발전기를 통해 획득된다. 해석에 사용된 주요 구성품의 성능곡선은 시험장치 제작자로부터 획득된 자료를 이용하였으며, 경우에 따라 시험장치를 이용한 실험을 통하여 보정하였다. 시험장치를 이용한 실험결과를 프로그램 해석결과와 비교한 결과, 시험장치의 운용제한에 의해 실제 자동영역이 제한되기는 했으나, 압력비, 출력 등 주요 변수들에서 10% 미만의 오차를 보였다.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.95-95
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• 2010

In recent, there are tremendous requirements to improve the fuel economy of vehicle. For satisfaction of requirements, Hybrid Electric Vehicle or other technologies are suggested and implemented. However, it should be noted that almost 35% energy loss is occurred in the shape of exhaust gas as ever. For increase the efficiency of vehicle, it is certain that the exhaust gas energy should be recover, and generate energy. In previous studies, the technologies such as turbo-compound, thermoelectric and rankine cycle are suggested to recover the exhaust heat energy in vehicle. But, they focus on the conventional vehicle or parallel Hybrid Electric Vehicle. Series Hybrid Electric Vehicle has advantage that the engine and drive shaft are de-coupled. It means that the engine can be operated in high efficiency area regardless with vehicle states. Therefore, if rankine cycle is applied to series hybrid electric vehicle, operating condition of that becomes almost steady. So, in this study, theoretical analysis on the efficiency of rankine cycle applied to series hybrid electric city bus is carried and the energy recovered from exhaust gas during vehicle drive cycle is calculated.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.1
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• pp.43-50
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• 2007

In this study, Hybrid Separate Learning Algorithm(SLA) consisting of Support Vector Machine(SVM) and Artificial Neural Network(ANN) has been used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine in the off-design range considering altitude variation. Although the number of teaming data and test data highly increases more than 6 times compared with those required for the design condition, the proposed defect diagnostics of gas turbine engine using SLA was verified to give the high defect classification accuracy in the off-design range considering altitude variation.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.102-109
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• 2006

In this Paper, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. The system that uses the ANN falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the Separate Learning Algorithm(SLA) of ANN has been proposed by using SVM. This is the method that ANN learns selectively after discriminating the defect position by SVM, then more improved performance estimation can be obtained than using ANN only. The proposed SLA can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure.