• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.3
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• pp.238-247
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• 2007

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to learning algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generator turbine and power turbine are considered for engine performance deterioration and estimation for performance deterioration of a single component at design point was conducted. As a result of that, defect diagnostics has been conducted. The input criteria for the genetic algorithm to guarantee the high stability and reliability was discussed as increasing learning data sets. As a result, the accuracy of defect estimation and diagnostics were verified with its RMS error within 3%.

• 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.

• Journal of Korea Ship Safrty Technology Authority
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• s.26
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• pp.24-35
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• 2009

본 연구에서는 소형선박용 고속디젤기관의 고장으로 발생되는 해양사고를 저감하기 위하여 엔진의 상태를 사전에 기관운전자가 알 수 있도록 시스템을 구성하여 기존의 엔진에 이상이 발생하였을때 가시가청 경보를 울리는 것보다 한 단계 전에 엔진의 상태를 미리 알 수 있도록 하여, 기관의 고장으로 인한 해양사고 저감에 목표를 두고 과제를 진행하였다. 또한, 대형선박에서 사용하고 있는 고가의 고성능을 발휘하는 고장예측진단시스템은 많이 개발되어 있으나, 영세한 소형어선에 적용할 수 있는 소형의 저렴한 시스템 개발에 최종목표를 두고 1차년도 연구과제를 수행하였다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.1165-1168
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• 2017

This paper suggests a fault diagnosis of damage on inner liner of regeneratively-cooled combustion chamber during gas generator cycle rocket engine hot firing test. This method focuses on a phenomenon that fuel flow rate difference between two flow estimate methods changes under an inner liner damage of combustion chamber causing fuel leakage and it is expected that it contributes to detect a damage on the combustion chamber in early stage and prevent further destruction during the hot firing test.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.213-217
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• 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 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.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.285-288
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• 2006

Genetic Algorithms(GA) which searches optimum solution using natural selection and the law of heredity has been applied to teaming algorithms in order to estimate performance deterioration of the aircraft gas turbine engine. The compressor, gas generation turbine and power turbine are considered for estimation for performance deterioration of a complex component at design point was conducted. As a result of that, complex defect diagnostics has been conducted. As a result, the accuracy of diagnostics were verified with its relative error with in 10% at each component.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.745-745
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• 2012

원자력발전소에서 비상디젤발전기는 노심의 안전성을 지키는 매우 중요한 역할을 담당하고 있다. 이를 위해 디젤발전기는 신뢰성능 높게 유지하도록 규제요건이 마련되어 있다. 디젤발전기의 엔진 상태를 주기적으로 감시하고 평가하기 위해서는 엔진 상태 신호 분석 기술이 필요하다. 엔진 상태 신호 분석에는 연소분석과 진동 및 초음파 측정 및 분석기술이 중요한 비중을 차지한다. 연소 분석은 디젤 엔진의 개별 실린더에 대한 연소 성능에 대한 정보를 제공한다. 진동 및 초음파 분석은 이벤트 타이밍과 기계적 상태에 대한 정보를 알려준다. 이들 신호는 정상적인 부하로 운전하는 디젤엔진의 가동에 영향을 미치지 않고 수집할 수 있다. 엔진 상태 신호 분석을 이용하는 주요 동기는 전통적으로 장비 제작자의 권고에 따라 수행되는 분해-검사 유지 보수 프로그램을 일부 대체하고 예측정비를 통해 신뢰도를 유지하기 위함이다. 상태 진단정비는 엔진 상태 신호분석을 주로 이용하여 엔진의 신뢰도와 이용률을 증가시킬 수 있다. 본 논문에서는 국내외서 경험한 엔진상태신호 측정 및 분석 사례를 기술하였다.