Defect diagnostics of SUAV gas turbine engine using hybrid SVM-artificial neural network method

Lee, Sang-Myeong; Roh, Tae‐Seong; Choi, Dong-Whan

doi:10.1007/s12206-008-1119-9

Cited by 25 publications

(13 citation statements)

References 16 publications

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“…If these bigger faults cannot be prevented in the aircraft, it can lead to high maintenance costs and accidents. When aircrafts are taken for maintenance, the condition of the gas turbine engine is investigated by various tests and measurements [7]. Condition-based maintenance (CBM) is being performed to provide effective and efficient maintenance in today's maintenance services.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Yildirim

Bulent

2018

International Journal of Aerospace Engineering

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Modern condition monitoring-based methods are used to reduce maintenance costs, increase aircraft safety, and reduce fuel consumption. In the literature, parameters such as engine fan speeds, vibration, oil pressure, oil temperature, exhaust gas temperature (EGT), and fuel flow are used to determine performance deterioration in gas turbine engines. In this study, a new model was developed to get information about the gas turbine engine's condition. For this model, multiple regression analysis was carried out to determine the effect of the flight parameters on the EGT parameter and the artificial neural network (ANN) method was used in the identification of EGT parameter. At the end of the study, a network that predicts the EGT parameter with the smallest margin of error has been developed.

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Section: Introductionmentioning

confidence: 99%

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Yildirim

Bulent

2018

International Journal of Aerospace Engineering

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“…Model-based methods typically includes linear gas path analysis [4][5][6], nonlinear gas path analysis [7,8], Kalman filters [9,10] and expert systems [11]. Data driven-based methods typically include artificial neural networks [12,13], support vector machine [14,15], Bayesian approaches [16,17], genetic algorithms [18] and fuzzy reasoning [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method

Wang

Zhou

et al. 2017

Energies

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Abstract:Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering the collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is used to evaluate posterior probabilities of observing symbolic sequences and the most probable state sequences they may locate. Hence an estimation-based model and a decoding-based model are used to identify anomalies in two different ways. Experimental results indicate that both models have both ideal performance overall, but the estimation-based model has a strong robustness ability, whereas the decoding-based model has a strong accuracy ability, particularly in a certain range of sequence lengths. Therefore, the proposed method can facilitate well existing symbolic dynamic analysis-based anomaly detection methods, especially in the gas turbine domain.

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“…Model based methods are typically includes linear gas path analysis [4][5][6], nonlinear gas path analysis [7,8], Kalman filters [9,10] and expert systems [11]. Data driven based methods typically include artificial neural networks [12,13], support vector machine [14,15], Bayesian approaches [16,17], genetic algorithms [18] and fuzzy reasoning [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection on Gas Turbine Fuel System Using a Sequential Symbolic Method

Li¹,

Wang²,

Zhou³

et al. 2017

Preprint

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Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is to evaluate posterior probabilities of observing symbolic sequences and most probable state sequences they may locate.Hence an estimating based model and a decoding based model are used to identify anomalies in two different ways. Experimental results indicates that these two models have both ideal performance overall, and estimating based model has a strong ability in robustness, while decoding based model has a strong ability in accuracy, particularly in a certain range of length of sequence. Therefore, the proposed method can well facilitate existing symbolic dynamic analysis based anomaly detection methods especially in gas turbine domain.

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Defect diagnostics of SUAV gas turbine engine using hybrid SVM-artificial neural network method

Cited by 25 publications

References 16 publications

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method

Anomaly Detection on Gas Turbine Fuel System Using a Sequential Symbolic Method

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