• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2007.11a
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• pp.345-349
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• 2007

In this study a fuzzy trend monitoring method for detecting the engine mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration. etc. Using engine condition data set as a input which generated by linear regression analysis of real engine instrument data, an application of fuzzy logic in diagnostics estimate a cause of fault in each components.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.6
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• pp.1-6
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• 2008

This work proposes a fuzzy trend monitoring method for the fault detection of a gas turbine engine through analyzing measured performance data trend. The proposed trend monitoring technique can diagnose the engine status by monitoring major engine measured parameters such as fuel flow rate, exhaust gas temperature, rotor rotational speed and vibration, and then analyzing their time deppendent changes. In order to perform this, firstly the measured engine performance data variation is formulated using Linear Regression, and then faults are isolated and identified using fuzzy logic.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.6
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• pp.431-441
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• 2020

In this paper, we propose a new method using a feedforward disturbance observer that guarantees stability and robustness about the effects of external disturbance and model uncertainty. The method is consist of a disturbance observer, a feedforward controller, and an overshoot detecting logic. It has an advantage of reducing the excessive overshoot by external disturbance and model uncertainty. Also, it is easy to adjust the control gain due to a simple structure. In order to verify the effectiveness of a new method, simulation results are given for longitudinal model of F-16 aircraft. By reflecting a various of model uncertainties, the stability and the robustness are guaranteed. Finally, the stability and the robustness of the proposed method are verified using root locus plot and bode plot.