A Model based approach for aircraft sensor fault detection

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2018
Serçekman, Ömür
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This thesis presents a reformative approach to a model-based fault detection and diagnosis (FDD) method that improves the capability of aircraft flight control systems and acquires low complexity and computational requirements. The main objective of the FDD techniques that are extensively applied in industrial systems is to increase the sensitivity of fault detection scheme with respect to additional noise, uncertainty or disturbances. The designed fault detection model is integrated to a civil aircraft model of Boeing 747. The developed system mainly consists of a nonlinear closed-loop aircraft model to verify the effectiveness of sensor fault detection technique, an observer to estimate states of the aircraft during steady state flight, a fault indicator to propagate faulty responses to the system and a reconfigurator to identify flight condition if it is faulty or fault-free by comparing the states which are achieved from sensors in real-time. Fault detection is accomplished by using mainly a Kalman filter as a linear observer design. The scheme presented based on Kalman filter decreases the effect of model uncertainty extensions, constitutes a residual sensitive to stuck faults and maintains a reliable fault detection approach incorporating the rejection of false alarm that is required for system reliability. The monitoring progression of the state estimation permits to observe any off-nominal system attitude and detects faults. The developed method is a viable solution for earlier detection of sensor stuck to lower threshold amplitude under multi- simulation tests performed in MATLAB Simulink.
Flight control., Airplanes, Guidance systems (Flight).
http://etd.lib.metu.edu.tr/upload/12622795/index.pdf
https://hdl.handle.net/11511/27682
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
Ö. Serçekman, “A Model based approach for aircraft sensor fault detection,” M.S. - Master of Science, Middle East Technical University, 2018.
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