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Flight Simulation of a MAKO UAV for Use in Data-Driven Fault Diagnosis

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Elgiz Baskaya
Murat Bronz
Daniel Delahaye

Résumé

Last decade witnessed the rapid increase in number of drones of various purposes. This pushes the regulators to rush for safe integration strategies in a way to properly share the utilization of airspace. Accommodating faults and failures is one of the key issues since they constitute the bigger chunk in the occurrence reports available. The hardware limitations for these small vehicles point the utilization of analytical redundancy rather than the usual practice of hardware redundancy in the conventional flights. In the course of this study, fault detection and diagnosis for aircraft is reviewed. Then a nonlinear model for MAKO aircraft is simulated to generate faulty and nominal flight data. This platform enables to generate data for various flight conditions and design machine learning implementations for fault detection and diagnosis.
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Dates et versions

hal-01610970 , version 1 (05-10-2017)

Identifiants

  • HAL Id : hal-01610970 , version 1

Citer

Elgiz Baskaya, Murat Bronz, Daniel Delahaye. Flight Simulation of a MAKO UAV for Use in Data-Driven Fault Diagnosis. IMAV 2017, 9th international microair vehicle conference, Sep 2017, Toulouse, France. ⟨hal-01610970⟩
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