The new era of small UAVs necessitates intelligent approaches towards the issue of fault diagnosis to ensure a safe flight. A recent attempt to accommodate quite a number of UAVs in the airspace requires to assure a safety level. 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. An approach of implementing machine learning practices to diagnose faults on a small fixed-wing is selected. The selection criteria behind is that, data-driven fault diagnosis enables avoiding the burden of accurate modeling needed in model-based fault diagnosis. In this study, first, a model of an aircraft is simulated. This model is not used for the design of Fault Detection and Diagnosis (FDD) algorithms, but instead utilized to generate data and test the designed algorithms. The measurements are simulated using the statistics of the hardware in the house. Simulated data is opted instead of flight data to isolate the probable effects of the controller on the diagnosis, which will complicate this preliminary study on FDD for drones. A supervised classification method, SVM (Support Vector Machines) is used to classify the faulty and nominal flight conditions. The features selected are the gyro and accelerometer measurements. The fault considered is loss of effectiveness in the control surfaces of the drone. Principle component analysis is used to investigate the data by reducing the feature space dimension. The training is held offline due to the need of labeled data. The results show that for simulated measurements, SVM gives very accurate results on the classification of loss of effectiveness fault on the control surfaces.
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