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Initial reliability assessment of a commercial-off-the-shelf GPS sensor for generic UAVs

With the rising adoption of multi-rotor UAVs, it has become ever more crucial that their airworthiness is ensured, especially for hobby-grade UAVs. For some UAVs, their onboard components might not be as reliable as required and as such, faults can occur during flight operations and may develop to cause catastrophe. Hence, these faults need to be accurately diagnosed and quickly mitigated. This paper presents a fault diagnosis model for a quadrotor subjected to partial actuator faults. Flight simulations, with actuator and GPS sensor fault injections, are performed on a hardware-in-the-loop experimental setup to gather flight data consisting of multiple sensors. Based on this data, a preliminary controllability threshold analysis is conducted for the quadrotor. After that, a fault diagnosis model using an online sequential fuzzy-extreme learning machine (OS-Fuzzy-ELM) is trained to locate the actuator faults on the quadrotor UAV. The trained model presents an average testing accuracy and macro-averaged F1 score of 80.2% and 78.0%. A subsequent study to isolate sensor and actuator faults presents the testing accuracy and macroaveraged F1 score to be 1.95% and 1.21%, marginally better than a fault diagnosis model based on a single-layer feedforward network

Airworthiness of multirotor unmanned aerial vehicles is of utmost importance for ensuring safe flight operations, especially in high-risk airspace. The propulsion system plays a critical role in determining the UAVs' stability and control, and their failures can render UAVs into significant hazards. Assessing the reliability of the propulsion system provides valuable insight into the overall airworthiness of the UAVs, benefitting both regulators and operators. Hence, this paper proposes a framework that integrates controllability analysis with Markov chain modeling to evaluate UAV reliability. The controllability analysis determines combinations of propulsion unit failures in which the UAV remains controllable, which are then modeled as Markov states. This framework is applied to a class of octorotor UAVs, comparing their reliability with other multi-rotor UAVs and examining the influence of different payloads. The results demonstrate the superior reliability of octorotor UAVs, emphasizing their increased suitability for high-risk airspace flight operations compared to other multirotor UAVs.

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