Despite the camera being ubiquitous to unmanned aerial vehicles (UAVs), it has not been used for fault detection and diagnosis (FDD) due to the nonexistence of UAV multi-sensor datasets that include actuator failure scenarios. This thesis proposes a knowledge-based FDD framework based on a lightweight Long-Short Term Memory network that fuses camera and Inertial Measurement Unit (IMU) information. Camera data is pre-processed by extracting features from its optical flow. Short-Time Fourier Transform is applied on the IMU data for obtaining its time-frequency information. For training and assessing the proposed framework, UUFOSim was developed: an Unreal Engine-based simulator built on AirSim that allows the collection of high-fidelity photo-realistic camera and sensor information with the possibility of injecting in-flight actuator failures. To simulate blade damage, a Blade Element Theory (BET) model is introduced as plug-in which enables any level of blade damage simulation without costly experimental data. The BET model was validated with static test stand wrench measurements obtained at 3 levels of blade damage (0%, 10%, 25%) from a dedicated wind tunnel experimental campaign in the Open Jet Facility of TU Delft with velocities up to 12 m/s. In the presence of blade damage, at high propeller rotational speeds the BET model shows a relative error between 5% and 24%. At low propeller rotational speeds, the relative error oscillates between 15% and 75%. Results of the FDD framework trained on 5,000 simulated flights demonstrate the added value of the camera and the complementary nature of both sensors with failure detection and diagnosis accuracies of 99.98% and 98.86%, respectively.

Major delivery companies such as DHL, UPS or Amazon have been developing small drones to deliver packages. This alternative to truck delivery is expected to start operating in the near future. The advantages of it are its speed, price, safety and sustainability: parcels would not be subjected to traffic and they would be delivered within an hour, it is 10% less expensive and it means a 73% reduction in CO2 emissions when compared to truck delivery, as well as a relieve in the road traffic network. The only drawback is that the noise produced by current drones is deemed by humans as more annoying than car noise due to its high frequency. The mission of the Silent Delivery Drone project is to provide a drone delivery system that is faster, less expensive and has lower emissions than truck delivery while complying with Dutch noise regulations. The presented innovative configuration is a combination of a quadcopter, suitable for Vertically Taking-Off and Landing (VTOL) in densely populated regions, and a flying wing, optimized for the cruise phase. It consists of a horizontal propeller used during cruise and four vertical propellers for VTOL. The drone can carry a payload of up to 2.5 kg, which corresponds to 89% of the packages delivered yearly worldwide. Four packages can be delivered while flying the maximum range of 30 km. Thanks to the low required revolutions per minute, the absolute maximum noise caused by the drone is 58 dBA at take-off from 7.5 m and 25 dBA during cruise from a distance of 120 m. This meets the Dutch night noise regulations which stablish a peak noise level of 65 dBA and average noise level of 40 dBA. We believe that a fleet of Silentium drones would revolutionize the way we perceive package delivery and it would mark the next step towards a greener, smarter and more connected future.