Real-time onboard propeller fault diagnosis of autonomous delivery drones through vibration analysis

Delivery drones have become increasingly important in recent years. It is advantageous for commercialization that suppliers are able to deliver orders autonomously and directly to their customers via air transport. However, the safety aspect must be considered. Real-time inspection of delivery drones during operation helps preventing accidents and a threat to civilians. For continuous monitoring, the sensors must be installed on the drone throughout the flight. A promising approach uses the inherent excitation of the servomotors for vibration-based Structural Health Monitoring (SHM). Vibrations can be recorded using triaxial acceleration sensors and analyzed using suitable methods such as stochastic subspace-based fault detection or histogram difference. In comparison to nondestructive testing, reference measurements of the intact structure are necessary in SHM. As soon as laboratory conditions no longer exist and environmental parameters, for example, wind, influence the vibration spectrum, classification methods are necessary for compensation. The recording of comprehensive reference datasets with different environmental conditions is limited by the battery life. This work focuses on diagnosing irreversible rotor blade damages of an 8.5 kg delivery drone. A parametric analysis taking into account systematic fusion of damage indicators and a specific number of considered references were determined for this purpose in order to assess the severity of the existing damage. Onboard SHM to evaluate the airworthiness in real time for linear and hovering flights was achieved.