Vibration Anomaly Detection by Clustering in Unmanned Aerial Vehicles.
One of the critical factors affecting flight safety of unmanned aerial vehicles (UAVs) is the amount of vibration they are exposed to during a flight. On one hand, external causes such as wind gusts and turbulences or internal vehicle-centric faults such as incorrect sensor mounting or propeller imbalances can cause high vibrations in UAVs. On the other hand, high vibration itself may induce noise in the onboard miniature sensors of the UAV such as its accelerometers, gyroscopes and GPS that can lead to uncertain state estimation causing the multi-rotor to drift from its desired position or even result in loss-of-control. Hence, it is important to monitor the vibration levels during a UAV flight. This paper specifically looks into vibrations recorded by the autopilot system of a multi-rotor in presence of varying magnitudes of wind. Using data from experimental flights conducted at two separate flight test regions under varying wind conditions, we aim to classify between a nominal and anomalous vibration level for small UAV systems. Further, we analyse other parameters of interest that affect vibrations in UAVs such as UAV air speed and any propeller imbalance signatures. Analysis results from experimental flights demonstrate the effect of wind on vibration magnitude in unmanned aircrafts.