  We present a dataset of several fault types in control surfaces of a
fixed-wing Unmanned Aerial Vehicle (UAV) for use in Fault Detection and
Isolation (FDI) and Anomaly Detection (AD) research. Currently, the dataset
includes processed data for 47 autonomous flights with 23 sudden full engine
failure scenarios and 24 scenarios for seven other types of sudden control
surface (actuator) faults, with a total of 66 minutes of flight in normal
conditions and 13 minutes of post-fault flight time. It additionally includes
many hours of raw data of fully-autonomous, autopilot-assisted and manual
flights with tens of fault scenarios. The ground truth of the time and type of
faults is provided in each scenario to enable evaluation of the methods using
the dataset. We have also provided the helper tools in several programming
languages to load and work with the data and to help the evaluation of a
detection method using the dataset. A set of metrics is proposed to help to
compare different methods using the dataset. Most of the current fault
detection methods are evaluated in simulation and as far as we know, this
dataset is the only one providing the real flight data with faults in such
capacity. We hope it will help advance the state-of-the-art in Anomaly
Detection or FDI research for Autonomous Aerial Vehicles and mobile robots to
enhance the safety of autonomous and remote flight operations further. The
dataset and the provided tools can be accessed from
https://doi.org/10.1184/R1/12707963.
