  The recent increase in the use of aerial vehicles raises concerns about the
safety and reliability of autonomous operations. There is a growing need for
methods to monitor the status of these aircraft and report any faults and
anomalies to the safety pilot or to the autopilot to deal with the emergency
situation. In this paper, we present a real-time approach using the Recursive
Least Squares method to detect anomalies in the behavior of an aircraft. The
method models the relationship between correlated input-output pairs online and
uses the model to detect anomalies. The result is an easy-to-deploy anomaly
detection method that does not assume a specific aircraft model and can detect
many types of faults and anomalies in a wide range of autonomous aircraft. The
experiments on this method show a precision of 88.23%, recall of 88.23%, and
86.36% accuracy for over 22 flight tests. The other contribution is providing a
new fault detection open dataset for autonomous aircraft, which contains
complete data and the ground truth for 22 fixed-wing flights with eight
different types of mid-flight actuator failures to help future fault detection
research for aircraft. The source code and the dataset can be accessed from
https://theairlab.org/fault-detection/.
