Abstract:

The recent incidents with Boeing 737 Max 8 aircraft have raised concerns about the safety and reliability of autopilots and autonomous operations. There is a growing need for methods to monitor the status of aircraft and report any faults and anomalies to the human pilot or to the autopilot to deal with the emergency situation. We present an online 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 and uses the model to detect the anomalies. The result is an easy-to-deploy anomaly detection method that does not assume a specific aircraft model, does not require any training, works in real-time and can detect many types of faults and anomalies in a wide range of autonomous aircraft, including fixed-wing planes, multirotors and VTOLs. Extensive experimentation in flight tests shows that the method can detect the faults with a high rate and low latency.

Speaker Bio: