Aircraft anomaly detection using performance models trained on fleet data

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into a list of abnormaly performing aircraft, abnormal flight-to-flight trends, and individual flight anomalies by fitting a large scale multi-level regression model to the entire data set. The model takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, a multi-terabyte airline data set with a half million flights was processed in a few hours. The anomalies found include wrong values of computed variables such as aircraft weight and angle of attack as well as failures, biases, and trends in flight sensors and actuators. These anomalies were missed by the FOQA data exceedance monitoring currently used by the airline.