Detecting Aircraft Performance Anomalies from Cruise Flight Data

E. Chu, D. Gorinevsky, and S. Boyd

Proceedings AIAA Infotech@Aerospace, Atlanta, Georgia, April 20-22, 2010
Best Student Paper Award

We propose an approach to detecting anomalies from aircraft cruise flight data. The detection is based on a model learned from the historical data of a fleet of aircraft. For a variety of cruise flight conditions with and without turbulence, we validate the approach using a FOQA dataset generated by a NASA flight simulator. We identify a regression model that maps the flight conditions and aircraft control inputs into accelerations (linear and rotational). Anomalies are detected as outliers that exceed the scatter caused by turbulence and the modeling error. The detection method is related to multivariable statistical process control.