Aircraft anomaly detection using performance models trained on fleet data

@article{Gorinevsky2012AircraftAD,
  title={Aircraft anomaly detection using performance models trained on fleet data},
  author={Dimitry M. Gorinevsky and Bryan L. Matthews and Rodney Martin},
  journal={2012 Conference on Intelligent Data Understanding},
  year={2012},
  pages={17-23}
}
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… CONTINUE READING
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