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Aircraft Anomaly Detection Using Performance Models Trained on Fleet DataThis 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 aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and 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, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.
Document ID
20130001693
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Gorinevsky, Dimitry (Mitek Analytics, LLC Palo Alto, CA, United States)
Matthews, Bryan L. (Stinger Ghaffarian Technologies, Inc. (SGT, Inc.) Moffett Field, CA, United States)
Martin, Rodney (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 27, 2013
Publication Date
October 23, 2012
Subject Category
Air Transportation And Safety
Report/Patent Number
ARC-E-DAA-TN5480
Meeting Information
Meeting: Conference on Intelligent Data Understanding 2012
Location: Boulder, CO
Country: United States
Start Date: October 24, 2012
End Date: October 26, 2012
Sponsors: National Center for Atmospheric Research
Funding Number(s)
WBS: WBS 534723.02.03.01
CONTRACT_GRANT: NNX12CA02C
CONTRACT_GRANT: NNA08CG83C
Distribution Limits
Public
Copyright
Public Use Permitted.

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