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Accession Number N20130001693
Title Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data.
Publication Date Oct 2012
Media Count 7p
Personal Author B. L. Matthews D. Gorinevsky R. Martin
Abstract 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 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.
Keywords A-320 aircraft
Acceleration measurement
Ailerons
Aircraft performance
Algorithms
Anomalies
Bias
Commercial aircraft
Data mining
Detection
Failure analysis
Flight operations
Mathematical models
Regression analysis


 
Source Agency National Aeronautics and Space Administration
NTIS Subject Category 85D - Transportation Safety
85A - Air Transportation
Corporate Author National Aeronautics and Space Administration, Moffett Field, CA. Ames Research Center.
Document Type Conference proceedings
Title Note N/A
NTIS Issue Number 1318
Contract Number NNX12CA02C NNA08CG83C

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