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.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data


    Contributors:
    D. Gorinevsky (author) / B. L. Matthews (author) / R. Martin (author)

    Publication date :

    2012


    Size :

    7 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




    Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

    Gorinevsky, Dimitry / Matthews, Bryan L. / Martin, Rodney | NTRS | 2012


    AIRCRAFT OPERATIONAL ANOMALY DETECTION

    HAUKOM MICHAEL JAMES | European Patent Office | 2016

    Free access

    Queuing models for estimating aircraft fleet availability

    Sarma, V.V.S. / Ramchand, K. / Rao, A.K. | Tema Archive | 1977


    AIRCRAFT OPERATIONAL ANOMALY DETECTION

    HAUKOM MICHAEL JAMES | European Patent Office | 2016

    Free access

    AIRCRAFT OPERATIONAL ANOMALY DETECTION

    HAUKOM MICHAEL JAMES | European Patent Office | 2016

    Free access