A neural network based low-airspeed sensor is developed using flight test data from an SH-60 helicopter specifically collected for this purpose. Inputs to the network are parameters that are typically monitored by a flight data recorder or a health and usage monitoring system. Approximately five percent of the data is utilized for network training. Prediction results indicate that accurate prediction of low airspeed magnitude and direction is possible with a root-mean-square error just under three knots. The neural network based low-airspeed sensor can be utilized in real time to improve situational awareness for pilots or implemented as a non-real-time algorithm for regime recognition in order to improve the assignment of fatigue damage for structural usage monitoring in the low-airspeed flight regime. The neural network based low-airspeed sensor provides a viable and affordable alternative to advanced mechanical low-airspeed sensor systems.


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    Title :

    Neural network based low-airspeed sensor


    Additional title:

    Messung niedriger Fluggeschwindigkeiten von Hubschraubern durch Auswertung von Sensordaten einem neuronalen Netz


    Contributors:
    McCool, K. (author) / Morales, M.A. (author) / Haas, D.J. (author)

    Published in:

    Publication date :

    2002


    Size :

    8 Seiten, 13 Bilder, 5 Tabellen, 18 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    Neural Network Based Low-Airspeed Sensor

    McCool, K. | Online Contents | 2002




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    RUDD, M. / DUBRO, G. / KIM, D. | AIAA | 1976