An artificial neural network is trained using helicopter flight test data to predict rotor system component loads during high-speed maneuvering flight. Inputs to the network include control positions and aircraft state parameters. These parameters can be easily measured in the nonrotating system, i.e., the fuselage, and vary at a relatively low-frequency. A network design sensitivity study is conducted and several networks are developed for three loads; the rotor blade pushrod load, blade normal bending moment, and main-rotor damper load. Prediction accuracy is evaluated using a validation data set consisting of symmetric pull out maneuvers, rolling pull out maneuvers, and climbing turns not contained in the training data set. A traditional statistical approach, stepwise multiple linear regression, is also utilized and the two methods are compared and contrasted. Correlation coefficients from 84 % to 97 % are achievable using the neural network model for all three loads. Through a unified approach involving both neural network and statistical analysis greater accuracy and understanding of the neural network is attained.


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    Title :

    Prediction of helicopter component loads using neural networks


    Additional title:

    Die Voraussage über die Belastung von Hubschrauberkomponenten unter Benutzung neutraler Netzwerke


    Contributors:
    Haas, D.J. (author) / Milano, J. (author) / Flitter, L. (author)


    Publication date :

    1993


    Size :

    14 Seiten, 5 Bilder, 9 Tabellen, 9 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





    Prediction of helicopter component loads using neural networks

    Haas, D.J. / Milano, J. / Flitter, L. | Tema Archive | 1995


    PREDICTION OF HELICOPTER COMPONENT LOADS USING NEURAL NETWORKS

    HAAS, DAVID / MILANO, JOEL / FLITTER, LANCE | AIAA | 1993


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