Simulated fault data from a mathematical model of a damaged rotor system is used to develop a neural network based approach for rotor system damage detection. The mathematical model of the damaged rotor is a comprehensive rotorcraft aeroelastic analysis based on a finite element approach in space and time. Selected helicopter rotor faults are simulated through changes in inertial, damping and stiffness properties of the damaged blade. A feedforward neural network with backpropagation learning is trained using both 'ideal' and 'noisy' simulated data. Testing of the trained neural network shows that it can detect and identify damage in the rotor system from simulated blade response and vibratory hub loads data.


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    Title :

    Detection of helicopter rotor system simulated faults using neural networks


    Additional title:

    Erkennung von simulierten Fehlern eines Hubschrauberrotorsystems mit Hilfe neuronaler Netze


    Contributors:
    Ganguli, R. (author) / Chopra, I. (author) / Haas, D.J. (author)


    Publication date :

    1996


    Size :

    18 Seiten, 22 Bilder, 6 Tabellen, 20 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





    Detection of helicopter rotor system simulated faults using neural networks

    Ganguli, R. / Chopra, I. / Haas, D.J. | Tema Archive | 1997


    Detection of helicopter rotor system simulated faults using neural networks

    Ganguli, Ranjan / Chopra, Inderjit / Haas, David | AIAA | 1996


    Detection of Helicopter Rotor System Simulated Faults Using Neural Networks

    Ganguli, R. / Chopra, I. / Haas, D. et al. | British Library Conference Proceedings | 1996