Before a helicopter leaves the plant, it needs to be tuned so that its vibrations meet the required specifications helicopter track and balance is currently performed based on sensitivity coefficients' which have been statistically developed after years of production experience. The fundamental problem with using these sensitivity coefficients, however, is that they do not account for both he non-linear coupling between modifications and the non-linear effect of modifications on high amplitude vibrations. Therefore, in order to ensure the reliabiilty of the linear coefficients, only a limited number of modifications are simultaneously applied. This implies that a number of flights are performed before the aircraft is tuned, resulting in increased production and maintenance cost. In this project, we demonstrate the application of artificial neural nets in learning the nonlinear effect of modifications, so as to be able to select the appropriate set of modifications in fewer iterations (flights). The effectiveness of neural nets for track and balance is currently being tested at the Sikorsky production line.


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    Title :

    Helicopter track and balance with artifical neural nets


    Additional title:

    Abstimmung der Richtungsstabilität und der Balance von Hubschraubern mit Hilfe neuronaler Netze


    Contributors:
    Taitel, H.J. (author) / Danai, K. (author) / Gauthier, D. (author)


    Publication date :

    1992


    Size :

    8 Seiten, 11 Bilder, 6 Tabellen, 10 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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