Structural health monitoring (SHM) systems will be one of the leading factors in the successful establishment of wind turbines in the energy arena. Detection of damage at an early stage is a vital issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of vibration analysis, extracted of a 9 m CX-100 blade under fatigue loading. For analysis, machine learning techniques utilised for failure detection of wind turbine blades will be applied, like non-linear Neural Networks, including Auto-Associative Neural Network (AANN) and Radial Basis Function (RBF) networks models.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Machine Learning Applications for a Wind Turbine Blade under Continuous Fatigue Loading


    Beteiligte:
    Dervilis, Nikolaos (Autor:in) / Choi, M. (Autor:in) / Antoniadou, Ifigeneia (Autor:in) / Farinholt, K.M. (Autor:in) / Taylor, S.G. (Autor:in) / Barthorpe, Rob J. (Autor:in) / Park, G. (Autor:in) / Farrar, Charles R. (Autor:in) / Worden, Keith (Autor:in)


    Erscheinungsdatum :

    2013


    Format / Umfang :

    9 Seiten




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





    Fatigue Behaviour of Fibreglass Wind Turbine Blade Material Under Variable Amplitude Loading

    Van Delft, D. R. V. / De Winkel, G. D. / Joosse, P. A. et al. | British Library Conference Proceedings | 1997


    Micromechanics Investigation of Wind Turbine Blade Fatigue Behavior Considering Multi-Axial Loading

    Crawford, C.A. / Mustafa, G. / McWilliam, M.K. et al. | British Library Conference Proceedings | 2013