Data driven on-line assessment of structural health of aircraft fuselage panels is crucial both in military and civilian settings. This paper shows how Support Vector Machines (SVM) and Genetic Algorithm (GA) enable to analyze the strain values acquired through a monitoring sensor network and improve the diagnostic steps: 1) detecting a damage 2) identifying the specific component affected 3) characterizing the damage in terms of centre and size. The first two steps are performed through the SVM while the 3rd step is based on an Artificial Neural Network (ANN). Finally, the remaining useful life is estimated by using ANNs to predict the values of two parameters of the NASGRO equation which is used to estimate the damage propagation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Smart data driven maintenance: Improving damage detection and assessment on aerospace structures


    Beteiligte:


    Erscheinungsdatum :

    01.05.2014


    Format / Umfang :

    1064470 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Dynamic-Data-Driven Damage Prediction in Aerospace Composite Structures

    Korobenko, Artem / Pigazzini, Marco / Singh, Victor et al. | AIAA | 2016


    Dynamic-Data-Driven Damage Prediction in Aerospace Composite Structures (AIAA 2016-4126)

    Korobenko, Artem / Pigazzini, Marco / Singh, Victor et al. | British Library Conference Proceedings | 2016



    Smart Structures in Aerospace Technology

    Santini, P. | British Library Online Contents | 1996


    Composite Damage Assessment and Repair Evaluation Toolkit for Aerospace Structures

    Lua, Jim / Wei, Jason / Li, Xiaoming et al. | AIAA | 2010