Damage characterization through wave propagation and scattering is of considerable interest to many non-destructive evaluation techniques. For fiber-reinforced composites, complex waves can be generated during the tests due to the non-homogeneous and anisotropic nature of the material when compared to isotropic materials. Additional complexities are introduced due to the presence of the damage and thus results in difficulty to characterize these defects. The inability to detect damage in composite structures limits their use in practice. A major task of structural health monitoring is to identify and characterize the existing defects or defect evolution through the interactions between structural features and multidisciplinary physical phenomena. In a wave-based approach to addressing this problem, the presence of damage is characterized by the changes in the signature of the resultant wave that propagates through the structure. In order to measure and characterize the wave propagation, we use the response of the surface-mounted piezoelectric transducers as input to an advanced machine-learning based classifier known as a Support Vector Machine.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Classification of Damage Signatures in Composite Plates using One-Class SVMs


    Contributors:


    Publication date :

    2007-03-01


    Size :

    933595 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Static Damage Surface Signatures for Laminated Composite Plates

    Mohite, Preetam / Upadhyay, Chandra | AIAA | 2008


    Learning Interpretable SVMs for Biological Sequence Classification

    Sonnenburg, S. / Ratsch, G. / Schafer, C. | British Library Conference Proceedings | 2005


    Learning of Facial Gestures Using SVMs

    Baltes, Jacky / Seo, Stela / Cheng, Chi Tai et al. | Springer Verlag | 2011


    Learning of Facial Gestures Using SVMs

    Baltes, J. / Seo, S. / Cheng, C.T. et al. | British Library Conference Proceedings | 2011


    Space Vision Marker System (SVMS)

    Bondy, Michel / Krishnasamy, Rubakumar / Crymble, Derry et al. | AIAA | 2007