Structural Health Monitoring by Means of Strain Field Pattern Recognition on the basis of PCA and Automatic Clustering Techniques Based on SOM

Julián Sierra-Pérez, Miguel A. Torres-Arredondo, Guénaël Cabanes, Alfredo Güeme, Luis E. Mujica

    Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

    8 Citas (Scopus)

    Resumen

    A new methodology to perform Structural Health Monitoring (SHM) in complex structures which is based on Data Driven Models (DDM) by means of strain measurements from Fiber Optic Sensors (FOS), in particular Fiber Bragg Gratings (FBGs), was developed by using Principal Component Analysis (PCA) and automatic clustering techniques based on Self-Organizing Maps (SOM) and density methods. The methodology includes techniques to uncoupling the changes in the strain field caused by the damage occurrence and the change in the operational conditions. Those techniques can be classified as Optimal Baseline Selection (OBS) techniques. Several experiments where performed to develop the methodology and demonstrate the whole concept. Some representative results are presented and discussed.

    Idioma originalInglés
    Páginas (desde-hasta)987-992
    Número de páginas6
    PublicaciónIFAC-PapersOnLine
    Volumen48
    N.º28
    DOI
    EstadoPublicada - 2015

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    © 2015

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