This paper is concerned with the experimental validation of a structural health monitoring methodology, previously only investigated using synthetic data. The structure considered here is a simplified model of a metallic aircraft wingbox i.e., a plate incorporating stiffening elements. Damage is simulated by a saw-cut to one of the panel stringers (stiffeners). The analysis approach uses novelty detection based on measured transmissibilities from the structure. Three different novelty detection algorithms are considered here: outlier analysis, density estimation and an auto-associative neural network technique. All three methods ire shown to be successful to an extent, although a critical comparison indicates reservations about the density estimation approach when used on sparse data sets. (to be continued)


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    Title :

    Experimental validation of a structural health monitoring methodology: Part 1. Novelty detection on a laboratory structure


    Additional title:

    Experimentelle Überprüfung einer Methode zur Strukturstabilitäts-Überwachung: Teil 1. Neuartige Detektionsmethode, angewandt auf eine Laborstruktur


    Contributors:
    Worden, K. (author) / Manson, G. (author) / Allman, D. (author)

    Published in:

    Publication date :

    2003


    Size :

    21 Seiten, 19 Bilder, 1 Tabelle, 19 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English