Extracting Information from Conventional AE Features for Fatigue Onset Damage Detection in Carbon Fiber Composites

Runar Unnthorsson, Niels Henrik Bohl Pontoppidan, Magnus Thor Jonsson

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    Abstract

    We have analyzed simple data fusion and preprocessing methods on Acoustic Emission measurements of prosthetic feet made of carbon fiber reinforced composites. This paper presents the initial research steps; aiming at reducing the time spent on the fatigue test. With a simple single feature probabilistic scheme we have showed that these methods can lead to increased classification performance. We conclude that: the derived features of the TTL count leads to increased classification under supervised conditions. The probabilistic classification scheme was founded on the histogram, however different approaches can readily be investigated using the improved features, possibly improving the performance using multiple feature classifiers, e.g., Voting systems; Support Vector Machines and Gaussian Mixtures.
    Original languageEnglish
    Title of host publication59th meeting of the Society for Machinery Failure Prevention Technology
    PublisherSociety for Machinery Failure Prevention Technology
    Publication date2005
    Publication statusPublished - 2005
    Event59th meeting of the Society for Machinery Failure Prevention Technology -
    Duration: 1 Jan 2005 → …

    Conference

    Conference59th meeting of the Society for Machinery Failure Prevention Technology
    Period01/01/2005 → …

    Keywords

    • Acoustic Emission
    • Data fusion
    • Carbon fibres

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