The study aims to investigate the accuracy of a wavelet-based segmentation algorithm for fatigue life assessment. A SAE-owned fatigue strain random signal, called SAESUS was extracted using the Morlet family and produced non-damaging and damaging segments. Furthermore, the segments were clustered using Fuzzy C-Means (FCM) in order to observe the accuracy of the algorithm. The study found that there was a strong relationship between the Morlet-based signal energy and the fatigue damage. Damaging segments then were joined in order to gain new edited signal. The extraction process was able to shorten the original signal up to 39% and was able to retain at least 90% of the statistical parameters and the fatigue damage, indicating the Morlet wavelet can be successfully applied to compress the original signal without changing the main history as well.


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    Title :

    The Morlet wavelet-based features extraction for compressing automotive fatigue strain data


    Additional title:

    Die Morlet-Wavelet-basierte Erfassung von Merkmalen für kompressive Ermüdungsverformungsdaten im Automobilbau


    Contributors:
    Putra, T.E. (author) / Abdullah, S. (author) / Schramm, D. (author) / Nuawi, M.Z. (author) / Bruckmann, T. (author)


    Publication date :

    2013


    Size :

    6 Seiten, 4 Bilder, 1 Tabelle, 11 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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