Caterpillar, Inc. and Frontier Technology, Inc. (FTI) are investigating prognostic health monitoring technologies for application to Caterpillar equipment. In particular, robust detection of mechanical damage in a wheel loader has been demonstrated via processing of high-speed, three-axis accelerometer data. Data collected with and without the damaged parts show distinctive signatures that are quantitatively separable. FTI's Pattern Recognition of Health (PRoH™) technology drives the signature generation and abnormality detection process through the use of data-driven techniques that estimate deviation from normal behavior.


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    Titel :

    Detection of mobile machine damage using accelerometer data and prognostic health monitoring techniques


    Beteiligte:
    Getman, Anya (Autor:in) / Cooper, Christopher D. (Autor:in) / Key, Gary (Autor:in) / Zhou, Heng (Autor:in) / Frankle, Nick (Autor:in)


    Erscheinungsdatum :

    2009-03-01


    Format / Umfang :

    524005 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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