A statistical pattern recognition technique based on time series analysis of vibration data is presented in this paper. A 20-m riser model experimentally validated is used for the numerical implementation of this technique. The dynamic response of the riser model is assessed using a semi-empirical approach with an increased mean drag coefficient model during lock-in events. Because structural damage is associated with fatigue damage, hinge connections are used to represent several damage scenarios. Then, the statistical pattern recognition technique is used to identify and locate structural damage using vibration data collected from strategically located sensors. Sensor locations are obtained from an optimum sensor placement method. The numerical results show that structural degradation due to fatigue in oscillating flexible risers can be assessed using the presented statistical pattern recognition technique.


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    Title :

    Damage detection in flexible risers using statistical pattern recognition techniques


    Additional title:

    Schadenserkennung in flexiblen Steigrohren unter Verwendung von Erkennungstechniken auf der Basis statistischer Muster


    Contributors:


    Publication date :

    2008


    Size :

    8 Seiten, 18 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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