This paper presents an intelligent computational methodology for loose-bolt detection in thermal protection panels, considering uncertainties in sensed data. The proposed methodology is based on the integration of a dynamic artificial neural network, wavelet signal analysis, and Bayesian probabilistic assessment. A dynamic fuzzy wavelet neural-network model is employed to perform the multiple-input/multiple-output nonparametric system identification of the panel using time-series data obtained from the panel under a healthy condition. The trained model is used to predict dynamic responses of the structural system under unknown conditions. Both predicted and sensed-time-history data are decomposed into multiple time-frequency resolutions using a discrete wavelet-packet transform method. The wavelet-packet component energy is computed in terms of the decomposed coefficients and used as a signal feature to detect loose bolts. The effectiveness of the selected features is assessed using both crosscorrelation and cross-coherence metrics. The multivariate comparison in damage detection is handled by an interval-based Bayesian hypothesis-testing approach. The methodology is implemented to detect one loose bolt of a prototype thermal protection system panel with four mechanically bolted joints using experimental data collected at the U.S. Air Force Research Laboratory from seven different sensor configurations.


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    Title :

    Bayesian wavelet methodology for damage detection of thermal protection system panels


    Additional title:

    Bayesische Wavelet-Methode zum Schädigungsnachweis der Wärmeschutzschild-Paneele


    Contributors:

    Published in:

    AIAA Journal ; 47 , 4 ; 942-952


    Publication date :

    2009


    Size :

    11 Seiten, 9 Bilder, 3 Tabellen, 66 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English