Model updating processes are important for improving a model’s accuracy by considering experimental data. Structural system identification was achieved here by applying the degree-of-freedom-based reduction method and the inverse perturbation method. Experimental data were obtained using the specific sensor location selection method. Experimental vibration data were restored to a full finite element model using the reduction method to compare and update the numerical model. Applied iteratively, the improved reduced system method boosts model accuracy during full model restoration; however, iterative processes are time consuming. The computation efficiency was improved using the system equivalent reduction–expansion process in concert with proper orthogonal decomposition. A convolutional neural network was trained and applied to the updating process. We propose the use of an efficient model updating method using a convolutional neural network to reduce computation time. Experimental and numerical examples were adopted to examine the efficiency and accuracy of the model updating method using a convolutional neural network.


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    Titel :

    Efficient Model Updating Method for System Identification Using a Convolutional Neural Network


    Beteiligte:
    Sung, Heejun (Autor:in) / Chang, Seongmin (Autor:in) / Cho, Maenghyo (Autor:in)

    Erschienen in:

    AIAA Journal ; 59 , 9 ; 3480-3489


    Erscheinungsdatum :

    01.09.2021




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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