Operational Modal Analysis on a 3D Scaled Model of a 3-Storey Aluminium Structure

  • Authors

    • M. Fadhil Shazmir
    • N. Ayuni Safari
    • M. Azhan Anuar
    • A. A.Mat Isa
    • Zamri A.R
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.27.22485
  • Operational Modal Analysis, Frequency Domain Decomposition, Dynamic characteristics, Stochastic Subspace Identification, Enhanced Frequency Domain Decomposition, 3D Aluminium Structure

  • Abstract

    Obtaining a good experimental modal data is essential in modal analysis in order to ensure accurate extraction of modal parameters. The parameters are compared with other extraction methods to ascertain its consistency and validity. This paper demonstrates the extraction of modal parameters using various identification algorithms in Operational Modal Analysis (OMA) on a 3D scaled model of a 3-storey aluminium structure. Algorithms such as Frequency Domain Decomposition (FDD), Enhanced Frequency Domain Decomposition (EFDD) and Stochastic Subspace Identification (SSI) are applied in this study to obtain modal parameters. The model test structure is fabricated of aluminium and assembled using bolts and nuts. Accelerometers were used to collect the responses and the commercial post processing software was used to obtain the modal parameters. The resulting natural frequencies and mode shapes using FDD method are then compared with other OMA parametric technique such as EFDD and SSI algorithm by comparing the natural frequencies and Modal Assurance Criterion (MAC). Comparison of these techniques will be shown to justify the validity of each technique used and hence confirming the accuracy of the measurement taken.  

     

     

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  • How to Cite

    Fadhil Shazmir, M., Ayuni Safari, N., Azhan Anuar, M., A.Mat Isa, A., & A.R, Z. (2018). Operational Modal Analysis on a 3D Scaled Model of a 3-Storey Aluminium Structure. International Journal of Engineering & Technology, 7(4.27), 78-82. https://doi.org/10.14419/ijet.v7i4.27.22485

    Received date: 2018-11-30

    Accepted date: 2018-11-30

    Published date: 2018-11-30