by Batista, F. C. and Maia, N. M. M.
Abstract:
In the present work, the authors propose a new methodology for the estimation of Frequency Response Functions (FRFs) that for some reason cannot be measured in a structure. With such an objective, one uses FRFs that are obtained through a finite element modelization of the structure. Due to instrumentation limitations, in the majority of the cases the FRFs that are associated to rotationaldegrees-of-freedom are often problematic. In the examples that are given in this paper those kind of FRFs are the ones to be estimated. Various examples are numerically simulated and the results obtained are discussed in order to assess the robustness of the method to the presence of noise in the data.
Reference:
(slides)
Bibtex Entry:
@INPROCEEDINGS {BatistaFC1207,
author = {Batista, F. C. and Maia, N. M. M.},
affiliation = {School of Technology and Management,
Polytechnic Institute of Leiria, Morro do Lena, 2401-951 Leiria, Portugal},
month = {jul},
title = {Estimation of Unmeasured Frequency Response Functions},
booktitle = {19th International Congress on Sound and Vibration},
keywords = {frf, unmeasured },
year = {2012},
url = {http://fernandobatista.net/download.php?f=78167d5acf05f8b2747cf6ecf9514613},
comment={<a target="_blank" href="http://fernandobatista.net/download.php?f=9e6a84617c066ad1b3fe1de599dac9aa">slides</a>},
abstract = {In the present work, the authors propose a new methodology for
the estimation of Frequency Response Functions (FRFs) that for some reason cannot be measured in a structure.
With such an objective, one uses FRFs that are obtained through a finite element modelization of the structure.
Due to instrumentation limitations, in the majority of the cases the FRFs that are associated to rotationaldegrees-of-freedom are often problematic.
In the examples that are given in this paper those kind of FRFs are the ones to be estimated.
Various examples are numerically simulated and the results obtained are discussed in order to assess the
robustness of the method to the presence of noise in the data.},
}