Modal Identification Using OMA Techniques: Nonlinearity Effect
This paper is focused on an assessment of the state of the art of operational modal analysis (OMA) methodologies in estimating modal parameters from output responses of nonlinear structures. By means of the Volterra series, the nonlinear structure excited by random excitation is modeled as best line...
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Online Access: | http://dx.doi.org/10.1155/2015/178696 |
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doaj-f24a6e7f9d5644e886ae0f94983b40b62020-11-24T23:14:27ZengHindawi LimitedShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/178696178696Modal Identification Using OMA Techniques: Nonlinearity EffectE. Zhang0R. Pintelon1P. Guillaume2School of Mechanical Engineering, Zhengzhou University, Science Road 100, Zhengzhou 450000, ChinaDepartment of Fundamental Electricity and Instrumentation, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, BelgiumDepartment of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, BelgiumThis paper is focused on an assessment of the state of the art of operational modal analysis (OMA) methodologies in estimating modal parameters from output responses of nonlinear structures. By means of the Volterra series, the nonlinear structure excited by random excitation is modeled as best linear approximation plus a term representing nonlinear distortions. As the nonlinear distortions are of stochastic nature and thus indistinguishable from the measurement noise, a protocol based on the use of the random phase multisine is proposed to reveal the accuracy and robustness of the linear OMA technique in the presence of the system nonlinearity. Several frequency- and time-domain based OMA techniques are examined for the modal identification of simulated and real nonlinear mechanical systems. Theoretical analyses are also provided to understand how the system nonlinearity degrades the performance of the OMA algorithms.http://dx.doi.org/10.1155/2015/178696 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
E. Zhang R. Pintelon P. Guillaume |
spellingShingle |
E. Zhang R. Pintelon P. Guillaume Modal Identification Using OMA Techniques: Nonlinearity Effect Shock and Vibration |
author_facet |
E. Zhang R. Pintelon P. Guillaume |
author_sort |
E. Zhang |
title |
Modal Identification Using OMA Techniques: Nonlinearity Effect |
title_short |
Modal Identification Using OMA Techniques: Nonlinearity Effect |
title_full |
Modal Identification Using OMA Techniques: Nonlinearity Effect |
title_fullStr |
Modal Identification Using OMA Techniques: Nonlinearity Effect |
title_full_unstemmed |
Modal Identification Using OMA Techniques: Nonlinearity Effect |
title_sort |
modal identification using oma techniques: nonlinearity effect |
publisher |
Hindawi Limited |
series |
Shock and Vibration |
issn |
1070-9622 1875-9203 |
publishDate |
2015-01-01 |
description |
This paper is focused on an assessment of the state of the art of operational modal analysis (OMA) methodologies in estimating modal parameters from output responses of nonlinear structures. By means of the Volterra series, the nonlinear structure excited by random excitation is modeled as best linear approximation plus a term representing nonlinear distortions. As the nonlinear distortions are of stochastic nature and thus indistinguishable from the measurement noise, a protocol based on the use of the random phase multisine is proposed to reveal the accuracy and robustness of the linear OMA technique in the presence of the system nonlinearity. Several frequency- and time-domain based OMA techniques are examined for the modal identification of simulated and real nonlinear mechanical systems. Theoretical analyses are also provided to understand how the system nonlinearity degrades the performance of the OMA algorithms. |
url |
http://dx.doi.org/10.1155/2015/178696 |
work_keys_str_mv |
AT ezhang modalidentificationusingomatechniquesnonlinearityeffect AT rpintelon modalidentificationusingomatechniquesnonlinearityeffect AT pguillaume modalidentificationusingomatechniquesnonlinearityeffect |
_version_ |
1725594377997254656 |