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**Harvard**

Khorsand Vakilzadeh, M., Yaghoubi, V., Johansson, A. och Abrahamsson, T. (2015) *Towards an Automatic Modal Parameter Estimation Framework: Mode Clustering*.

** BibTeX **

@conference{

Khorsand Vakilzadeh2015,

author={Khorsand Vakilzadeh, Majid and Yaghoubi, Vahid and Johansson, Anders T and Abrahamsson, Thomas},

title={Towards an Automatic Modal Parameter Estimation Framework: Mode Clustering},

booktitle={Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, 2015; (Topics in Modal Analysis, Volume 10)},

isbn={978-3-319-15250-9},

pages={243-259},

abstract={The estimation of modal parameters from a set of measured data is a highly judgmental task, with user expertise playing a significant role for distinguishing between physical and spurious modes. However, it can be very tedious especially in situations when the data is difficult to analyze. This study presents a new algorithm for mode clustering as a preliminary step in a multi-step algorithm for performing physical mode selection with little or no user interaction. The algorithm commences by identification of a high-order model from estimated frequency response functions to collect all the important characteristics of the structure in a so-called library of modes. This often results in the presence of spurious modes which can be detected on the basis of the hypothesis that spurious modes are estimated with a higher level of uncertainty comparing to physical modes. Therefore, we construct a series of data using a simple random sampling technique in order to obtain a set of linear systems using subspace identification. Then, their similar modes are grouped together using a new correlation criterion, which is called Modal Observability Correlation (MOC). An illustrative example shows the efficiency of the proposed clustering technique and also demonstrates its capability to dealing with inconsistent data.},

year={2015},

keywords={Clustering, FRF based N4SID, Inconsistent datam, Modal observability correlation, Modal parameters, QR- and singular value decomposition},

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 226125

A1 Khorsand Vakilzadeh, Majid

A1 Yaghoubi, Vahid

A1 Johansson, Anders T

A1 Abrahamsson, Thomas

T1 Towards an Automatic Modal Parameter Estimation Framework: Mode Clustering

YR 2015

T2 Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, 2015; (Topics in Modal Analysis, Volume 10)

SN 978-3-319-15250-9

SP 243

OP 259

AB The estimation of modal parameters from a set of measured data is a highly judgmental task, with user expertise playing a significant role for distinguishing between physical and spurious modes. However, it can be very tedious especially in situations when the data is difficult to analyze. This study presents a new algorithm for mode clustering as a preliminary step in a multi-step algorithm for performing physical mode selection with little or no user interaction. The algorithm commences by identification of a high-order model from estimated frequency response functions to collect all the important characteristics of the structure in a so-called library of modes. This often results in the presence of spurious modes which can be detected on the basis of the hypothesis that spurious modes are estimated with a higher level of uncertainty comparing to physical modes. Therefore, we construct a series of data using a simple random sampling technique in order to obtain a set of linear systems using subspace identification. Then, their similar modes are grouped together using a new correlation criterion, which is called Modal Observability Correlation (MOC). An illustrative example shows the efficiency of the proposed clustering technique and also demonstrates its capability to dealing with inconsistent data.

LA eng

DO 10.1007/978-3-319-15251-6_23

LK http://dx.doi.org/10.1007/978-3-319-15251-6_23

OL 30