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Time and frequency domain regression‐based stiffness estimation and damage identification
Modal analysis and system identification are popular methods for obtaining insight into structural properties from vibration signals. The associated algorithms, however, often require responses from a large group of representative DOFs of the structure to produce accurate and consistent results. In addition, their direct output, which is generally a set of estimated modal properties, does not explicitly reflect possible structural damage locations and extent. In this paper, two novel regression‐based techniques that use local acceleration responses of a frame structure to estimate its local stiffness are proposed. One employs displacement simulation methods; the other is based on spectral estimates. Both methods are proven effective in their application to data collected from two laboratory specimens that are subjected to white noise excitation. Copyright © 2013 John Wiley & Sons, Ltd.
Time and frequency domain regression‐based stiffness estimation and damage identification
Modal analysis and system identification are popular methods for obtaining insight into structural properties from vibration signals. The associated algorithms, however, often require responses from a large group of representative DOFs of the structure to produce accurate and consistent results. In addition, their direct output, which is generally a set of estimated modal properties, does not explicitly reflect possible structural damage locations and extent. In this paper, two novel regression‐based techniques that use local acceleration responses of a frame structure to estimate its local stiffness are proposed. One employs displacement simulation methods; the other is based on spectral estimates. Both methods are proven effective in their application to data collected from two laboratory specimens that are subjected to white noise excitation. Copyright © 2013 John Wiley & Sons, Ltd.
Time and frequency domain regression‐based stiffness estimation and damage identification
Yao, Ruigen (Autor:in) / Pakzad, Shamim N. (Autor:in)
Structural Control and Health Monitoring ; 21 ; 356-380
01.03.2014
25 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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