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A structural decomposition approach for detecting, locating, and quantifying nonlinearities in chain‐like systems
Author(s) -
HernandezGarcia Miguel R.,
Masri Sami F.,
Ghanem Roger,
Figueiredo Eloi,
Farrar Charles R.
Publication year - 2010
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.396
Subject(s) - nonlinear system , parametric statistics , reliability (semiconductor) , chain (unit) , decomposition , computer science , engineering , reliability engineering , data mining , mathematics , statistics , physics , ecology , power (physics) , quantum mechanics , astronomy , biology
Experimental data from a test‐bed structure tested at the Los Alamos National Laboratory are used to evaluate the effectiveness and reliability of a data‐driven non‐parametric technique to identify nonlinearities in uncertain MDOF chain‐like systems. The results of this study showed that the proposed approach was able, in a stochastic framework, to confidently detect the presence of nonlinearities, accurately locate the structural section where the nonlinear effects were observed, and provide an estimate of the severity of the nonlinearity. Copyright © 2010 John Wiley & Sons, Ltd.