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Analysis of
nonstationary random processes using smooth decomposition
Rubens Sampaio and Sergio Bellizzi |
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Vol. 6 (2011), No. 7-8, 1137–1152
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Abstract |
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Orthogonal decompositions provide a powerful tool for stochastic dynamics analysis. The most popular decomposition is the Karhunen–Loève decomposition (KLD), also called proper orthogonal decomposition. KLD is based on the eigenvectors of the correlation matrix of the random field. Recently, a modified KLD called smooth Karhunen–Loève decomposition (SD) has appeared in the literature. It is based on a generalized eigenproblem defined from the covariance matrix of the random process and the covariance matrix of the associated time-derivative random process. SD appears to be an interesting tool to extend modal analysis. Although it does not satisfy the optimality relation of KLD, and maybe is not as good a candidate for building reduced models as KLD is, SD gives access to the modal vectors independently of the mass distribution. In this paper, the main properties of SD for nonstationary random processes are explored. A discrete nonlinear system is studied through its linearization, for uncorrelated and correlated excitation, and the SD of the nonlinear system and of the linearization are compared. It seems that SD detects not only mass inhomogeneities but also nonlinearities. |
Keywords
smooth decomposition, output only modal analysis, linear
and nonlinear systems
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Milestones
Received: 20 May 2010
Revised: 27 September 2010
Accepted: 14 November 2010
Published: 21 December 2011
Proposed: Adair R. Aguiar
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Authors |
| Rubens Sampaio | |
| Departamento de Engenharia
Mecânica PUC-Rio Rua Marquês de São Vicente, 225 22453-900 Rio de Janeiro-RJ Brazil |
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| Sergio Bellizzi | |
| Laboratoire de Mécanique et
d’Acoustique - CNRS 31 chemin Joseph Aiguier 13402 Marseille France |
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