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Erschienen in:
2017 | OriginalPaper | Buchkapitel
12. Single Mode Problem
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Abstract
This chapter develops the computational algorithm for determining the most probable value (MPV) and covariance matrix of modal parameters in Bayesian operational modal analysis with data in a single setup and for well-separated modes. The MPV of the mode shape is analytically derived in terms of other parameters, which effectively reduces the numerical optimization problem for MPV to involve only four parameters independent of the number of measured degrees of freedom. The asymptotic behavior of MPV for modes with high signal-to-noise ratio is analyzed. Analytical formulas are derived for systematically computing the covariance matrix of parameters given the measured data. Examples with synthetic data, laboratory data and field data are presented to illustrate the algorithms and their applications.