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Variance estimation of modal parameters from input/output covariance-driven subspace identification

Philippe Mellinger 1 Michael Döhler 2 Laurent Mevel 2
2 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
Abstract : For Operational Modal Analysis (OMA), the vibration response of a structure from ambient and unknown ex-citation is measured and used to estimate the modal parameters. For OMA with eXogenous inputs (OMAX), some of the inputs are known in addition, which are considered as realizations of a stochastic process. When identifying the modal parameters from noisy measurement data, the information on their uncertainty is most relevant. Previously, a method for variance estimation has been developed for the output-only case with covariance-driven subspace identification. In this paper, a recent extension of this method for the in-put/output covariance-driven subspace algorithm is discussed. The resulting variance expressions are easy to evaluate and computationally tractable when using an efficient implementation. Based on Monte Carlo simulations, the quality of identification and the accuracy of variance estimation are evaluated. It is shown how the input information leads to better identification results and lower uncertainties.
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Submitted on : Friday, September 16, 2016 - 3:28:37 PM
Last modification on : Tuesday, December 8, 2020 - 10:20:25 AM
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Philippe Mellinger, Michael Döhler, Laurent Mevel. Variance estimation of modal parameters from input/output covariance-driven subspace identification. ISMA - 27th Conference on Noise and Vibration Engineering, Sep 2016, Leuven, Belgium. ⟨hal-01367695⟩

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