Modal Parameter Uncertainty Estimates as a Tool for Automated Operational Modal Analysis: Applications to a Smart Building
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The knowledge of modal parameter uncertainties derived from operational modal analysis (OMA) can greatly improve automated decisions by providing information about the quality of the modal identification. Yet so far, this information has been largely ignored in continuous monitoring studies on civil infrastructure, especially with respect to buildings. In this paper, we implement an automated version of Covariance Based Stochastic Subspace Identification on a highly instrumented smart building. An expansion of the technique estimates uncertainty bounds for all modal parameters. Through a series of full scale experiments, we demonstrate how uncertainties are valuable tools in various contexts of automation. These include the identification and removal of badly-fitted modes, the identification of periods of high signal-to-noise ratio, and the validation of reference sensors selection.
