Bayesian operational modal analysis

<p>Bayesian operational modal analysis (BAYOMA) adopts a <a href="/facts/Bayesian_inference/tEpK3zLx">Bayesian</a> <a href="/facts/System_identification/v2xk1X0e">system identification</a> approach for <a href="/facts/Operational_modal_analysis/tSIcC4zN">operational modal analysis</a> (OMA). Operational modal analysis aims at identifying the modal properties (<a href="/facts/Natural_frequency/pqTgYguB">natural frequencies</a>, <a href="/facts/Damping_ratio/UW3RsjEd">damping ratios</a>, <a href="/facts/Mode_shape/XCsvQpuI">mode shapes</a>, etc.) of a constructed structure using only its (output) vibration response (e.g., velocity, acceleration) measured under operating conditions. The (input) excitations to the structure are not measured but are assumed to be '<a href="/facts/Ambient_vibrations/MJpA0WLq">ambient</a>' ('broadband random'). In a Bayesian context, the set of modal parameters are viewed as uncertain parameters or random variables whose probability distribution is updated from the prior distribution (before data) to the posterior distribution (after data). The peak(s) of the posterior distribution represents the most probable value(s) (MPV) suggested by the data, while the spread of the distribution around the MPV reflects the remaining uncertainty of the parameters.
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Bayesian operational modal analysis open-in-new

Bayesian operational modal analysis