The majority of rotordynamic studies concerned with bearing properties identification estimate rotordynamic coefficients without addressing the issue of parameter uncertainties. Uncertainty quantification is required to establish the accuracy and therefore the robustness of the identified parameters. Accuracy on the identification methodology is hampered by measurement noise, experimental and modeling error, and numerical method. The aim of this article is to determine, by means of error analysis, the propagated uncertainty contributions in a parametric frequency-domain identification. The methodology is based on linearly independent excitations for a direct estimation of the bearing rotordynamic coefficients. Errors on measurable excitations and responses are considered in the identification strategy to evaluate uncertainties of the estimated parameters. General formulation using errors-in-variables noise model is presented for system identification, taking into account uncertainty propagation in bearing parameters estimation. Experimental measurements, obtained from a test rig, are employed to estimate rotordynamic coefficients of a three lobe air bearing and the associated uncertainties. Confidence intervals are suggested for the expected bearing coefficients. A Monte Carlo simulation is conducted to study the statistical behavior as a result of simulated stochastic uncertainty propagation for comparison purposes with the experimental evidence. Results are presented graphically to assess the influence of the uncertainty propagation on the bearing properties calculation.

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