AN INVESTIGATION INTO THE EFFECTS OF FREQUENCY RESPONSE FUNCTION ESTIMATORS ON MODEL UPDATING

Abstract Model updating is a very active research field, in which significant effort has been invested in recent years. Model updating methodologies are invariably successful when used on noise-free simulated data, but tend to be unpredictable when presented with real experimental data that are—unavoidably—corrupted with uncorrelated noise content. In the development and validation of model-updating strategies, a random zero-mean Gaussian variable is added to simulated test data to tax the updating routines more fully. This paper proposes a more sophisticated model for experimental measurement noise, and this is used in conjunction with several different frequency response function estimators, from the classicalH1andH2to more refined estimators that purport to be unbiased. Finite-element model case studies, in conjunction with a genuine experimental test, suggest that the proposed noise model is a more realistic representation of experimental noise phenomena. The choice of estimator is shown to have a significant influence on the viability of the FRF sensitivity method. These test cases find that the use of theH2estimator for model updating purposes is contraindicated, and that there is no advantage to be gained by using the sophisticated estimators over the classicalH1estimator.