Keywords: damping, power ratios, robustness, structural health monitoring.

In composite structures, cracking, delamination will cause changes in the measured dynamic response of the structure and so on modal parameters. Estimation of the damping in structural control often poses a difficult problem especially using broadband experiments. If these estimations are faulty, it is difficult to propose a robust structural health monitoring (SHM) algorithm. Recently we introduced the optimal power ratio damping estimator. A new theoretical basis of the bandwidth method for the damping estimation from frequency response functions (in case of a single degree of freedom system) has been proposed.

For the engineer one advantage of an average inverse power ratio (AIPR) is the simplicity: every one knows the classical bandwidth method (power ratio of 0.5). But here instead of determining the bandwidth for a given value of the power ratio which requires numerical interpolations, the method involves an average inverse power ratio calculated at two frequencies symmetrically located from a peak. In this way, the damping estimation from a frequency response function (FRF) becomes straightforward for simple modal test cases and would be sufficiently accurate provided that the coupling effects are not strong and the frequency resolution is high enough to determine accurately the peak amplitude frequencies.

This paper focuses on the estimation of the damping ratio often difficult to estimate accurately. We first analyze the robustness of the AIPR method versus the rational fraction polynomial (RFP) method using analytical (supervised) data. Our results demonstrate a different behaviour function of the damping ratio level. For low damping, low power ratio should be preferred (around 0.3), notably dealing with high noise.

The experimental modal tests aim at showing the effect on modal parameters (pole shift) of multisite localized impacts on composites laminates. We focus especially in two modes where the pole shift was not clear using POLYMAX. The simplicity of the AIPR method here becomes an advantage for rapid analysis. But the limitation is that a classification of "interesting FRFs" (based on the 33 experiments) should be made for a good AIPR estimation (sufficient peak amplitude). Finally the AIPR method correlates well with the POLYMAX method for noisy experimental data.

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