Identification of modal parameters from inconsistent data

Most of the recent modal parameter estimators start from the fact that natural frequencies, damping ratios and modal participation factors are global parameters of the measured structure. Measuring the structure in different patches can however cause inconsistencies in the data due to e.g. the mass-loading effect of the accelerometers. When trying to fit a global model through these inconsistent data, errors can result by identifying multiple close-coupled poles. By means of simulated and experimental data, a strategy is proposed to solve this problem without increasing measurement time. First, each patch is processed individually. Next, the results are compared over the different patches and missing poles are re-estimated using adapted identification techniques. After compensating for the mass-loading effect, the obtained parameters are combined using clustering techniques and merged into one final global model. The proposed method can still be applied when the inconsistencies are not only due to mass-loading. The strategy has been tested on different modeling problems. Based on these results, an optimal measuring strategy has been developed to facilitate the automatic processing of the data. This optimal measuring strategy minimizes the occurrence of problems during the processing, and, consequently, optimizes the final quality of the estimated modal model.