Two fundamental questions that arise in finite ele- ment model updating and error localization prob- lems are addressed. These are whether available test data are informative enough with respect to the quantification of possible model errors and whether sufficient identifiability of such errors is at hand for a given test data set. We advocate the use of informativeness and identifiability based indices in a preparatory process to increase the likelihood of a successful error localization. Based on model properties, such informativeness and identifiability indices may be used in the pre-test planning for the determination of frequency, time and spatial resolution to be used in a vibratory test.
First, the test data informativeness with respect to model parameters which might be in error is quan- tified. Here a dual assumption is made such that if model parameter perturbations could be detected by data from the planned test, then the test data could be used to detect such perturbations, i.e. the test is informative. A Data Information Richness (DIR) index has been developed to assess the level od Data Informativeness with respect to model parameters. Secondly, the identifiability of the model parame- ters are studied. The dynamic properties of a struc- ture, as recorded by a measurement system, may under certain conditions change similarly by changing one parameter or a set of other parame
ters. Should that be the case, there is no identifi- ability and before a meaningful error localization may take place, either complementary test data have to be added or a re-parameterization of the model has to be made. To assess the identifiability, identifiability based criteria are further developed, based on earlier work by the authors. A newly developed orthogonality/co-linearity index ocI assist in the re-parameterization of systems with low identifiability.
The methods of preparatory error localisation are applied to a six-degree-of-freedom system in a numerical example in which the analytical results of a finite element analysis are taken as substitute for measured data.
San Antonio, TX, 2000. 1520-1527 p.