Test Design for Finite Element Model Updating - Identifiable Parameters and Informative Test Data
It is important to predict structural phenomena, such as noise and fatigue, stemming from vibrations. To do this, reliable structural dynamic models are needed. To be useful the models have to compare well with reality in the validation against test data; if not, the models should be modified. The thesis research is in the field of computational model updating, which is, more often than not, the updating of uncertain parameters of a finite element model to better correlate to test data. This is a specialization that started to grow in the 1970s, and since then much research has been done. The work presented here concerns the design of tests for model updating, which is one of several model updating sub-tasks.
For a test to be useful for model updating, the test data set must be such that the model parameters are sufficiently well identifiable. The dynamic properties of a structure to be compared with test data may under certain conditions change similarly when one parameter or a set of other parameters is changed. When this happens, there is lack of identifiability and, before a meaningful model updating can take place, either complementary test data have to be added or a re-parameterization of the model must be made. An index was developed, the Orthogonality-Co-linearity Index (OCI), that helps to find the best way to reduce the number of parameters when there is low identifiability. For the model updating, test data also need to be informative with respect to the parameters to be tuned. The data informativeness depends on the test design, i.e. the choice of stimuli and the placement of the actuators and sensors. A data informativeness index that supports the design of an informative test is proposed. Procedures were also worked out to make the test design robust with respect to parameter uncertainties. The study is limited to linear and time-invariant systems.