Section: New Results

identification of linear systems

Modular identification and damage detection for large structures

Participants : Michael Döhler, Laurent Mevel.

In Operational Modal Analysis (OMA) of large structures it is often needed to process sensor data from multiple non-simultaneously recorded measurement setups, especially in the case of large structures.. In this work a new efficient variant of the PreGER algorithm is presented that avoids the numerical explosion of the calculation by using a modular approach, where the data from the measurement setups is processed setup by setup and not at the same time [16] .

Fast multi order subspace identification algorithm

Participants : Michael Döhler, Laurent Mevel.

Stochastic subspace identification methods are an efficient tool for system identification of mechanical systems in Operational Modal Analysis (OMA), where modal parameters are estimated from measured vibrational data of a structure. System identification is usually done for many successive model orders, as the true system order is unknown and identification in results at different model orders need to be compared to distinguish true structural modes from spurious modes in so-called stabilization diagrams. An algorithm to estimate the system matrices at multiple model orders has been derived [20] .

Evaluation of confidence intervals and computation of sensitivities for subspace methods

Participants : Michael Döhler, Xuan Lam, Laurent Mevel.

In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes) obtained from Stochastic Subspace Identification (SSI) of a structure, are afflicted with statistical uncertainty. A variant of this approach has been derived for the Eigenvalue-Realization-Algorithm (ERA) [25] . Another version has been proposed for the merging subspace algorithm [17] , [17] . This approach has been validated on large scale examples[14] .