Operational Modal Analysis Using a Fast Stochastic Subspace Identification Method

Abstract

AbstractStochastic subspace identification methods are an efficient tool for system identification of mechanical systems in Operational Modal Analysis, where modal parameters (natural frequencies, damping ratios, mode shapes) are estimated from measured ambient vibration data of a structure. System identification is usually done for many successive model orders, as the true system order is unknown. Then, identification results at different model orders are compared to distinguish true structural modes from spurious modes in so-called stabilization diagrams. These diagrams are a popular GUI-assisted way to select the identified system model, as the true structural modes tend to be stable for successive model orders, fulfilling certain stabilization criteria that are evaluated in an automated procedure. In Operational Modal Analysis of large structures the number modes of interest as well as the number of used sensors can be very large, thus leading to high model orders that have to be considered for system identification. This also means a big computational burden. Recently, an efficient approach to estimate system matrices at multiple model orders in Stochastic Subspace Identification was proposed. In this paper it is shown how this new “Fast SSI” improves the computation of the stabilization diagrams, leading to much faster system identification results for large systems. The Fast SSI is applied to the system identification of some relevant large scale industrial examples.KeywordsSystem identificationSubspace methodsLeast-squares problemsSystem orderStabilization diagram