Efficient Multi-Order Uncertainty Computation for Stochastic Subspace Identification
DOHLER ; MEVEL
Type de document
ARTICLE A COMITE DE LECTURE REPERTORIE DANS BDI (ACL)
Langue
anglais
Auteur
DOHLER ; MEVEL
Résumé / Abstract
In operational modal analysis, the modal parameters (natural frequencies, damping ratios and mode shapes), obtained with stochastic subspace identification from ambient vibration measurements of structures, are subject to statistical uncertainty. It is hence necessary to evaluate the uncertainty bounds of the obtained results, which can be done by a first-order perturbation analysis. To obtain vibration measurements at many coordinates of a structure with only a few sensors, it is common practice to use multiple sensor setups for the measurements. Recently, a multi-setup subspace identification algorithm has been proposed that merges the data from di_erent setups prior to the identification step to obtain one set of global modal parameters, taking the possibly di_erent ambient excitation characteristics between the measurements into account. In this paper, an algorithm is proposed that e_ciently estimates the covariances on modal parameters obtained from this multi-setup subspace identification. The new algorithm is validated on multi-setup ambient vibration data of the Z24 Bridge, benchmark of the COST F3 European network. Stochastic Subspace Identification methods have been extensively used for the modal analysis of mechanical, civil or aeronautical structures for the last ten years. So-called stabilization diagrams are used, where modal parameters are estimated at successive model orders, leading to a graphical procedure where the physical modes of the system are extracted and separated from spurious modes. Recently an uncertainty computation scheme has been derived allowing the computation of uncertainty bounds for modal parameters at some given model order. In this paper, two problems are addressed. Firstly, a fast computation scheme is proposed reducing the computational burden of the uncertainty computation scheme by an order of magnitude in the model order compared to a direct implementation. Secondly, a new algorithm is proposed to derive e_ciently the uncertainty bounds for the estimated modes at all model orders in the stabilization diagram. It is shown that this new algorithm is both computationally and memory e_cient, reducing the computational burden by two orders of magnitude in the model order.
Source
Mechanical Systems and Signal Processing, num. 2, pp346-366 p.
Editeur
Elsevier
