Fast multi-order computation of system matrices in subspace-based system identification

Subspace methods have proven to be efficient for the identification of linear time-invariant systems, especially applied to mechanical, civil or aeronautical structures in operation conditions. Therein, system identification results are needed at multiple (over-specified) model orders in order to distinguish the true structural modes from spurious modes using the so-called stabilization diagrams. In this paper, new efficient algorithms are derived for this multi-order system identification with subspace-based identification algorithms and the closely related Eigensystem Realization Algorithm. It is shown that the new algorithms are significantly faster than the conventional algorithms in use. They are demonstrated on the system identification of a large-scale civil structure.

► We investigate subspace methods for system identification in operation conditions. ► The system order is unknown and needs to be over-specified because of noise. ► New efficient algorithms are derived for system identification at multiple model orders. ► The computational complexity is reduced by one order of magnitude. ► We demonstrate the efficiency on the identification of a large-scale civil structure.