An eigensystem realization algorithm for modal parameter identification and model reduction

TLDR

The paper develops the Eigensystem Realization Algorithm (ERA) to identify modal parameters and reduce model order of dynamic systems from test data. ERA employs a singular‑value‑decomposition based extension of the Ho‑Kalman algorithm, transforms the resulting realization into modal space for parameter identification, and introduces two accuracy indicators to distinguish system modes from noise modes. Illustrations with simulation and experimental data on a rectangular grid structure demonstrate the effectiveness of the algorithm.

Abstract

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.