Influence of user-defined parameters using stochastic subspace identification (Ssi)

Abstract

Operational Modal Analysis (OMA) is one of the most used technique to study structures under environmental excitations, for the purpose of structural health monitoring, acceptance test and model updating. In OMA, the modal parameters are obtained only from the measured data using environmental vibrations as unknown input (e.g. wind load, micro-tremors, traffic) and without any artificial excitations applied on the structure. One of the advantages of OMA technique is the possibility to test large-scale structures, which are impossible to test by using artificial excitations, and to provide a modal model under operating conditions, meaning within true boundary conditions, actual forces and vibration levels. Other advantages of OMA are the velocity and cheapness to make the tests, and the possibility to detect close-spaced modal shapes. One of the most used methods in OMA is the Stochastic Subspace Identification (SSI). It relies on an elegant mathematical framework and robust linear algebra tools to identify the state-space matrix from raw data. As a result, non-linear optimization problems are avoided. Moreover, the use of well-known tools from numerical linear algebra, such as Singular Value Decomposition and LQ Decomposition, leads to a numerically very efficient implementation. In order to obtain accurate modal parameters estimations, some user-defined parameters need to be properly set. In this paper, Data-Driven Stochastic Subspace Identification (DD-SSI) method and its sensitivity to two user-defined parameters are investigated. These parameters are, namely, the number of block rows in Hankel matrix and the selection of the length of the data acquired and used in the identification process. In order to establish a standardization on the use of these parameters for reliable system parameters identification, a sensitivity analysis has been conducted on real scale building vibration data.