Data merging for multi-setup operational modal analysis with data-driven SSI

Abstract

In Operational Modal Analysis (OMA) of large structures we often need to process sensor data from multiple non-simultaneously recorded measurement setups. These setups share some sensors in common, the so-called reference sensors that are fixed for all the measurements, while the other sensors are moved from one setup to the next. To obtain the modal parameters of the investigated structure it is necessary to process the data of all the measurement setups and normalize it as the unmeasured background excitation of each setup might be different. In this paper we present system identification results using a merging technique for data-driven Stochastic Subspace Identification (SSI), where the data is merged and normalized prior to the identification step. Like this, the different measurement setups can be processed in one step and do not have to be analyzed separately. We apply this new merging technique to measurement data of the Heritage Court Tower in Vancouver, Canada. ©2010 Society for Experimental Mechanics Inc.