A statistical strategy to select optimal structural health monitoring data in operational modal analysis

Abstract

Operational modal analysis (OMA) is prevalent in modal identification of civil structures. It asks for response measurements of the underlying structure under ambient loads. A valid OMA method requires the excitation be white noise in time and space. Although there are numerous applications of OMA in the literature, few have investigated the statistical distribution of a measurement and the influence of such randomness to modal identification. This research has attempted modified kurtosis to evaluate the statistical distribution of raw measurement data. In addition, a windowing strategy employing this index has been proposed to select quality datasets. In order to demonstrate how the data selection strategy works, the ambient vibration measurements of a laboratory bridge model and a real cable-stayed bridge have been respectively considered. The analysis incorporated with frequency domain decomposition (FDD) as the target OMA approach for modal identification. The modal identification results using the data segments with different randomness have been compared. The discrepancy in FDD spectra of the results indicates that, in order to fulfil the assumption of an OMA method, special care shall be taken in processing a long vibration measurement data. The proposed data selection strategy is easy-to-apply and verified effective in modal analysis.