High Dimensional Data Reduction in Modal Analysis with Stochastic Subspace Identification

Abstract

Subspace system identification methods are widely used in output-only vibration analysis of civil structures, known as operational modal analysis. With the advent of new sensor technologies, such as video camera-based full field displacement or velocity measurements, the number of measured outputs is quickly increasing. In this paper, we propose principal component analysis-based data size reduction methods for efficient application of subspace methods, while preserving the high spatial resolution of the identified mode shapes for detailed modal analysis.