Adaptive modal identification of structures with equivariant adaptive separation via independence approach

Abstract

An efficient output-only Blind Source Separation (BSS) method was recently introduced for the modal identification of structures. BSS procedures recover a set of independent sources from their unknown linear mixtures when only mixtures are observed. Batch data is required for the separation in traditional blind source separation methods. These algorithms are however unfavorable, as some sets of data are observed one after another. In this paper, an adaptive blind source separation technique - equivariant adaptive separation via independence (EASI) - is introduced to overcome the mentioned disadvantage within the structures. The EASI algorithm is beneficial as it can provide solutions to real time problems, while also update the un-mixing matrix for each step. EASI not only avoids increases in size of the relevant matrices and vectors, but also decreases the analysis time. A synthetic example and a benchmark structure have been used in this paper to better investigate the efficiency of the proposed method. The simulation results demonstrate the effectiveness of the EASI algorithm in on-line identification of modal parameters of structures.