Blind source separation based vibration mode identification

In this paper, a novel method for linear normal mode identification based on Blind Source Separation (BSS) is introduced. Modal coordinates are considered as a specific case of sources that have certain time structure. This structure can be identified by many BSS algorithms. However, algorithms based on second order statistics are particularly suited for the linear normal mode identification. Two well-known BSS algorithms are considered. First, Algorithm for Multiple Unknown Signals Extraction (AMUSE) is used to illustrate the similarity with Ibrahim Time Domain (ITD) modal identification method. Secondly, Second Order Blind Identification (SOBI) is used to demonstrate noise robustness of BSS-based mode shape extraction. Numerical results from these BSS algorithms and ITD method under different noise environments are provided. The Monte Carlo numerical simulations for the noisy cases are specifically used to show the robustness of BSS methods