Towards an Automatic Modal Parameter Estimation Framework: Mode Clustering

Paper in proceeding, 2015

The estimation of modal parameters from a set of measured data is a highly judgmental task, with user expertise playing a significant role for distinguishing between physical and spurious modes. However, it can be very tedious especially in situations when the data is difficult to analyze. This study presents a new algorithm for mode clustering as a preliminary step in a multi-step algorithm for performing physical mode selection with little or no user interaction. The algorithm commences by identification of a high-order model from estimated frequency response functions to collect all the important characteristics of the structure in a so-called library of modes. This often results in the presence of spurious modes which can be detected on the basis of the hypothesis that spurious modes are estimated with a higher level of uncertainty comparing to physical modes. Therefore, we construct a series of data using a simple random sampling technique in order to obtain a set of linear systems using subspace identification. Then, their similar modes are grouped together using a new correlation criterion, which is called Modal Observability Correlation (MOC). An illustrative example shows the efficiency of the proposed clustering technique and also demonstrates its capability to dealing with inconsistent data.