Maximum likelihood identification of non-stationary operational data

Abstract

In-operation modal analysis has become a valid alternative for structures where a classic input–output test would be difficult if not impossible to conduct. Due to practical considerations, measurements are sometimes performed in patches (roving sensor setups) instead of covering the entire structure at once. In practice, one is often confronted with non-stationary ambient excitation sources (e.g., wind, traffic, waves, etc.). Since the scaling of operational mode shape estimates depends on the unknown level of the ambient excitation, an extra effort is required in order to correctly merge the different parts of the mode shapes. In this contribution, two different approaches, for merging operational mode shapes from non-stationary data, are proposed. Both methods are based upon a single maximum likelihood estimation procedure. For comparison and validation, both techniques were applied to non-stationary data sets obtained by scanning laser vibrometry as well as the Z24 bridge bench mark data.