Identification uncertainty of close modes in Operational modal analysis

Operational modal analysis has attracted a lot of attention in both theory development and field applications for its high economy in implementation. It allows the modal properties (natural frequencies, damping ratios, mode shapes, etc.) to be identified based on 'output' vibration data only. In the absence of information about the input loading, the uncertainty associated with the identified modal parameters is a significant concern. Among the challenging situations encountered in practice, close modes (i.e., modes with similar frequencies) are significantly more difficult to identify than wellseparated modes. The possible interaction of modes with similar frequencies complicates the identification model and in many cases reduces the identification precision, or even renders the situation unidentifiable. Using a Bayesian modal identification approach, this paper investigates the identification uncertainty of closely-spaced modes, which are identified using a multi-mode model with FFT (Fast Fourier Transform) data on the same frequency band. In this context, the identification uncertainty is investigated through the posterior covariance matrix, which can be computed for a given set of data. A series of numerical studies will be performed, where synthetic data in different specially designed situations are generated. Based on these data the modal properties in different situations are identified and their resulting posterior uncertainties are investigated. The effects of the proximity of close mode frequencies and mode shapes will be investigated. It is anticipated that this work will provide significant insights on the identification uncertainty in operational modal analysis for closelyspaced modes encountered in practice.