DBSCAN-Based Approach for the Automatic Estimate of the Modal Parameters

The growing interest in the dynamic characterization of systems in operational conditions has highlighted the effectiveness of the Operational Modal Analysis methods. Some of these methods rely on stabilization diagrams for which the engineer’s judgment is fundamental since require the manual identification of the modal parameters, thus introducing additional uncertainties based on the engineers’ experience. To mitigate this, an algorithm based on the DBSCAN clustering method is proposed and applied to the Stochastic Subspace Identification (SSI). The proposed approach uses the DBSCAN algorithm and several pole selection criteria to find the most physical poles in the stabilization diagram with the consequent reduction of the estimation uncertainty. The proposed clustering method for automatic modal parameter estimate has been validated first by considering a 4 DOF mass-spring-damper system in terms of the accuracy and effectiveness of the automatic procedure concerning the closeness of the poles and the presence of noise. Then, the method is applied to estimate the modal parameters of an actual high-flexible wing wind tunnel tested at the Department of Mechanical and Aerospace Engineering of the University of Rome “La Sapienza”. The proposed automatic pole identification method allowed the tracking of the poles at varying wind velocities and angles of attack with a very high degree of accuracy compared to manual identification, revealing itself as a promising tool for the identification of the dynamic properties of operating systems and flight certification purposes.