The structural health monitoring of offshore wind turbine towers is critical for ensuring their operational integrity. The condition of the tower structure is characterized by its modal parameters, which serve as effective health indicators. These parameters are identified through modal identification techniques. In this study, an automated modal identification method is developed for tower vibration data. The identification method is based on Stochastic Subspace Identification (SSI) and Fast Search Density Peaks Clustering (FSDPC), which combines the reconstructed Hankel matrices for spurious pole elimination with FSDPC clustering for automated modal identification. This method is conducted on a 5‐DOF numerical model for verification and robustness testing. A 4 MW wind turbine field structure is also introduced as the studying case. The automated modal identification method is further verified based on the offshore wind turbine tower vibration monitoring data. The identified frequencies measured from the monitoring data are analyzed and compared to those of the numerical method using eigenvalue analysis. The results show that the SSI‐FSDPC algorithm enables fully automated, robust modal identification, providing valuable support for tower structural health monitoring.
Li et al. (Thu,) studied this question.