The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe’s renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool.

Predicting the behavior of engineering structures with high accuracy remains a challenging task as a result of their continuous interaction with the immediate environment and varying operating conditions. In that context, forecasting tools are primarily focused on the creation of a model of a so-called baseline system. This established model serves as a foundation for identifying changes when new outputs deviate from the predictions made by the model. Physics-based numerical models, like the finite element method, often carry significant uncertainty stemming from assumptions regarding structural characteristics, environmental influences, and various loads affecting the system under study. Consequently, identifying the source of any existing discrepancies between obtained model results and measured data is difficult. This paper demonstrates a straightforward implementation of the polynomial chaos expansion method for the formulation of prognostic data-driven models targeted at tracking changes in continuously measured structural response. The method's effectiveness and positive features are showcased via practical application onto two full-scale engineering structures: a concrete arch dam and an industrial steel chimney. The models utilize environmental as well as response data collected over two years and two months of monitoring of these structures, respectively. The obtained results reveal the models' considerable potential as a long-term monitoring tool for autonomous assessment of structural behavior.