Cointegration for Detecting Structural Blade Damage in an Operating Wind Turbine: An Experimental Study

Environmental and operational variabilities (EOVs) are known to pose an issue in structural health monitoring (SHM) systems, as these variabilities can mask the effect of structural damage. Numerous approaches to remove, or, at least, mitigate, the effect of EOVs in SHM applications have been proposed and tested through numerical simulations and in experimental studies. One of the approaches that has exhibited promising potential is cointegration, which, in this particular SHM context, is a technique for singling out and removing common signal trends stemming from the EOVs. In the present paper, the cointegration technique is employed to mitigate the effect of certain EOVs in an experimental, vibration-based damage detection analysis of a wind turbine blade under operating conditions. In the experimental campaign, the installed SHM system was recording blade accelerations and different environmental and operational conditions over a 3.5-month period. In the period, one of the blades was treated in its reference state and in damaged states with a trailing edge opening of increasing size. Based on the available data from these different structural states, it is demonstrated how cointegration can be used to successfully detect the introduced damages under conditions not allowing for direct discrimination between damage and EOVs.