The evaluation of fatigue damage accumulation on wind turbine support structures under operational conditions is heavily influenced by a number of uncertainties. These uncertainties may, firstly, be attributed to the highly variable and complex environmental loads, and secondly, to the unavoidable modelling errors which mainly originate from the inherent randomness in both material properties and fatigue resistance of structural components. It is therefore essential that assessment of fatigue life is carried out within a probabilistic framework; one that accounts for the stochastic nature of the phenomenon. The present study proposes a strategy for real-time reliability prediction of accumulated fatigue damage on wind turbine support structures by taking into account the above-mentioned uncertainties. To this end, the availability of structural monitoring information for the identification of the global response on wind-turbine support structures is exploited in order to address the discrepancies between actual and predicted damage accumulation. This is carried through utilization of an augmented version of the Kalman filter, which is capable of jointly estimating the response and the unknown inputs of the structure while relying on a limited number of noisy observations and a presumably uncertain model of the real system. A fixed-lag smoother is further deployed for the attenuation of the estimation error in an on-line mode and the smoothed stochastic estimates of the response are propagated over the model at the level of stresses. The accumulated damage along with the corresponding reliability level is finally predicted using a stochastic nonstationary fatigue damage model. The proposed scheme is demonstrated via implementation on the NREL 5.0 MW wind turbine under different operational conditions, on the basis of dummy vibration data generated via the FAST software. @en

Quasi-periodic loading resulting from waves and a rotationally sampled wind field often leads to fatiguedriven designs for offshore wind turbine support structures. The uncertainty on wind and wave loading, together with large modelling uncertainties, lead to large discrepancies between the observed and predicted dynamic behaviour of these structures. Among many recent-developed techniques for monitoring of true fatigue damage development, two promising Kalman-type filters are compared, namely the recently proposed Dual Kalman filter (DKF) and the Gillijns and De Moor filter (GDF). The filters are applied to synthetic vibration data in order to predict the global response of a lattice support structure assuming large modelling uncertainties and no knowledge of the input forces. A critical assessment of both filters with regard to requirements on the available data and tuning of the filter parameters is presented.@en

Slender structures with high stiffness-to-weight ratio form the main bearing element of modern engineering. This renders geometrical non-linear effects a key feature to be considered throughout the whole life-time of diverse structural components, such as Wind Turbine (WT) blades. Although the Finite Element (FE) method constitutes a well-established tool for the analysis of such systems, the resulting models are often prohibitively expensive in terms of computational resources and thus cannot be implemented in the design. The problem of state estimation for condition diagnostics and control applications is therefore rendered a challenging and intricate task when it comes to systems experiencing geometrical non-linearities. This is firstly due to the computationally demanding FE models associated with such systems and, secondly, to the requirement that estimation methods must consider non-linear phenomena. The problem is further pronounced in online applications, where real-time performance is required, as is commonly the case in structural health monitoring (SHM). Within this context, the focus is on computationally efficient models that operate on subspaces of significantly smaller size as compared to the full-order problem and which can be tailored to the framework of non-linear state estimation. This study proposes the implementation of physics-based reduced-order models (ROMs) for response prediction of systems featuring geometrically non-linear effects. In so doing, the concept of modal derivatives is adopted and combined with a flexible multibody approach in order to capture secondorder effects, e.g. twist-bend coupling, that arise as the system departs from the linear regime. In identifying the vibration response of such structures, the ROMs are compounded with the unscented Kalman filter (UKF) for the non-linear state estimation. The outlined approach is tested on the real-time response prediction of a WT blade, assuming that a limited number of artificial vibration measurements is available. The effectiveness of the scheme is assessed as a tool for online SHM and vibration control.@en

Slender structures with high stiffness-to-weight ratio form the main bearing element of modern engineering. This renders geometrical non-linear effects a key feature to be considered throughout the whole life-time of diverse structural components, such as Wind Turbine (WT) blades. Although the Finite Element (FE) method constitutes a well-established tool for the analysis of such systems, the resulting models are often prohibitively expensive in terms of computational resources and thus cannot be implemented in the design. The problem of state estimation for condition diagnostics and control applications is therefore rendered a challenging and intricate task when it comes to systems experiencing geometrical non-linearities. This is firstly due to the computationally demanding FE models associated with such systems and, secondly, to the requirement that estimation methods must consider non-linear phenomena. The problem is further pronounced in online applications, where real-time performance is required, as is commonly the case in structural health monitoring (SHM). Within this context, the focus is on computationally efficient models that operate on subspaces of significantly smaller size as compared to the full-order problem and which can be tailored to the framework of non-linear state estimation. This study proposes the implementation of physics-based reduced-order models (ROMs) for response prediction of systems featuring geometrically non-linear effects. In so doing, the concept of modal derivatives is adopted and combined with a flexible multibody approach in order to capture secondorder effects, e.g. twist-bend coupling, that arise as the system departs from the linear regime. In identifying the vibration response of such structures, the ROMs are compounded with the unscented Kalman filter (UKF) for the non-linear state estimation. The outlined approach is tested on the real-time response prediction of a WT blade, assuming that a limited number of artificial vibration measurements is available. The effectiveness of the scheme is assessed as a tool for online SHM and vibration control.@en