The presented study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to a SSP 34 m blade mounted on a test rig. Operational modal analysis (OMA) was conducted to obtain mode shapes for undamaged and damaged states of the blade. Subsequently, the mode shapes were analyzed with one-dimensional continuous wavelet transformations (CWTs) for damage identification. The basic idea of the method is that structural damage will introduce local mode shape irregularities which are captured in the CWT by significantly magnified transform coefficients, thus providing combined damage detection, localization, and size assessment. It was found that due to the nature of the proposed method, the value of the identification results highly depends on the number of employed measurement points. Since only a limited number of measurement points were utilized in the experiments, valid damage identification can only be obtained when employing high-frequency modes.

The present paper explores the application of a well-established vibration-based damage detection method to an operating Vestas V27 wind turbine blade. The blade is analyzed in a total of four states, namely, a healthy one plus three damaged ones in which trailing edge openings of increasing sizes are introduced. In each state, the blade is subjected to controlled actuator hits, yielding forced vibrations that are measured in a total of 12 accelerometers; of which 11 are used for damage detection. The dimensionality of these acceleration data is reduced by means of principal component analysis (PCA), and then a reduced set of selected principal scores are employed as damage features in the Mahalanobis metric in order to detect damageinduced anomalies.

No abstract

The aim of the present paper is to evaluate structural health monitoring (SHM) techniques based on modal analysis for crack detection in small wind turbine blades. A finite element (FE) model calibrated to measured modal parameters will be introduced to cracks with different sizes along one edge of the blade. Changes in modal parameters from the FE model are compared with data obtained from experimental tests. These comparisons will be used to validate the FE model and subsequently discuss the usability of SHM techniques based on modal parameters for condition monitoring of wind turbine blades.

The present paper provides a model updating application study concerning the jacket substructure of an offshore wind turbine. The updating is resolved in a sensitivity-based parameter estimation setting, where a cost function expressing the discrepancy between experimentally obtained modal parameters and model-predicted ones is minimized. The modal parameters of the physical system are estimated through stochastic subspace identification (SSI) applied to vibration data captured for idling and operational states of the turbine. From a theoretical outset, the identification approach relies on the system being linear and time-invariant (LTI) and the input white noise random processes; criteria which are violated in this application due to sources such as operational variability, the turbine controller, and non-linear damping. Consequently, particular attention is given to assess the feasibility of extracting modal parameters through SSI under the prevailing conditions and subsequently using these parameters for model updating. On this basis, it is deemed necessary to disregard the operational turbine states-which severely promote non-linear and time-variant structural behaviour and, as such, imprecise parameter estimation results-and conduct the model updating based on modal parameters extracted solely from the idling state. The uncertainties associated with the modal parameter estimates and the model parameters to be updated are outlined and included in the updating procedure using weighting matrices in the sensitivity-based formulation. By conducting the model updating based on in-situ data harvested from the jacket substructure during idling conditions, the maximum eigenfrequency deviation between the experimental estimates and the model-predicted ones is reduced from 30% to 1%.