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Direct model updating schemes modify the entries of system matrices to better match modal data. Thus far this has been done under the constraint imposed by symmetry and, in some cases, zeros from connectivity. Relations between coefficients of the stiffness matrix imposed by equilibrium, however, which are here designated as static constraints, have not been considered. The addition of static constraints to those from symmetry and connectivity completes the set of constraints and restricts the solution space of any direct update to that which is parametrically realizable from the model; eliminating, in this way, the long standing drawback of matrix modifications that have no physical interpretation. Static constraints lead to reductions in the amount of modal data needed to attain a unique solution and, as one anticipates, improve the accuracy of the update, in general. Direct model updating schemes modify the entries of system matrices to better match modal data. Thus far this has been done under the constraint imposed by symmetry and, in some cases, zeros from connectivity. Relations between coefficients of the stiffness matrix imposed by equilibrium, however, which are here designated as static constraints, have not been considered. The addition of static constraints to those from symmetry and connectivity completes the set of constraints and restricts the solution space of any direct update to that which is parametrically realizable from the model; eliminating, in this way, the long standing drawback of matrix modifications that have no physical interpretation. Static constraints lead to reductions in the amount of modal data needed to attain a unique solution and, as one anticipates, improve the accuracy of the update, in general.

This paper presents a case study demonstrating how to quantify thevalue of structural health monitoring (SHM), when used optimally inmaintenance planning for wind turbine blades. Maintenance costoptimization is performed using a risk-based approach based onBayesian decision analysis, in which probabilistic models are developedfor blade deterioration processes, blade inspections and SHM systems.The probabilistic SHM system model is based on data from an SHMcampaign with a 225 kW Vestas V27 wind turbine, where an artificialtrailing edge crack of increasing size was introduced. The statisticsderived from this model are applied to the case study concerningmaintenance of an 8 MW offshore wind turbine. It is found that thebenefit of the SHM highly depends on the reliability of the SHM systemand on how SHM observations are used when making decisions oninspections and maintenance. A sensitivity study confirms the generalityof the findings. The paper presents a case study demonstrating the methods used to estimate the value of the information (VoI) delivered by a structural health monitoring (SHM) system. The monitored object is a blade of an operating wind turbine. The case study shows how the maintenance cost optimization can be performed using a risk-based approach cast in a Bayesian decision analysis framework, in which probabilistic models are developed for blade deterioration processes, blade inspections, and SHM systems. The paper also provides a parameter study, which demonstrates how the potential benet of SHM highly depends on the reliability of the utilized SHM system and how the SHM observations are used for decision making on the inspections and maintenance.

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.

Today, structural integrity inspections of wind turbine blades are typically carried out by the use of rope or platform access. Since these inspection approaches are both tedious and extremely costly, a need for a method facilitating reliable, remote monitoring of the blades has been identified. In this article, it is examined whether a vibration-based damage localization approach proposed by the authors can provide such reliable monitoring of the location of a structural damage in a wind turbine blade. The blade, which is analyzed in idle condition, is subjected to unmeasured hits from a mounted actuator, yielding vibrations that are measured with a total of 12 accelerometers; of which 11 are used for damage localization. The employed damage localization method is an extended version of the stochastic dynamic damage location vector method, which, in its origin, is a model-based method that interrogates damage-induced changes in a surrogate of the transfer matrix. The surrogate’s quasi-null vector associated with the lowest singular value is converted into a pseudo-load vector and applied to a numerical model of the healthy structure in question, hereby, theoretically, yielding characteristic stress resultants approaching zero in the damaged elements. The proposed extension is based on outlier analysis of the characteristic stress resultants to discriminate between damaged elements and healthy ones; a procedure that previously, in the context of experiments with a small-scale blade, has proved to mitigate noise-induced anomalies and systematic, non-damage-associated adverse effects.

This 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 an SSP 34-m blade mounted on a test rig. Operational modal analysis 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 for damage identification. The basic idea of the method is that structural damage will introduce local mode shape irregularities which are captured in the continuous wavelet transformation 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 a limited number of measurement points were utilized in the experiments, only certain damage-sensitive modes, in which pronounced damage-induced mode shape changes occur, are applicable for valid identification of the damage.

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. 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 damage-induced anomalies.

The present paper documents a proposed modal and wavelet analysis-based structural health monitoring (SHM) method for damage identification in wind turbine blades. A finite element (FE) model of a full-scale wind turbine blade is developed and introduced to a transverse surface crack. Hereby, post-damage mode shapes are derived through modal analysis and subsequently analyzed with continuous two-dimensional wavelet transformation for damage identification, namely detection, localization and assessment. It is found that valid damage identification is obtained even when utilizing the mode shape of the first structural blade mode. However, due to the nature of the proposed method, it is also found that the accuracy of the damage assessment highly depends on the number of employed measurement points.

Laser-welded joints of stainless steel AISI304 are investigated experimentally to determine the transfor- mation of austenite to martensite during the welding process. This transformation, which occurs in the welded region due to heating and residual stresses, can influence the fatigue and fracture properties of the affected material. Therefore, the scope of the present study is to determine the quantity of introduced martensite in the welded region and hereby clarify the influence of laser welding on the fatigue and fracture properties of welded AISI304 joints. The quantification of martensite concentration is carried out by use of four different methods, namely Lichtenegger and Bloech (LB1) etching, Ferritescope, X-ray diffraction (XRD), and Vickers hardness. It is found that a concentration of 1-1.6 % martensite is introduced in the laser-welded area; a quantity that has insignificant influence on the fatigue properties of the joints.

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 stochastic dynamic damage location vector (SDDLV) method has previously proved to facilitate effective damage localization in truss- and plate-like structures. The method is based on interrogating damage-induced changes in transfer function matrices in cases where these matrices cannot be derived explicitly due to unknown input. Instead, vectors from the kernel of the transfer function matrix change are utilized; vectors which are derived on the basis of the system and state-to-output mapping matrices from output-only state-space realizations. The idea is then to convert the kernel vectors associated with the lowest singular values into static pseudo-loads and apply these alternately to an undamaged reference model with known stiffness matrix. By doing so, the stresses in the potentially damaged elements will, theoretically, approach zero. The present paper demonstrates an application of the SDDLV method for localization of structural damages in a cantilevered residential-sized wind turbine blade. The blade was excited by an unmeasured multi-impulse load and the resulting dynamic response was captured through accelerometers mounted along the blade. The static pseudo-loads were applied to a finite element (FE) blade model, which was tuned against the modal parameters of the actual blade. In the experiments, an undamaged blade configuration was analysed along with different damage scenarios, hereby testing the applicability of the SDDLV method.