Stavroula Tsiapoki, Moritz W. Häckell, Tanja Grießmann, Raimund Rolfes
|Titel:||Damage and ice detection on wind turbine rotor blades using a structural health monitoring framework|
|Stichworte:||Wind turbines, rotor blades, damage detection, ice detection, vibration based, machine learning|
|Kategorie:||Artikel in Fachzeitschriften|
The increasing number of installed wind turbines has led to a greater need for monitoring of their subcomponents. In particular, damages on rotor blades should be detected as early as possible, since they can cause long and hence expensive standstill times. In this work, a three-tier structural health monitoring framework is employed on the experimental data of a 34-m rotor blade for damage and ice detection. The structural health monitoring framework includes the functions of data normalization by clustering according to environmental and operational conditions, feature extraction, and hypothesis testing. In order to assess the framework and the methods applied with respect to ice detection, an ice accretion test was performed by gradually adding masses at the blade tip. First, a modal test by means of manual and impulse excitation was performed on the healthy blade and for all steps of the ice test. Subsequently, to induce damage, the blade was cyclically excited in edgewise direction for over 1 million cycles until failure occurred at the trailing edge. Finally, the initial modal test was repeated on the damaged blade. Modal parameters from system identification and further damage features, also called condition parameters, are presented and compared to each other. Results from the modal test show that structural changes due to damage at the trailing edge and added mass can be detected by changes in the condition parameters. Nevertheless, it is shown that some condition parameters exhibit higher sensitivity to damage than natural frequencies. Furthermore, a correlation between the amount of added mass and the changes in natural frequencies and some of the condition parameters is shown. For the analysis of the fatigue test, condition parameters were determined with and without prior data clustering according to the applied damage equivalent load, resulting in two realizations of the structural health monitoring framework. Results from the fatigue test show that the majority of condition parameters have good detection performance regarding structural change due to fatigue cracks and due to damage at the trailing edge for various confidence intervals. Finally, it is shown that the detection performance in the case of data clustering according to applied damage equivalent load is higher than without data clustering. This emphasizes the need of data normalization by clustering according to the environmental and operational conditions.