Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension

Abstract

Cost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring (SHM) to monitor the foundation (support structure) condition is of utmost importance in offshore-fixed wind turbines. In this work a SHM strategy is presented to monitor online and during service a WT offshore jacket-type foundation. Standard SHM techniques, as guided waves with a known input excitation, cannot be used in a straightforward way in this particular application where unknown external perturbations as wind and waves are always present. To face this challenge, a vibration-response-only SHM strategy is proposed via machine learning methods. In this sense, the fractal dimension is proposed as a suitable feature to identify and classify different types of damage. The proposed proof-of-concept technique is validated in an experimental laboratory down-scaled jacket WT foundation undergoing different types of damage.

Detection of Jacket Offshore Wind Turbine Structural Damage using an 1D-Convolutional Neural Network with a Support Vector Machine Layer

“…On the left side of the figure, it can be seen that the model is overfitting since the validation loss is higher than the training loss. On the other hand, on the right side of the figure the confusion matrix is observed where it is evident that this model is not capable of Finally, to comprehensively verify the algorithm's functional properties, a comparison is done with four additional techniques that use the identical laboratory setup ( 19), ( 15), (35), and (16). The first method is based on principal component analysis and support vector machines, as described in (19).…”

Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

“…Vidal et al [ 7 ] used a combination of PCA and quadratic SVM for damage classification of the same structure and the same types of damage. The same benchmark structure was considered in [ 13 , 14 ], but with different types of damage. The overall performance of these approaches is excellent.…”

“…The CNN comprises seven convolutional layers performing feature extraction, followed by three fully connected layers and a SoftMax block for classification; an overall accuracy of is obtained. Hoxha et al [ 14 ] solved the identification and classification damage problem in an experimental laboratory wind-turbine offshore jacket-type foundation through a fractal dimension methodology that performs feature extraction in a machine learning (ML) setting. k NN, quadratic SVM, and Gaussian SVM were used as classifiers.…”

Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

“…The operation efficiency of wind turbines is affected by defects that exist on the surface of blades. Defect detection systems based on ML and DL methods have been utilised to inspect the regular operation of WTBs damage diagnosis [ 41 , 42 ] and condition monitoring [ 43 , 44 , 45 ]. Future work includes extending the proposed pipeline for the task of condition monitoring.…”