Abstract. Wind turbines are complex electromechanical systems that require continuous monitoring to ensure operational efficiency, reduce maintenance costs, and prevent critical failures. Machine learning has shown great promise in structural health monitoring (SHM) by enabling automated fault detection through data-driven approaches. However, challenges remain in adapting SHM methods to complex environmental conditions while maintaining reliable fault detection and classification. This work proposes a hybrid model that combines supervised and unsupervised learning techniques for classifying operational failures in wind turbines. The proposed framework integrates multimodal data, combining structural and environmental information to monitor four distinct operational states. The approach begins with analysing sensor signals and extracting descriptive features that capture the turbine's dynamic behaviour, accounting for the effects of temperature and wind speed. The unsupervised k-means is applied to label and cluster the dataset, while feature and sensor selection are performed using canonical correlation analysis to rank the most informative variables. A novel relative change damage index is introduced to normalize and scale features based on their relative variability, enhancing the accuracy of clustering and fault classification. Classification is performed using six machine learning algorithms, and the best model is identified. Experimental results, also considering environmental conditions and sensor failures, demonstrate strong performance across both binary and multi-class tasks, including the detection of pitch drive faults and the accurate identification of rotor icing and aerodynamic imbalance. The model achieved classification accuracies ranging from 85 % to 98 %, highlighting its effectiveness in diagnosing wind turbine conditions, and improving the overall reliability and operational analysis of these systems.
Machado et al. (Wed,) studied this question.