Comparative Study of Vibration-Based Machine Learning Algorithms for Crack Identification and Location in Operating Wind Turbine Blades

The growing energy demand has increased the number of wind turbines, raising the need to monitor blade health. Since blades are prone to damage that can cause severe failures, early detection is crucial. Machine learning-based monitoring systems can identify and locate cracks without interrupting energy production, enabling timely maintenance. This study provides a comparative analysis and approach to the application and effectiveness of different vibration-based machine learning algorithms to detect the presence of cracks, identify the cracked blade, and locate the zone where the crack occurs in rotating blades of a small wind turbine. The datasets comprise root vibration signals, derived from healthy and cracked blades of a wind turbine in operational conditions. In this study, the blades are not considered identical. The sampling set dimension and the number of features were variables considered during the development and assessment of different models based on decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and multilayer perceptron algorithms (MLP). Overall, the KNN models are the clear winners in terms of training efficiency, even as the sample size increases. DT is the most efficient algorithm in terms of test speed, followed by SVM, MLP, and KNN.