Leading Edge Erosion Classification in Offshore Wind Turbines Using Feature Extraction and Classical Machine Learning

Best, Oscar, Khan, Asiya, Sharma, Sanjay, Collins, Keri and Gianni, Mario ORCID: 0000-0001-5410-2377 (2024) Leading Edge Erosion Classification in Offshore Wind Turbines Using Feature Extraction and Classical Machine Learning Energies, 17 (21). p. 5475. ISSN 1996-1073, 1996-1073

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Abstract

Leading edge (LE) erosion is a type of damage that inhibits the aerodynamic performance of a wind turbine, resulting in high operation and maintenance (O&M) costs. This paper makes use of a small dataset consisting of 50 images of LE erosion and healthy blades for feature extraction and the training of four types of classifiers, namely, support vector machine (SVM), random forest, K-nearest neighbour (KNN), and multi-layer perceptron (MLP). Six feature extraction methods were used with these classifiers to train 24 models. The dataset has also been used to train a convolutional neural network (CNN) model developed using Keras. The purpose of this work is to determine whether classical machine learning (ML) classifiers trained with extracted features can produce higher-accuracy results, train faster, and classify faster than deep learning (DL) models for the application of LE damage detection of wind turbine blades. The oriented fast and rotated brief (ORB)-trained SVM achieved an accuracy of 90% ± 0.01, took 80.4 s to train, and achieved inference speeds of 63 frames per second (FPS), compared to the CNN model, which achieved an accuracy of 79.4% ± 2.07, took 4667.4 s to train, and achieved an inference speed of 1.3 FPS. These results suggest that classical ML models can be more accurate and efficient than DL models if the appropriate feature extraction method is used.

Item Type:	Article
Uncontrolled Keywords:	machine learning, damage detection, feature extraction, offshore devices
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	29 Nov 2024 09:56
Last Modified:	16 Jun 2026 20:23
DOI:	10.3390/en17215475
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3188935
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