Prediction of Wind Turbine Bearing Faults: A Machine Learning Approach

In this paper, we present a near investigation of five different AI algorithms for the fault classification of wind turbines in MATLAB: NN (Neural Networks), KNN (k-Nearest acquaintances), TREE (choice timber), SVM (Support Vector Machines), and Kernel SVM. We evaluated the performance of these algorithms on a real-world dataset of wind turbine condition data. Neural networks were the most computationally expensive to train but also the most accurate. SVM and kernel SVM were less computationally expensive than neural networks but were also less accurate. KNN and decision trees were the most efficient to train, but they were also the least accurate. Overall, our results suggest that neural networks are the best choice for the predictive classification of wind turbine faults in MATLAB. Still, SVM and kernel SVM may be better choices for applications where computational efficiency is more important than accuracy.