Open access
2026
Prediction and Classification of Residual Service Life in Wind Turbine Bearings Under Variable Speed Conditions Using Hybrid Machine Learning Models
A hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings is proposed by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach, offering a reliable and scalable solution for predictive maintenance and assessment of degradation progression in wind turbine bearings.
Gustavo Gomes Do Valle, Benjamin Soudhan, Meisam Mahdavi et al.
· IEEE Access · 0 citations