A. de Leles Ferreira

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.