<ul><li>Predictive maintenance (PdM) is pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically scheduling maintenance.</li><li>A novel deep learning (DL) methodology, ForeNet-2d and ForeNet-3d, is proposed for future RUL forecasting.</li><li>The models successfully forecast RUL for seven wind turbine failures with a 2-week forecast window.</li><li>The methodology offers a substantial time frame for remote wind turbines maintenance, enabling the practical implementation of PdM.</li></ul>

RUL forecasting for wind turbine predictive maintenance based on deep learning

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