Icing Prediction of Fan Blade based on a Hybrid Model

For the problem that fan blade icing failures cannot be accurately predicted in advance, a data-driven fault prediction method is proposed in this paper. Firstly, the delay window is introduced to the PCA algorithm to extract the fault mode related features from the SCADA high-dimensional data. Then, the trained Elman neural network is adopted to predict the future value of the relevant features. Finally, a BP self-clustering algorithm is designed to predict the icing fault of the blade with the multi-source data fusion. The results show that the proposed method can effectively predict the icing failure of wind turbine blades and has reference significance for the maintenance of wind turbines.