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- W4205584925 abstract "AbstractAiming at the problem that there are single characteristics and non-exquisite granularity for the existing key component state prediction methods of wind turbines, a wind turbine spindle fault state prediction model based on fusion structure is proposed. The model based on integration can more fully mine the information representation of wind turbine status. The Factorization Machine (FM) module is used to fine-tune the parameters of first-order and second-order features of the wind turbine. The Cross Network is used to perform a finite-order feature cross-product. The Deep Network module is combined to explore the advantages of high-order wind turbine feature information. The fusion structure model effectively fuses the features of the three modules. The influence of the high and low order features are fully considered to improve the fault state prediction effect of the model. In this paper, Area Under the Curve (AUC) and Log-likelihood Loss (Logloss) are used as evaluation indexes to verify the model's prediction performance in the real wind turbine data set of the SCADA system. The experimental results show that the spindle fault state prediction model based on the fusion structure has better performance than the traditional prediction model and the latest deep learning prediction model.KeywordsWind turbine spindleFusion structureNeural networkFault predictionHigh-order features" @default.
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- W4205584925 date "2022-01-01" @default.
- W4205584925 modified "2023-10-17" @default.
- W4205584925 title "Fusion Structure-based Fault State Prediction of Wind Turbine Spindle" @default.
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- W4205584925 doi "https://doi.org/10.1007/978-981-16-8430-2_64" @default.
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