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- W3149885144 abstract "The wind turbine is a complex system which may shut down and result in huge economic lost due to component failures. The health status prediction of wind turbines can provide an important guidance and basis for the preventive maintenance of wind turbines, reduce maintenance costs and unplanned downtime, and improve the productivity and economic benefits of wind turbines. Aiming at the problem of wind turbines health status predication, this paper uses Long Short-Term Memory (LSTM) combined with Full Connected Networks (FC) to construct a network model. The LSTM is used to effectively extract the information from time-series data, and then the FC integrates the extracted information and completes the state classification to predict the wind turbines health status at the next moment. The proposed model was tested using the data of a wind power station in the north of China in 2019. The results show that the proposed method has a high classification accuracy compared to other state-of-the-art algorithms, which proves the effectiveness of the proposed model. Moreover, the proposed method can effectively predict the wind turbine health status in the next two hours, play a role in predictive maintenance, and has strong practical significance." @default.
- W3149885144 created "2021-04-13" @default.
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- W3149885144 date "2020-12-01" @default.
- W3149885144 modified "2023-10-17" @default.
- W3149885144 title "A modified framework based on LSTM-FC for wind turbine health status prediction" @default.
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- W3149885144 doi "https://doi.org/10.1109/bigdia51454.2020.00033" @default.
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