Abnormality Detection Method for Wind Turbine Bearings Based on CNN-LSTM

Fanghong Zhang, Yuze Zhu, Chuanjiang Zhang, Peng Yu and Qingan Li
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Fanghong Zhang: The National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
Yuze Zhu: The National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
Chuanjiang Zhang: CSIC Haizhuang Windpower Co., Ltd., Chongqing 401122, China
Peng Yu: School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
Qingan Li: University of Chinese Academy of Sciences, Beijing 100049, China

Energies, 2023, vol. 16, issue 7, 1-11

Abstract: Wind turbine energy generators operate in a variety of environments and often under harsh operational conditions, which can result in the mechanical failure of wind turbines. In order to ensure the efficient operation of wind turbines, the detection of any abnormality in the mechanics is particularly important. In this paper, a method for detecting abnormalities in the bearings of wind turbine energy generators, based on the cascade deep learning model, is proposed. First, data on the mechanics of wind turbine generators were collected, and the correlation between the data was studied in order to select the parameters related to the bearing temperature. Then, the logical relationship between the observation parameters and the target parameters was established based on a one-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network, and the difference between the predicted temperature and the actual temperature was assessed using the root mean square error evaluation model. Finally, a numerical example was used to verify the operational data from a wind farm unit in northwest China. The results show that the CNN-LSTM model proposed in this paper can detect abnormalities earlier in the state of the main bearing than the LSTM model, and the CNN-LSTM model can detect abnormalities in the main bearing that the LSTM network cannot find.

Keywords: wind turbine; deep learning; bearing; failure early warning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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