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| Bearing Fault Diagnosis of Wind Turbine Based on Multi-wavelet-1D Convolutional LSTM |
| CHEN Wei-xing1,CUI Chao-chen1,LI Xiao-jing1,ZHAO Hui2 |
1. Civil Aviation University of China, Tianjin 300300, China
2. 31439 Troops of the Chinese Peoples Liberation Army, Shenyang, Liaoning 110000, China |
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Abstract To solve the problem of high false alarm rate of wind turbine bearing fault diagnosis under complex conditions, an end-to-end hybrid deep learning framework is proposed. One-dimensional convolutional recurrent neural network based on multiple wavelet transforms (multi-wavelet-1D Convolutional LSTM, Mw-1DConvLSTM). Firstly, multiple time-frequency maps are obtained by multiple wavelet transforms to fully extract the signal features. Then, an extended LSTM is used to extract different time step information of multi-channel time-frequency maps, and time-space characteristics of time-frequency data are captured. Finally, the fault state is classified by the global pooling layer and the classification layer. The test results show that under complex conditions, Mw-1DConvLSTM can achieve more than 95% fault identification of wind turbine bearing faults.
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Received: 18 August 2019
Published: 24 May 2021
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| Fund:Central University's basic research business Civil Aviation University of China special fund project;Central University's basic research business Civil Aviation University of China special fund project |
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2021, Vol. 42 
