FAULT DIAGNOSIS METHOD OF WIND TURBINE PLANETARY GEARBOX BASED ON ENHANCED CONVOLUTIONAL EURAL NETWORK

Liang Shuman; Gu Yanling; Luo Yuanqing; Chen Changzheng

doi:10.19912/j.0254-0096.tynxb.2021-1109

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (2) : 146-152. DOI: 10.19912/j.0254-0096.tynxb.2021-1109

FAULT DIAGNOSIS METHOD OF WIND TURBINE PLANETARY GEARBOX BASED ON ENHANCED CONVOLUTIONAL EURAL NETWORK

Liang Shuman¹, Gu Yanling^1,2, Luo Yuanqing¹, Chen Changzheng^1,2

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Abstract

Aiming at the problem that the health maintenance and state detection of wind turbine planetary gearboxes are difficult to diagnose, a fault diagnosis research method of the initial dilated convolutional neural network （IDCNN） that combines the initial net and dilated convolution is proposed in this paper. This method first constructs an initial dilated convolutional layer to expand the receptive field to enrich the learned fault features. Subsequently, in order to facilitate signal input and ensure rich information, a preprocessing method of transforming the one-dimensional original signal sequence into a two-dimensional matrix will be adopted. Finally, the generated two-dimensional signal is input into IDCNN for model training, and the model is evaluated with test data. The experimental results show that the proposed IDCNN method has high accuracy in the fault diagnosis of the planetary gearbox of the wind turbine. In the comparison results, the diagnosis accuracy of the proposed method is higher than that of the traditional deep learning method.

Key words

wind turbines / inception net / dilated convolutional neural network / planetary gearbox / fault diagnosis

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Liang Shuman, Gu Yanling, Luo Yuanqing, Chen Changzheng. FAULT DIAGNOSIS METHOD OF WIND TURBINE PLANETARY GEARBOX BASED ON ENHANCED CONVOLUTIONAL EURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(2): 146-152 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1109

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