BEARING FAULT DIAGNOSIS METHOD OF WIND TURBINE BASED ON DEEP CONVOLUTIONAL CONDITIONAL GENERATIVE ADVERSARIAL NETWORK

Wang Na; Wang Zicong; Liu Jialin

doi:10.19912/j.0254-0096.tynxb.2024-2045

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 402-411. DOI: 10.19912/j.0254-0096.tynxb.2024-2045

BEARING FAULT DIAGNOSIS METHOD OF WIND TURBINE BASED ON DEEP CONVOLUTIONAL CONDITIONAL GENERATIVE ADVERSARIAL NETWORK

Wang Na^1,2, Wang Zicong¹, Liu Jialin¹

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Abstract

For the accuracy decreasing in fault diagnosis derived from the samples insufficient of the rolling bearings in wind turbines, a fault diagnosis method based on deep convolutional-conditional Wasserstein generative adversarial network （DC-CWGAN） is proposed. Firstly, the continuous wavelet transform （CWT） is applied to the vibration signals to construct a dataset of time-frequency feature images. As a result, the feature capture ability for the fault model is increased. Secondly, the fully connected layers in the conditional generative adversarial networks （CGAN） are replaced by the convolution al structures. Followingly, the Wasserstein distance is introduced to reconstruct the CGAN loss function. Thus the quality of the generated samples and the stability of the DC-CWGAN training are both strengthened. Thirdly, with the application of the model transfer strategy, the generalization and the computational efficiency of the objective classification network are enhanced. Finally, it is demonstrated that the diagnosis accuracy of the rolling bearings under the small-sample condition is improved effectively by the proposed method.

Key words

wind turbines / rolling bearings / fault diagnosis / small sample / model transfer / conditional generative adversarial network

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Wang Na, Wang Zicong, Liu Jialin. BEARING FAULT DIAGNOSIS METHOD OF WIND TURBINE BASED ON DEEP CONVOLUTIONAL CONDITIONAL GENERATIVE ADVERSARIAL NETWORK[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 402-411 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2045

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