RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON BNN-RA MODEL

Yu Ping; Song Ziqiong; Cao Jie; Chen Xiliang

doi:10.19912/j.0254-0096.tynxb.2023-1941

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (3) : 643-651. DOI: 10.19912/j.0254-0096.tynxb.2023-1941

RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON BNN-RA MODEL

Yu Ping^1~3, Song Ziqiong¹, Cao Jie¹, Chen Xiliang¹

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Abstract

To address the challenges of feature extraction, slow model iteration, and low accuracy in diagnosing faults in wind turbine bearings, this paper introduces a diagnostic approach based on an enhanced version of the Binarized Neural Network （BNN） methodology. Firstly, the Gramian Angular Field （GAF） is utilized to transform the bearing vibration signal into a two-dimensional image, improving the accuracy of feature extraction. Next, the BNN-RA model （BNN + Residual Network + Spatial Attention Network） is constructed by integrating a deep residual network with an attention mechanism, enabling efficient fault diagnosis for bearings. The results demonstrate that the proposed method significantly enhances both network iteration speed and diagnostic accuracy. Specifically, the model achieves convergence after only 11 iterations under a single working condition of the CWRU bearing dataset, with fault diagnosis accuracy reaching 99.20%. Furthermore, the average accuracy across the two datasets are 98.46% and 97.60% under different operating conditions, respectively.

Key words

wind turbines / fault diagnosis / bearing / binarized neural networks / Gramian angular field

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Yu Ping, Song Ziqiong, Cao Jie, Chen Xiliang. RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON BNN-RA MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 643-651 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1941

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