ROTATING MACHINERY FAULT DIAGNOSIS METHOD BASED ON IMPROVED RESIDUAL NEURAL NETWORK

Xu Shuo; Deng Aidong; Yang Hongqiang; Fan Yongsheng; Deng Minqiang; Liu Dongchuan

doi:10.19912/j.0254-0096.tynxb.2022-0393

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (7) : 409-418. DOI: 10.19912/j.0254-0096.tynxb.2022-0393

ROTATING MACHINERY FAULT DIAGNOSIS METHOD BASED ON IMPROVED RESIDUAL NEURAL NETWORK

Xu Shuo^1,2, Deng Aidong^1,2, Yang Hongqiang³, Fan Yongsheng³, Deng Minqiang^1,2, Liu Dongchuan^1,2

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Abstract

Aiming at the problem that the traditional fault diagnosis method of wind turbine bearings and gearboxes components requires manual extraction of features and is easy to introduce human error and shallow neural network is difficult to extract features, resulting in poor diagnostic results, a fault diagnosis method for wind power bearings and gearboxes based on improved residual neural network is proposed. The selective kernel network（SKNet） structure is introduced to perform attention mechanisms on the convolution kernel, and the global context network（GCNet） structure is introduced to take full advantage of the global context information to rescale the weights of different channels. Experimental results show that the proposed method has good fault diagnosis ability for wind turbine bearings under strong noise conditions and wind turbine gearbox with multi-dimensional characteristics.

Key words

wind turbines / rolling bearing / gearbox / fault diagnosis / residual neural network

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Xu Shuo, Deng Aidong, Yang Hongqiang, Fan Yongsheng, Deng Minqiang, Liu Dongchuan. ROTATING MACHINERY FAULT DIAGNOSIS METHOD BASED ON IMPROVED RESIDUAL NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(7): 409-418 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0393

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