FAULT DIAGNOSIS METHOD OF WIND TURBINES ROLLING BEARING BASED ON IMPROVED RESNET AND TRANSFER LEARNING

Lei Chunli; Xue Linlin; Jiao Mengxuan; Zhang Huqiang; Shi Jiashuo

doi:10.19912/j.0254-0096.tynxb.2022-0204

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (6) : 436-444. DOI: 10.19912/j.0254-0096.tynxb.2022-0204

FAULT DIAGNOSIS METHOD OF WIND TURBINES ROLLING BEARING BASED ON IMPROVED RESNET AND TRANSFER LEARNING

Lei Chunli^1,2, Xue Linlin¹, Jiao Mengxuan¹, Zhang Huqiang¹, Shi Jiashuo¹

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Abstract

In order to solve the problem of few fault training samples of wind turbines rolling bearings in practical applications, a novel rolling bearing fault diagnosis model based on improved residual neural network and transfer learning under small samples condition is proposed. Firstly, the squeeze-and-excitation networks is embedded into the one-dimensional residual neural network, which increases the feature extraction ability of the model. Secondly, the improved residual neural network model is trained with source domain data to determine the structure and parameters, and L2 regularization method and Dropout mechanism are used to suppress overfitting. Then, transfer learning is introduced to freeze some model parameters trained with source domain data, and fine-tune the full connection layer parameters of the model by using a small amount of target domain data. Finally, the samples of different faults are classified. This method is verified by experiments on the bearing data set of Case Western Reserve University and the bearing data set of our laboratory. The experimental results show that under different experimental conditions, the proposed method has higher fault diagnosis accuracy and stronger generalization ability than that of compared methods.

Key words

wind turbines / rolling bearings / fault diagnosis / transfer learning / squeeze-and-excitation networks / small sample

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Lei Chunli, Xue Linlin, Jiao Mengxuan, Zhang Huqiang, Shi Jiashuo. FAULT DIAGNOSIS METHOD OF WIND TURBINES ROLLING BEARING BASED ON IMPROVED RESNET AND TRANSFER LEARNING[J]. Acta Energiae Solaris Sinica. 2023, 44(6): 436-444 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0204

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