RESEARCH ON BEARING FAULT DIAGNOSIS OF WIND TURBINES BASED ON TRANSFER LEARNING

An Wenjie; Chen Changzheng; Tian Miao; Su Xiaoming; Sun Xianming; Gu Yanling

doi:10.19912/j.0254-0096.tynxb.2022-0085

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (6) : 367-373. DOI: 10.19912/j.0254-0096.tynxb.2022-0085

RESEARCH ON BEARING FAULT DIAGNOSIS OF WIND TURBINES BASED ON TRANSFER LEARNING

An Wenjie¹, Chen Changzheng^1,2, Tian Miao¹, Su Xiaoming¹, Sun Xianming³, Gu Yanling¹

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Abstract

Aiming at the problem that the classification effect of the fault diagnosis model is low due to the complexity of wind turbine operating conditions and the difference in the distribution of vibration signals actually collected, a multi-scale convolutional neural network（MSCNN） with multi-kernel domain adaptation（MKDA） was proposed in this paper（MKDA-MSCNN）. In this method, the known wind turbine knowledge was transferred to the target wind turbine through the transfer theory to achieve fault diagnosis. Firstly, the MSCNN model was pre-trained by source domain data, and the MKDA was used to reduce the distribution difference between the source domain and target domain, and finally, the target wind turbine fault diagnosis model was obtained. The test results show that the proposed MKDA-MSCNN method in actual wind turbines in bearing fault diagnosis classification accuracy is as high as 96.17%. The comparison results show that the fault classification accuracy of the proposed method is superior to the other deep learning and deep transfer learning methods, which is valuable for the study of transfer learning theory in the fault diagnosis of wind turbine bearings in practical engineering.

Key words

wind turbines / fault diagnosis / rolling bearing / convolutional neural network / transfer learning

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An Wenjie, Chen Changzheng, Tian Miao, Su Xiaoming, Sun Xianming, Gu Yanling. RESEARCH ON BEARING FAULT DIAGNOSIS OF WIND TURBINES BASED ON TRANSFER LEARNING[J]. Acta Energiae Solaris Sinica. 2023, 44(6): 367-373 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0085

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