RESEARCH ON FAULT DIAGNOSIS METHOD OF WIND TURBINE BEARING BASED ON IBCAN

He Linfang; Wang Daohan; Tian Miao; An Wenjie; Sun Xianming

doi:10.19912/j.0254-0096.tynxb.2023-1380

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 97-104. DOI: 10.19912/j.0254-0096.tynxb.2023-1380

RESEARCH ON FAULT DIAGNOSIS METHOD OF WIND TURBINE BEARING BASED ON IBCAN

He Linfang¹, Wang Daohan¹, Tian Miao², An Wenjie², Sun Xianming³

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Abstract

Aiming at the problem that the fault categories of wind turbine bearings accumulate gradually over time under actual operating conditions, this paper proposes an improved fault diagnosis method with incremental learning capability of Width Convolutional Attention Network （IBCAN）, which can diagnose new fault categories without retraining the model. Firstly, the time-frequency characteristics of wind turbine bearing vibration signals are extracted using continuous wavelet transform （CWT）. Secondly, the convolutional attention network （CAN） will be used to obtain the deep feature representation of the wavelet transform map of the bearing vibration signal for historical fault category datasets. Then, using elastic network regression to improve the width learning system （IBLS） and transfer the features and corresponding labels obtained from CAN to IBLS for classification. This paper conducts experimental verification on the proposed method through the collected bearing data of wind turbines under actual operating conditions and compares it with other methods. The results show that this method can effectively update the fault diagnosis model of wind turbine bearings, incrementally learn new fault categories, and is of great significance for the research of wind turbine bearing fault diagnosis in practical engineering.

Key words

wind turbines / bearings / fault diagnosis / incremental learning / convolutional neural network

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He Linfang, Wang Daohan, Tian Miao, An Wenjie, Sun Xianming. RESEARCH ON FAULT DIAGNOSIS METHOD OF WIND TURBINE BEARING BASED ON IBCAN[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 97-104 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1380

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