FAULT DIAGNOSIS OF WIND TURBINE GEARBOX BASED ON DEEP LEARNING

Xiao Junqing; Jin Jiangtao; Li Chun; Xu Zifei; Luo Shuai

doi:10.19912/j.0254-0096.tynxb.2021-0956

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (5) : 302-309. DOI: 10.19912/j.0254-0096.tynxb.2021-0956

FAULT DIAGNOSIS OF WIND TURBINE GEARBOX BASED ON DEEP LEARNING

Xiao Junqing¹, Jin Jiangtao¹, Li Chun^1,2, Xu Zifei¹, Luo Shuai¹

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Abstract

The gearbox of wind turbine runs in multi-noise and high speed for a long time, the vibration signal presents nonlinear characteristics, which makes it difficult to extract the fault information accurately and effectively. Based on this, a fault diagnosis method based on the fusion of complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）, and convolutional neural network is proposed. CEEMDAN’s strong nonlinear feature decomposition ability is used to decompose vibration signals, and multiple correlation coefficients are used to screen effective fault feature components and eliminate redundant components, and then the optimal component group is input into CNN to realize fault diagnosis. The results show that comparing with EMD-CNN and EEMD-CNN, the proposed method has better robustness and generalization under different fault states and SNR.

Key words

wind turbines / fault diagnosis / signal to noise ration / CNN / correlation analysis methods

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Xiao Junqing, Jin Jiangtao, Li Chun, Xu Zifei, Luo Shuai. FAULT DIAGNOSIS OF WIND TURBINE GEARBOX BASED ON DEEP LEARNING[J]. Acta Energiae Solaris Sinica. 2023, 44(5): 302-309 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0956

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