基于HVD小波包降噪编码深度学习的风电机组智能诊断研究

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Abstract An intelligent detection method based on the deep feature learning and Hilbert vibration decomposition (Hilbert Vibration Decomposition, HVD) of the denoising encoder is proposed to address the problem of the disturbance caused by the nonlinear and non-stationarity effect of the vibration signals of the fan gearbox bearing. The kurtosis evaluation index is introduced to evaluate the HVD component modal, and the wavelet packet is used to extract the energy entropy of the component to construct the feature vector to realize data preprocessing. A stacked denoising encoder (Stacked Denoising Autoencoder, SDAE) model was constructed to complete signal feature learning and fault classification. Two bearing datasets were used for algorithm verification, the experimental results show that the proposed HVD wavelet packet denoising encoding method (HWSDAE) can effectively identify fault signals, has outstanding diagnostic performance, and the single diagnostic accuracy is up to 100%, the average diagnostic accuracy is 99.49%, improving the diagnostic accuracy by 13.52% compared to the unprepared bearing data input into the SDAE model.

Key words： Fault diagnosis; hilbert vibration decomposition; wavelet packet;　denoising autoencoder

Received: 16 December 2021 Published: 28 June 2022

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	Articles by authors
	SHI Peiming1
	FAN Yafei1
	YI Siying1
	HAN Dongying2

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SHI Peiming1,FAN Yafei1,YI Siying1, et al. A study on wind turbine intelligent diagnosis based on HVD wavelet packet de noising coding deep learning[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(12): 196-201.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I12/196

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