A study on wind turbine intelligent diagnosis based on HVD wavelet packet de noising coding deep learning
SHI Peiming1,FAN Yafei1,YI Siying1,HAN Dongying2
1.School of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China;
2.School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004 China
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.
时培明1,范雅斐1,伊思颖1,韩东颖2. 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[J]. 振动与冲击, 2022, 41(12): 196-201.
SHI Peiming1,FAN Yafei1,YI Siying1,HAN Dongying2. A study on wind turbine intelligent diagnosis based on HVD wavelet packet de noising coding deep learning. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(12): 196-201.
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