Transfer prediction of RUL of rolling bearing under variable operating conditions based on TCN and residual self-attention

PAN Xuejiao1, DONG Shaojiang2, ZHU Peng3, ZHOU Cunfang2, SONG Kai4

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (1) : 145-152.

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (1) : 145-152.

Transfer prediction of RUL of rolling bearing under variable operating conditions based on TCN and residual self-attention

  • PAN Xuejiao1, DONG Shaojiang2, ZHU Peng3, ZHOU Cunfang2, SONG Kai4
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Abstract

Aiming at the problem of the difference in characteristic distribution of the collected data of rolling bearing life status under variable working conditions and the poor generalization ability of the deep neural network model, an end-to-end transfer prediction method of rolling bearing remaining life (RUL) is proposed in this paper, combining the temporal convolutional neural network (TCN) and the residual self-attention mechanism (RSAM). Firstly, the one-dimensional time-domain signal collected by the sensor was converted into a frequency-domain signal by short-time Fourier transform; Secondly, the proposed network was used in the general feature extraction layer of the transfer residual life prediction network. While extracting time series information, the RSAM was further used to capture the local degradation features of the bearing, which enhances the model's ability to extract transfer features; Thirdly, the proposed joint domain self-adaptive strategy is used to match the characteristic distribution difference of rolling bearing life state data under variable working conditions, so as to realize the migration prediction of bearing life state information under different working conditions; Finally, the experimental verification was carried out on the full life of the rolling bearing. The results show that the proposed method can effectively realize the RUL prediction of the rolling bearing under variable working conditions, and obtain a better prediction. performance.

Key words

remaining useful life / rolling bearings / temporal convolutional neural network / residual self-attention / transfer learning.

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PAN Xuejiao1, DONG Shaojiang2, ZHU Peng3, ZHOU Cunfang2, SONG Kai4. Transfer prediction of RUL of rolling bearing under variable operating conditions based on TCN and residual self-attention[J]. Journal of Vibration and Shock, 2024, 43(1): 145-152

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