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Residual life prediction of rolling bearing based on ResNet-ABiLSTM

Published:2023-08-14 author:LIU Wen-guang, SI Yong-zhan. Browse: 466 Check PDF documents

Residual life prediction of rolling bearing based on ResNet-ABiLSTM

LIU Wen-guang, SI Yong-zhan

(School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

Abstract: Aiming at the problem that traditional data-driven methods over-rely on prior knowledge and lack feature extraction ability, which leads to low prediction accuracy, a residual service life prediction method(ResNet-ABiLSTM) combining residual network(ResNet) with selfattention mechanism(SAM) and bidirectional long and short term memory network(BiLSTM) was proposed. Firstly, the original monitoring signals were standardized and resampled by sliding window method to realize data expansion. Then, the residual network and the bidirectional long and short term memory network were used to extract the deep features of the data in the spatial dimension and the temporal dimension respectively, and the self-attention mechanism was introduced to focus on the more important features reflecting the equipment degradation trend in the spatial and temporal dimension. Finally, the PHM2012 bearing data set was used for verification, and the results were compared with the predicted results of CNN-LSTM, ResNet-BiLSTM, HI-GRNN, CNN-HI, ResNet-CBAM, DRN-BiGRU and other methods. The results show that the two error values (RMSE and MAE) of ResNet-ABiLSTM method are 0.037 and 0.029, respectively, which are significantly superior to other comparison methods. The results verify the accuracy and effectiveness of the proposed method for predicting bearing RUL.

Key words: rolling bearing; remaining useful life(RUL); residual network (ResNet); bidirectional long and short term memory network (BiLSTM); self-attention mechanism(SAM)

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