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Remaining useful life prediction of bearings based on ConvNeXt network and Stacked BiLSTM-Self-Attention mechanism

Published:2024-11-21 author:ZHANG Yinwe, WANG Linlin, XUE Wenke, et al. Browse: 127 Check PDF documents

Remaining useful life prediction of bearings based on ConvNeXt network and

Stacked BiLSTM-Self-Attention mechanism

ZHANG Yinwe, WANG Linlin, XUE Wenke, LIANG Wenjie

(School of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110000, China)

Abstract: Traditional methods have various problems such as poor robustness and low accuracy in the remaining service life of rolling bearings. In recent years, the development of deep learning has provided new ideas for solving these problems. In order to further improve the accuracy of predicting bearing life, a rolling bearing life prediction method based on ConvNeXt network, stacked bidirectional long short-term memory network (SBiLSTM) and self-attention mechanism (Self-Attention) was proposed. Firstly, continuous wavelet transform (CWT) was used to construct the time-frequency map of the vibration signal, in order to better capture the time-domain and frequency-domain characteristics of the signal. Then, the obtained time-frequency map was input into the constructed ConvNeXt network, and key features of the time-frequency map were extracted through operations such as convolution, pooling, and layer normalization. Finally, the extracted features were input into the SBiLSTM-Self-Attention module for further extraction of temporal information and feature weight allocation. The PHM2012 challenge data set was used for experimental verification. The root means square error (RMSE) and mean absolute error (MAE) of the proposed method were experimentally analyzed. The results show that, comparing with existing technical methods, the average RMSE of this method is 0.031. Comparing with the other three comparison methods, convolutional neural network (CNN), deep residual network-bidirectional gated recurrent unit (DRN-BiGRU) and deep convolutional neural networkself attention-bidirectional gated recurrent unit (DCNN-Self Attention-BiGRU), its average RMSE values respectively decrease by 79%, 74% and 55%, the average MAE values respectively decrease by 78%, 73% and 53%. This method has achieved good performance in predicting the remaining life of rolling bearings.

Key words: rolling bearings; remaining useful life (RUL) prediction; ConvNeXt network; stacked bidirectional long shortterm memory network (SBiLSTM); selfattention mechanism (SelfAttention); deep learning; continuous wavelet transform (CWT)

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