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Convolutional neural network based on attention mechanism and Bi-LSTM for bearing remaining life prediction

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Abstract

Good prognostic health management (PHM) plays a crucial role in industrial production and other fields. The accurate prediction of remaining useful life (RUL) can ensure good working condition of machines, and the selection of health indicators (HI) is the key to prediction. Due to the length and noise of the data, the selection of features requires a lot of prior knowledge. Therefore, a novel convolution-based attention mechanism bidirectional long and short-term memory (CABLSTM) network is proposed to achieve the end-to-end lifetime prediction of rotating machinery in this paper. Unlike concatenating two networks, the model in this paper is used to convolute the cell states of Bi-LSTM. Firstly, the input signal is performed through CNN to obtain feature information. Secondly, the obtained features are fed into the Bi-LSTM network with attention mechanism for convolution operation to obtain time-frequency information to construct HI. Finally, the training data are normalized to predict RUL. Bi-LSTM can capture features in longer time-frequency information, and attention mechanism can give input influence weight, and highlight its effective characteristics to obtain better prediction accuracy. The complex process of feature extraction, HI construction, and RUL prediction is combined in one algorithm by deep learning. To verify the performance of the method in this paper, experiments were conducted on the bearing dataset of PRONOSTIA, and compared with other methods. The results showed that the method outperforms other methods due to its better accuracy and prediction precision.

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Acknowledgements

The authors would like to thank the FEMTO-ST Institute for their public data.

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  1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201600, China

    Jiahang Luo & Xu Zhang

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  1. Jiahang Luo
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  2. Xu Zhang
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Correspondence to Xu Zhang.

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Luo, J., Zhang, X. Convolutional neural network based on attention mechanism and Bi-LSTM for bearing remaining life prediction. Appl Intell 52, 1076–1091 (2022). https://doi.org/10.1007/s10489-021-02503-2

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