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Prediction of the remaining useful life of rolling bearings by LSTM based on multidomain characteristics and a dual-attention mechanism

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Abstract

This study proposes a framework for bearing remaining useful life (RUL) prediction that uses multidomain features and a dual-attention mechanism (DAM). First, sparsity measures are introduced as new feature parameters to comprehensively and accurately extract the degradation features of bearings. Second, a long short-term memory network integrated with DAM is applied for RUL prediction. DAM simultaneously applies the attention mechanism to the time steps and feature dimension to increase the attention to important information and enhance the prediction performance of the network. Third, a pseudo-normalization method is proposed to solve the problem of unknown bearing test data in actual working conditions under the premise of retaining the original data characteristics and RUL prediction accuracy as much as possible. Lastly, the proposed framework is experimentally proven on public datasets and compared with other methods to prove its feasibility and effectiveness.

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Abbreviations

x i :

Signal series

N :

Number of time points

f :

Frequency

P(f):

Amplitude

f(t 1):

Feature vector

t k :

Time sequence

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (52105110 and 52005303) and the Natural Science Foundation of Shandong Province (ZR2021QE024, ZR2020QE157, and ZR2022ME119).

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Authors and Affiliations

  1. College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, 266000, China

    Huaiqian Bao, Lijin Song, Zongzhen Zhang, Baokun Han, Jinrui Wang, Junqing Ma & Xingwang Jiang

  2. Longkou Zhongyu Machinery Co., Ltd, Yantai, 265716, China

    Zongzhen Zhang

Authors
  1. Huaiqian Bao
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  2. Lijin Song
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  3. Zongzhen Zhang
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  4. Baokun Han
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  5. Jinrui Wang
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  6. Junqing Ma
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  7. Xingwang Jiang
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Corresponding author

Correspondence to Zongzhen Zhang.

Additional information

Zongzhen Zhang received his Ph.D. degree from Nanjing University of Aeronautics and Astronautics, China, in 2020. He is currently an Associate Professor at Shandong University of Science and Technology. He is mainly engaged in research on mechanical dynamics and fault diagnosis.

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Bao, H., Song, L., Zhang, Z. et al. Prediction of the remaining useful life of rolling bearings by LSTM based on multidomain characteristics and a dual-attention mechanism. J Mech Sci Technol 37, 4583–4596 (2023). https://doi.org/10.1007/s12206-023-0814-x

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  • DOI: https://doi.org/10.1007/s12206-023-0814-x

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