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A deep learning-based two-stage prognostic approach for remaining useful life of rolling bearing

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Abstract

Remaining useful life (RUL) prediction is of great importance to improve the reliability and availability of machinery. Traditional rolling bearing RUL prediction methods ignore the impact of bearing degradation states recognition on accurate the RUL prediction. A novel deep learning-based two-stage RUL prognostic approach is presented in this paper by using fast search and find of density peaks clustering (FSFDPC) and multi-dimensional deep neural network (MDDNN). In the first stage, health states of the rolling bearing are automatically perceived by the FSFDPC, where no manual parameters need to be preset. In the second stage, MDDNN is constructed with parallel BLSTM and BGRU channels to extract the multi-dimensional feature maps from the input for accurate RUL prediction. The effectiveness of the proposed two-stage prognostic approach is validated by rolling bearing experimental data of three cases. The comparisons with other existing approaches show that the proposed approach has superior prediction performance under different working conditions.

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Acknowledgements

This research is supported financially by Grant No. 51875225 and 52075202 from the National Natural Science Foundation of China. The authors would like to thank the editors and the anonymous reviewers for their insightful comments and suggestions.

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Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Yiwei Cheng & Haiping Zhu

  2. School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, China

    Kui Hu & Jun Wu

  3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    Carman K. M. Lee

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  1. Yiwei Cheng
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  2. Kui Hu
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  3. Jun Wu
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  4. Haiping Zhu
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  5. Carman K. M. Lee
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Correspondence to Jun Wu.

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Cheng, Y., Hu, K., Wu, J. et al. A deep learning-based two-stage prognostic approach for remaining useful life of rolling bearing. Appl Intell 52, 5880–5895 (2022). https://doi.org/10.1007/s10489-021-02733-4

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