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Intelligent Prediction of Bearing Remaining Useful Life Based on Data Enhancement and Adaptive Temporal Convolutional Networks

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Abstract

The bearing is one of the most important components of rotating machinery. Predicting the remaining useful life (RUL) of bearings is of great importance to avoid unforeseen failures in the mechanical system and reduce the maintenance cost of the system. In conventional RUL prediction methods, the influence of noise and timing information on the prediction accuracy is often ignored. To address this problem, a method for RUL prediction of bearings based on data enrichment and adaptive temporal convolutional networks (TCN) is proposed in this paper. First, the sample entropy is used to optimize the singular spectrum decomposition (SSD) and combine with the correlation coefficient, and the data enhancement method based on the enhanced SSD and correlation coefficient (ISSD–CC) is proposed to reduce the signal noise; then, the time domain information is extracted to construct the feature vector. Finally, the Nadam optimizer and adaptive parametric rectified linear unit activation function (APRelu) are introduced, and an improved temporal convolutional network (ITCN) is proposed to improve the adaptive learning ability of TCN for time and time–domain information. IEEE PHM2012 dataset and the XJTU-SY dataset are used to verify the feasibility of the proposed method. The experimental results show that the accuracy of ISSD correlation coefficient and ITCN prediction method in RUL prediction of bearings is better than other comparison methods.

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References

  1. D. He, C. Liu, Z. Jin et al., Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning. Energy. 239, 122108 (2022)

    Article  Google Scholar 

  2. Z. Jin, Y. Xiao, D. He et al., Fault diagnosis of bearing based on refined piecewise composite multivariate multiscale fuzzy entropy. Digit. Signal. Process, 133, 103884 (2023)

    Article  Google Scholar 

  3. S.K. Yang, C.M. Chen, H.L. Chang, A combined preventive maintenance strategy for bearings to accomplish the failure prevention of rotating equipment. J. Fail. Anal. Prev. 22(4), 1457–1467 (2022)

    Article  Google Scholar 

  4. J. Lei, W. Zhang, Z. Jiang et al., A review: prediction method for the remaining useful life of the mechanical system. J. Fail. Anal. Prev. 22(6), 2119–2137 (2022)

    Article  Google Scholar 

  5. Z. Jin, D. He, Z. Wei, Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN. Eng. Appl. Artif. Intell. 110, 104713 (2022)

    Article  Google Scholar 

  6. R. Guo, Z. Liu, Y. Wei, Remaining useful life prediction for the air turbine starter based on empirical mode decomposition and relevance vector machine. Trans. Inst. Meas. Control. 42(13), 2578–2588 (2020)

    Article  Google Scholar 

  7. R. Guo, Y. Wang, H. Zhang et al., Remaining useful life prediction for rolling bearings using EMD-RISI-LSTM. IEEE Trans. Instrum. Meas. 70, 1–12 (2021)

    CAS  Google Scholar 

  8. S. Zhao, Y. Zhang, S. Wang et al., A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method. Measurement. 146, 279–288 (2019)

    Article  Google Scholar 

  9. X. Yan, Y. Liu, Y. Xu et al., Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity. Renew. Energy. 170, 724–748 (2021)

    Article  Google Scholar 

  10. C. Xiao, H. Tang, Y. Ren et al., Fuzzy entropy assisted singular spectrum decomposition to detect bearing faults in axial piston pump. Alex. Eng. J. 61(8), 5869–5885 (2022)

    Article  Google Scholar 

  11. B. Cui, P. Guo, W. Zhang, Research on fault diagnosis of rolling bearing based on the MCKD-SSD-TEO with optimal parameters. J. Mech. Sci. Technol. 37(1), 31–42 (2023)

    Article  Google Scholar 

  12. W. Sun, B. Tan, Q. Wang, Multi-step wind speed forecasting based on secondary decomposition algorithm and optimized back propagation neural network. Appl. Soft Comput. 113, 107894 (2021)

    Article  Google Scholar 

  13. B. Pang, G. Tang, T. Tian, Enhanced singular spectrum decomposition and its application to rolling bearing fault diagnosis. IEEE Access. 7, 87769–87782 (2019)

    Article  Google Scholar 

  14. Y. Mao, M. Jia, X. Yan, A new bearing weak fault diagnosis method based on improved singular spectrum decomposition and frequency-weighted energy slice bispectrum. Measurement. 166, 108235 (2020)

    Article  Google Scholar 

  15. Z. Jin, D. He, Z. Lao et al., Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM. Nonlinear Dyn. 111(6), 5287–5306 (2023)

    Article  Google Scholar 

  16. Z.H. Liu, X.D. Meng, H.L. Wei et al., A regularized LSTM method for predicting remaining useful life of rolling bearings. Int. J. Autom. Comput. 18(4), 581–593 (2021)

    Article  Google Scholar 

  17. S. Fu, Y. Zhang, L. Lin et al., Deep residual LSTM with domain-invariance for remaining useful life prediction across domains. Reliab. Eng. Syst. Saf. 216, 108012 (2021)

    Article  Google Scholar 

  18. Y. Ge, L. Guo, Y. Dou, Remaining useful life prediction of machinery based on KS distance and LSTM neural network. Int. J. Perform. Eng. 15(3), 895 (2019)

    Google Scholar 

  19. Y. Qin, S. Xiang, Y. Chai et al., Macroscopic–microscopic attention in LSTM networks based on fusion features for gear remaining life prediction. IEEE Trans. Industr. Electron. 67(12), 10865–10875 (2019)

    Article  Google Scholar 

  20. J.Y. Wu, M. Wu, Z. Chen et al., Degradation-aware remaining useful life prediction with LSTM autoencoder. IEEE Trans. Instrum. Meas. 70, 1–10 (2021)

    Google Scholar 

  21. H. Yan, Y. Qin, S. Xiang et al., Long-term gear life prediction based on ordered neurons LSTM neural networks. Measurement. 165, 108205 (2020)

    Article  Google Scholar 

  22. A.Z. Hinchi, M. Tkiouat, Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network. Procedia Comput. Sci. 127, 123–132 (2018)

    Article  Google Scholar 

  23. S. Bai, J.Z. Kolter, V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018.

  24. W. Yang, Q. Yao, K. Ye et al., Empirical mode decomposition and temporal convolutional networks for remaining useful life estimation. Int. J. Parallel Prog. 48(1), 61–79 (2020)

    Article  CAS  Google Scholar 

  25. C. Liu, L. Zhang, C. Wu, Direct remaining useful life prediction for rolling bearing using temporal convolutional networks. 2019 IEEE symposium series on computational intelligence (SSCI). (IEEE, 2019), pp. 2965-2971

  26. Y. Wang, L. Deng, L. Zheng et al., Temporal convolutional network with soft thresholding and attention mechanism for machinery prognostics. J. Manuf. Syst. 60, 512–526 (2021)

    Article  Google Scholar 

  27. P. Bonizzi, J.M.H. Karel, O. Meste et al., Singular spectrum decomposition: a new method for time series decomposition. Adv. Adapt. Data Anal. 6(04), 1450011 (2014)

    Article  Google Scholar 

  28. Z. Wei, D. He, Z. Jin et al., Density-based affinity propagation tensor clustering for intelligent fault diagnosis of train bogie bearing. IEEE Trans. Intell. Transp. Syst. 24(6), 6053–6064 (2023)

    Article  Google Scholar 

  29. J. Sharma, S. Soni, P. Paliwal et al., A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: a case study of India. Energy Sci. Eng. 10, 2909–2929 (2022)

    Article  Google Scholar 

  30. M. Zhao, S. Zhong, X. Fu et al., Deep residual networks with adaptively parametric rectifier linear units for fault diagnosis. IEEE Trans. Ind. Electron. 68(3), 2587–2597 (2020)

    Article  Google Scholar 

  31. Q. Li, C. Yan, G. Chen et al., Remaining useful life prediction of rolling bearings based on risk assessment and degradation state coefficient. ISA Trans. 129, 413–428 (2022)

    Article  Google Scholar 

  32. P. Li, X. Liu, Y. Yang, Remaining useful life prognostics of bearings based on a novel spatial graph-temporal convolution network. Sensors. 21(12), 4217 (2021)

    Article  Google Scholar 

  33. B. Wang, Y. Lei, N. Li et al., A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Trans. Reliab. 69(1), 401–412 (2018)

    Article  Google Scholar 

Download references

Acknowledgments

The research was supported by the National Natural Science Foundation of China [Grant Number 72201146] and Project of Improving the Basic Ability of Scientific Research of Young and Middle-aged Teachers in Guangxi Universities [Grant Number 2022KY1134].

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Authors and Affiliations

  1. School of Traffic Management Engineering, Guangxi Police College, Nanning, China

    Bo Su

  2. Guangxi Transport Vocational and Technical College, Nanning, China

    Yingqian Sun

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  1. Bo Su
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Correspondence to Yingqian Sun.

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Su, B., Sun, Y. Intelligent Prediction of Bearing Remaining Useful Life Based on Data Enhancement and Adaptive Temporal Convolutional Networks. J Fail. Anal. and Preven. 23, 2709–2720 (2023). https://doi.org/10.1007/s11668-023-01813-6

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  • DOI: https://doi.org/10.1007/s11668-023-01813-6

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