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Remaining useful life predictions for turbofan engine degradation based on concurrent semi-supervised model

  • Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems
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Abstract

As a crucial and expensive component of the aircraft, it is important to effectively predict its remaining useful life (RUL) so as to reduce maintenance costs and improve maintenance strategies. In this paper, a novel concurrent semi-supervised model is proposed to estimate the RUL of the aero-engine. This semi-supervised model can provide satisfying prediction results with only a small amount of labeled data. And the concurrent structure is designed to improve the stability and accuracy of the prediction. The proposed method is verified on the popular C-MAPSS dataset and is compared with a variety of state-of-the-art approaches. The experimental results demonstrate that the proposed method is effective in the task of remaining useful life prediction.

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References

  1. Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2013) Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Eng Appl Artif Intell 26(7):1751–1760

    Article  Google Scholar 

  2. Zaidan MA, Mills AR, Harrison RF, Fleming PJ (2016) Gas turbine engine prognostics using Bayesian hierarchical models: a variational approach. Mech Syst Signal Process 70–71:120–140

    Article  Google Scholar 

  3. Kan MS, Tan ACC, Mathew J (2015) A review on prognostic techniques for non-stationary and non-linear rotating systems. Mech Syst Signal Process 62–63:1–20

    Article  Google Scholar 

  4. Shin JH, Jun HB (2015) On condition based maintenance policy. J Comput Des Eng 2(2):119–127

    Google Scholar 

  5. Si XS, Wang WB, Hu CH, Zhou DH (2011) Remaining useful life estimation-a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14

    Article  MathSciNet  Google Scholar 

  6. André LE, Bjrlykhaug E, Sy V, Ushakov S, Zhang H (2018) Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture. Reliab Eng Syst Saf 183:240–251

    Google Scholar 

  7. Zhao G, Zhang G, Ge Q, Liu X (2016) Research advances in fault diagnosis and prognostic based on deep learning. In: Proceedings of PHM-2016, pp 1–6

  8. Pu G, Wang L, Shen J, Dong F (2021) A hybrid unsupervised clustering-based anomaly detection method. Tsinghua Sci Technol 26(2):146–153

    Article  Google Scholar 

  9. Gao J, Li F, Wang B, Liang H (2021) Unsupervised nonlinear adaptive manifold learning for global and local information. Tsinghua Sci Technol 26(2):163–171

    Article  Google Scholar 

  10. Wang T, Guo D, Sun X-M (2020) A new paralleled semi-supervised deep learning method for remaining useful life prediction. In: Proceedings of ICCSIP-2020, pp 1–8

  11. Juesas P, Ramasso E, Drujont S, Placet V (2016) On partially supervised learning and inference in dynamic Bayesian networks for prognostics with uncertain factual evidence : Illustration with Markov switching models. Int J Prognost Health Manag 7:1–9

    Google Scholar 

  12. Das K, Bhaduri K, Votava P (2011) Distributed anomaly detection using 1-class SVM for vertically partitioned data. Stat Anal Data Min 4:393–406

    Article  MathSciNet  Google Scholar 

  13. Giantomassi A, Ferracuti F, Benini A, Ippoliti G, Longhi S, Petrucci A (2011) Hidden Markov model for health estimation and prognosis of Turbofan engines. Int Conf Mechatronic Embedded Syst Appl 3:681–689

    Google Scholar 

  14. Mosallam A, Medjaher K, Zerhouni N (2016) Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. J Intell Manuf 27:1037–1048

    Article  Google Scholar 

  15. Babu GS, Zhao P, Li XL (2016) Deep convolutional neural network based regression approach for estimation of remaining useful life. Int Conf Database Syst Adv Appl 9642:214–228

    Article  Google Scholar 

  16. Li X, Ding Q, Sun JQ (2018) Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab Eng Syst Saf 172:1–11

    Article  Google Scholar 

  17. Yuan M, Wu Y, Lin L (2016) Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. Proc IEEE Int Conf Aircraft Util Syst 2016:135–140

    Google Scholar 

  18. Li J, Li X, He D (2019) A Directed Acyclic Graph Network Combined with CNN and LSTM for Remaining Useful Life Prediction. IEEE Access 99:75464–75475

    Article  Google Scholar 

  19. Yu W, Kim IY, Mechefske C (2020) An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme. Reliab Eng Syst Saf 199:1–12

    Article  Google Scholar 

  20. Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: Proceeding of ICLR2014, pp 1–14

  21. Saxena A, Goebel K, Simon D, Eklund N (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. In: Proceedings of PHM-2008, pp 1–9

  22. Liu H, Yu Y, Sun F, Gu J (2017) Visual-tactile fusion for object recognition. IEEE Trans Autom Sci Eng 14(2):996–1008

    Article  Google Scholar 

  23. Zhang C, Lim P, Qin AK, Tan KC (2017) Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans Neural Netw Learn Syst 28(10):2306–2318

    Article  Google Scholar 

  24. Liu H, Sun F, Zhang X (2019) Robotic material perception using active multi-modal fusion. IEEE Trans Ind Electron 66(12):9878–9886

    Article  Google Scholar 

  25. Ramasso E (2014) Investigating computational geometry for failure prognostics in presence of imprecise health indicator: results and comparisons on C-MAPSS datasets. In: 2nd Europen conference of the prognostics and health management society, vol 5, pp 1–13

  26. Wang B, Lei Y, Li N, Yan T (2019) Deep separable convolutional network for remaining useful life prediction of machinery. Mech Syst Signal Process 134:1–18

    Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos 61890920 & 61890921 and the LiaoNing Revitalization Talents Program XLYC1808015.

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Correspondence to Di Guo or Xi-Ming Sun.

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Wang, T., Guo, D. & Sun, XM. Remaining useful life predictions for turbofan engine degradation based on concurrent semi-supervised model. Neural Comput & Applic 34, 5151–5160 (2022). https://doi.org/10.1007/s00521-021-06089-1

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  • DOI: https://doi.org/10.1007/s00521-021-06089-1

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