Advertisement

Remaining useful life prediction for multi-component systems with hidden dependencies

  • Xiaopeng Xi
  • Maoyin ChenEmail author
  • Donghua ZhouEmail author
Research Paper
  • 17 Downloads

Abstract

How can we predict the remaining useful life (RUL) of a dynamic system subject to multiple dependent degradations? Is it possible to address the above problem when the degradation information is obtained indirectly? According to a new type of state space-based model, we mainly develop an online RUL prediction method for the above system. In this model, the dependencies among different degradations can be reflected in a diffusion coefficient matrix. Considering that some industrial systems like blast furnaces are usually equipped with multi-sensors, an efficient information fusion strategy also plays an important role in predicting the RUL. Based on multi-dimensional observations, the hidden degradation states are identified through the sequential Kalman filtering. Meanwhile, the unknown parameters in the model are updated iteratively by the expectation maximization (EM) algorithm. At last, the RUL distributions are simulated through the Monte Carlo method, in which three types of failure structures with regard to the degradations are considered. The effectiveness of the proposed method is fully verified by a numerical example as well as a case study about the blast furnace.

Keywords

remaining useful life hidden dependencies state space model multi-source information fusion failure structure 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61490701, 61290324, 61473164) and Research Fund for the Taishan Scholar Project of Shandong Province of China.

References

  1. 1.
    Sikorska J Z, Hodkiewicz M, Ma L. Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process, 2011, 25: 1803–1836CrossRefGoogle Scholar
  2. 2.
    Okoh C, Roy R, Mehnen J, et al. Overview of remaining useful life prediction techniques in through-life engineering services. Procedia CIRP, 2014, 16: 158–163CrossRefGoogle Scholar
  3. 3.
    Xi X P, Chen M Y, Zhou D H. Remaining useful life prediction for degradation processes with memory effects. IEEE Trans Rel, 2017, 66: 751–760CrossRefGoogle Scholar
  4. 4.
    Goebel K F. Management of uncertainty in sensor validation, sensor fusion, and diagnosis of mechanical systems using soft computing techniques. Dissertation for Ph.D. Degree. Berkeley: University of California, 1996Google Scholar
  5. 5.
    Ahmadzadeh F, Lundberg J. Remaining useful life estimation: review. Int J Syst Assur Eng Manag, 2014, 5: 461–474CrossRefGoogle Scholar
  6. 6.
    Si X, Wang W, Hu C, et al. Remaining useful life estimation-a review on the statistical data driven approaches. Eur J Oper Res, 2011, 213: 1–14MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wei M H, Chen M Y, Zhou D H. Multi-sensor information based remaining useful life prediction with anticipated performance. IEEE Trans Rel, 2013, 62: 183–198CrossRefGoogle Scholar
  8. 8.
    Niu G, Yang B S. Intelligent condition monitoring and prognostics system based on data-fusion strategy. Expert Syst Appl, 2010, 37: 8831–8840CrossRefGoogle Scholar
  9. 9.
    Li R, Ryan J K. A Bayesian inventory model using real-time condition monitoring information. Prod Oper Manage, 2011, 20: 754–771CrossRefGoogle Scholar
  10. 10.
    Tang S, Yu C, Wang X, et al. Remaining useful life prediction of lithium-ion batteries based on the wiener process with measurement error. Energies, 2014, 7: 520–547CrossRefGoogle Scholar
  11. 11.
    Bian L, Gebraeel N. Stochastic framework for partially degradation systems with continuous component degradationrate-interactions. Naval Res Log, 2014, 61: 286–303CrossRefGoogle Scholar
  12. 12.
    Wang X, Guo B, Cheng Z. Residual life estimation based on bivariate Wiener degradation process with measurement errors. J Cent South Univ, 2012, 20: 1844–1851CrossRefGoogle Scholar
  13. 13.
    Xi Z M, Jing R, Wang P F, et al. A copula-based sampling method for data-driven prognostics. Reliability Eng Syst Saf, 2014, 132: 72–82CrossRefGoogle Scholar
  14. 14.
    Shi A H, Zeng J C. Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence. Comput Industrial Eng, 2016, 93: 192–204CrossRefGoogle Scholar
  15. 15.
    Khorasgani H, Biswas G, Sankararaman S. Methodologies for system-level remaining useful life prediction. Reliab Eng Syst Saf, 2016, 154: 8–18CrossRefGoogle Scholar
  16. 16.
    Rodrigues L R. Remaining useful life prediction for multiple-component systems based on a system-level performance indicator. IEEE/ASME Trans Mechatron, 2018, 23: 141–150CrossRefGoogle Scholar
  17. 17.
    Prakash O, Samantaray A K, Bhattacharyya R. Model-based multi-component adaptive prognosis for hybrid dynamical systems. Control Eng Practice, 2018, 72: 1–18CrossRefGoogle Scholar
  18. 18.
    Mercier S, Pham H H. A preventive maintenance policy for a continuously monitored system with correlated wear indicators. Eur J Oper Res, 2012, 222: 263–272MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Wang X L, Guo B, Cheng Z J. Residual life estimation based on bivariate Wiener degradation process with time-scale transformations. J Stat Comput Simul, 2014, 84: 545–563MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wei M H. Multi-sensor monitoring information based remaining useful life prediction for industrial equipments. Dissertation for Ph.D. Degree. Beijing: Tsinghua University, 2013Google Scholar
  21. 21.
    Liao H, Elsayed E A. Reliability inference for field conditions from accelerated degradation testing. Naval Res Log, 2006, 53: 576–587MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Trevisanello L, Meneghini M, Mura G, et al. Accelerated life test of high brightness light emitting diodes. IEEE Trans Device Mater Relib, 2008, 8: 304–311CrossRefGoogle Scholar
  23. 23.
    Ye Z S, Wang Y, Tsui K L, et al. Degradation data analysis using wiener processes with measurement errors. IEEE Trans Rel, 2013, 62: 772–780CrossRefGoogle Scholar
  24. 24.
    Wang X, Balakrishnan N, Guo B. Residual life estimation based on a generalized Wiener degradation process. Reliability Eng Syst Saf, 2014, 124: 13–23CrossRefGoogle Scholar
  25. 25.
    Ye Z S, Chen N, Shen Y. A new class of Wiener process models for degradation analysis. Reliability Eng Syst Saf, 2015, 139: 58–67CrossRefGoogle Scholar
  26. 26.
    Xi X P, Chen M Y, Zhou D H. Online prognostics based on multiple dependent degradation processes. In: Proceedings of Prognostics and System Health Management Conference (PHM), Harbin, 2017. 1–6Google Scholar
  27. 27.
    Kao Y H, van Roy B. Directed principal component analysis. Oper Res, 2014, 62: 957–972MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Tipping M E, Bishop C M. Probabilistic principal component analysis. J R Stat Soc B, 1999, 61: 611–622MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Chui C K, Chen G. Kalman Filtering. Berlin: Springer, 2009zbMATHGoogle Scholar
  30. 30.
    Shumway R H, Stoffer D S. Time Series Analysis and Its Applications. New York: Springer, 2000CrossRefzbMATHGoogle Scholar
  31. 31.
    Houtekamer P L, Mitchell H L. Ensemble Kalman filtering. Q J R Meteorol Soc, 2005, 131: 3269–3289CrossRefGoogle Scholar
  32. 32.
    Sun S L. Multi-sensor optimal fusion fixed-interval Kalman smoothers. Inf Fusion, 2008, 9: 293–299CrossRefGoogle Scholar
  33. 33.
    Lemieux C. Monte Carlo and Quasi-Monte Carlo Sampling. New York: Springer, 2009zbMATHGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of AutomationTsinghua UniversityBeijingChina
  2. 2.College of Electrical Engineering and AutomationShandong University of Science and TechnologyQingdaoChina

Personalised recommendations