Skip to main content

Degradation Modeling and Reliability Prediction Based on Evidence Reasoning

  • Chapter
  • First Online:
Residual Life Prediction and Optimal Maintenance Decision for a Piece of Equipment

Abstract

In the previous chapter, we discussed the performance degradation modeling and residual life prediction methods based on stochastic process, support vector machine, relevance vector machine and other data driving.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pearl J (1988) Probabilistic reasoning in intelligence systems. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  2. Shafer G (1976) A Mathematical theory of evidence. Princeton Univ. Press, Princeton, NJ

    Book  Google Scholar 

  3. Walley P (1996) Measures of uncertainty in expert system. Artif Intell 83(1):1–58

    Article  MathSciNet  Google Scholar 

  4. Yang JB, Singh MG (1994) An evidential reasoning approach for multiple attribute decision making with uncertainty. IEEE Trans Syst Man Cybern Part A Syst Humans 24(1):1–18

    Google Scholar 

  5. Yang JB (2001) Rule and utility based evidential reasoning approach for multi-attribute decision analysis under uncertainties. Eur J Oper Res 131(1):31–61

    Article  Google Scholar 

  6. Yang JB, Xu DL (2002) On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans Syst Man Cybern Part A Syst Humans 32(3):289–304

    Google Scholar 

  7. Hu CH, Si XS, Yang JB et al (2011) Online updating with a probability-based prediction model using expectation maximization algorithm for reliability forecasting. IEEE Trans Syst Man Cybern Part A Syst Humans 41(6):1268–1277

    Google Scholar 

  8. Zhou ZJ, Hu CH, Yang JB et al (2009) Online updating belief-rule-based systems for pipeline leak detection under expert intervention. Exp Syst Appl 36(4):7700–7709

    Article  Google Scholar 

  9. Si XS, Hu CH, Zhou ZJ (2010) Fault prediction model based on evidential reasoning approach. Sci China Inf Sci 53(10):2032–2046

    Article  Google Scholar 

  10. Si X, Changhua Hu, Zhang Qi et al (2012) Fault prognosis based on evolving belief-rule-base system. Control Theor Appl 29(12):1589–1586

    Google Scholar 

  11. Hu CH, Si XS, Yang JB (2010) Systems reliability forecasting based on evidential reasoning algorithm with nonlinear optimization. Exp Syst Appl 37(3):2550–2562

    Article  Google Scholar 

  12. Chung PJ, Bohme JF (2005) Recursive EM and SAGE-Inspired algorithms with application to DOA estimation. IEEE Trans Sig Process 53(8):2664–2677

    Article  MathSciNet  Google Scholar 

  13. Dempster AP, Laird N, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B 39(1):1–38

    MathSciNet  MATH  Google Scholar 

  14. Titterington DM (1984) Recursive parameter estimation using incomplete data. J Roy Stat Soc B 46(2):257–267

    MathSciNet  MATH  Google Scholar 

  15. Cao L (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110:43–50

    Article  Google Scholar 

  16. Ying-Ming, Wang Jian-Bo, Yang Dong-Ling, Xu (2006) Environmental impact assessment using the evidential reasoning approach. Eur J Oper Res 174(3):1885–1913 https://doi.org/10.1016/j.ejor.2004.09.059

    Article  MATH  Google Scholar 

  17. Kushner HJ, Kelmanson MZ (1976) Stochastic approximation algorithms of the multiplier type for the sequential Monte Carlo optimization of stochastic systems. SIAM J Control Optim 14(5):827–842

    Article  MathSciNet  Google Scholar 

  18. Kushner HJ, Lakshmivarahan S (1977) Numerical studies of stochastic approximation procedures for constrained problems. IEEE Trans Autom Control 22(3):428–439

    Article  MathSciNet  Google Scholar 

  19. Kushner HJ, Yin GG (1997) Stochastic approximation algorithms and applications. Springer, New York

    Book  Google Scholar 

  20. Passino KM, Yurkovich S (1998) Fuzzy control. Addison-Wesley, Menlo Park, Calif

    Google Scholar 

  21. Harrison PJ, Stevens CF (1976) Bayesian forecasting. J Roy Stat Soc B 38(3):205–247

    MathSciNet  MATH  Google Scholar 

  22. West M (1981) Robust sequential approximate Bayesian estimation. J Roy Stat Soc B 43(2):157–166

    MathSciNet  MATH  Google Scholar 

  23. Ordonez R, Spooner JT, Passino KM (2006) Experimental studies in nonlinear discrete-time adaptive prediction and control. IEEE Trans Fuzzy Syst 14(2):275–286

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2022 National Defense Industry Press

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hu, C., Fan, H., Wang, Z. (2022). Degradation Modeling and Reliability Prediction Based on Evidence Reasoning. In: Residual Life Prediction and Optimal Maintenance Decision for a Piece of Equipment. Springer, Singapore. https://doi.org/10.1007/978-981-16-2267-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-2267-0_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2266-3

  • Online ISBN: 978-981-16-2267-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics