Skip to main content

Degradation Modeling and Residual Life Prediction Based on Fuzzy Model of Relevance Vector Machine

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

Abstract

Generally, the core of realizing the prediction, control and decision-making of the actual system is to establish the mathematical model of the system.

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. Chen Y (2005) Support vector machine method and fuzzy system. Fuzzy Syst Math 19(1):1–11

    Google Scholar 

  2. Dragan K (2002) Design of adaptive Takagi–Sugeno–Kang fuzzy models. Appl Soft Comput 2(2):89–103

    Article  Google Scholar 

  3. Chen SW, Wang J, Wang DS (2008) Extraction of fuzzy rules by using support vector machines. In: Proceedings of the 2008 fifth international conference on fuzzy systems and knowledge discovery, IEEE Computer Society Washington, DC, USA. IEEE, pp 438–442

    Google Scholar 

  4. Wong CC, Chen CC (1999) A hybrid clustering and gradient descent approach for fuzzy modeling. IEEE Trans Syst Man Cybern Part B 29(6):686–693

    Google Scholar 

  5. Huang XX, Shi FH, Gu W et al (2009) SVM-based fuzzy rules acquisition system for pulsed GTAW process. Eng Appl Artif Intell 22(8):1245–1255

    Article  Google Scholar 

  6. Cai Q, Hao Z, Liu W (2009) TSK fuzzy system based on fuzzy partition and support vector machine. Pattern Recogn Artif Intell 22(3):411–416

    Google Scholar 

  7. Liu H, Zhou D, Qian F (2008) Control of double inverted pendulum based on fuzzy inference of support vector machine. Chin J Sci Instrum 29(2):330–335

    Google Scholar 

  8. Wei L (2009) Study on fuzzy model identification based on kernel method. Doctoral Dissertation of Shanghai Jiaotong University, Shanghai

    Google Scholar 

  9. Xu XM, Mao YF, Xiong JN, et al (2007) Classification performance comparison between RVM and SVM. In: IEEE international workshop on anti-counterfeiting, security, identification, Fujian, China. IEEE, pp 208–211

    Google Scholar 

  10. Tipping ME (2000) The relevance vector machine. In: Solla SA, Leen TK, Müller K-R (eds) Advances in neural information processing systems, vol 12. MIT Press, Cambridge, pp 652–658

    Google Scholar 

  11. Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1(3):211–244

    MathSciNet  MATH  Google Scholar 

  12. Zhang X, Chen F, Gao J et al (2006) Prediction of sparse Bayesian time series. Control Decis Making 21(5):585–588

    MATH  Google Scholar 

  13. Kim J, Suga Y, Won S (2006) A new approach to fuzzy modeling of nonlinear dynamic systems with noise: relevance vector learning mechanism. IEEE Trans Fuzzy Syst 14(2):222–231

    Article  Google Scholar 

  14. Wang ZQ, Hu CH et al (2013) A new online fuzzy modelling method considering prior information with its application in PHM. Int J Adv Comput Technol 5(6):694–703

    Google Scholar 

  15. Changhua Hu, Wang Z et al (2011) A RVM fuzzy model identification method and its application in fault prediction. Acta Automatica Sinica 37(4):503–512

    MathSciNet  Google Scholar 

  16. Wang Z (2010) Adaptive fuzzy system and its application in fault prediction of inertial devices. Rocket Force University of Engineering, Xi’an

    Google Scholar 

  17. Yang G, Zhou X, Xuchu Yu (2010) Study of sparse Bayesian model and relevance vector machine. Comput Sci 37(7):225–228

    Google Scholar 

  18. Translated by Shen X, Fang Q, Lou Y et al (1989) (author, Дзяднк BК) Introduction to uniform approximation of polynomials. Peking University Press, Beijing

    Google Scholar 

  19. Wang LX, Mendel JM (1992) Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans Neural Netw 3(5):807–814

    Article  Google Scholar 

  20. Si X (2009) Research on fault prediction method of nonlinear system based on evidence reasoning and its application. Master's Thesis of Rocket Force University of Engineering, Xi'an

    Google Scholar 

  21. Zhou ZJ, Hu CH, Xu DL et al (2010) A model for real-time failure prognosis based on hidden Markov model and belief rule base. Eur J Oper Res 207(1):269–283

    Article  MathSciNet  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 Residual Life Prediction Based on Fuzzy Model of Relevance Vector Machine. 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_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-2267-0_7

  • 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