Skip to main content
SpringerLink
Log in

Hybrid prognostic method applied to mechatronic systems

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Fault detection and isolation, or fault diagnostic, of mechatronic systems has been the subject of several interesting works. Detecting and isolating faults may be convenient for some applications where the fault does not have severe consequences on humans as well as on the environment. However, in some situations, diagnosing faults may not be sufficient and one needs to anticipate the fault. This is what is done by fault prognostics. This latter activity aims at estimating the remaining useful life of systems by using three main approaches: data-driven prognostics, model-based prognostics, and hybrid prognostics. In this paper, a hybrid prognostic method is proposed and applied on a mechatronic system. The method relies on two phases: an offline phase to build the behavior and degradation models and an online phase to assess the health state of the system and predict its remaining useful life.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. AFNOR (2005) Condition monitoring and diagnostics of machines—prognostics—part 1: general guidelines. NF ISO 13381-1

  2. Chelidze D, Cusumano J (2004) A dynamical systems approach to failure prognosis. J Vib Acoust 126:2–8

    Article  Google Scholar 

  3. Dong M, He D (2007) A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mech Syst Signal Process 21:2248–2266

    Article  Google Scholar 

  4. Heng A, Tan AC, Mathew J, Montgomery N, Banjevic D, Jardine AK (2009) Intelligent condition-based prediction of machinery reliability. Mech Syst Signal Process 23(5):1600–1614

    Article  Google Scholar 

  5. Heng A, Zhang S, Tan AC, Mathew J (2009) Rotating machinery prognostics: state of the art, challenges and opportunities. Mech Syst Signal Process 23(3):724–739

    Article  Google Scholar 

  6. Isermann R (1997) Supervision, fault-detection and fault-diagnosis methods—an introduction. Control Eng Pract 5:639–652

    Article  Google Scholar 

  7. Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510

    Article  Google Scholar 

  8. Karnopp D, Margolis D, Rosenberg R (2006) Systems dynamics: modeling and simulation of mechatronic systems, 2nd edn. Wiley, New York

    Google Scholar 

  9. Kothamasu R, Huang SH, Verduin WH (2006) System health monitoring and prognostics—a review of current paradigms and practices. Int J Adv Manuf Tech 28:1012–1024

    Article  Google Scholar 

  10. Lebold M, Thurston M (2001) Open standards for condition-based maintenance and prognostic systems. In: Proceedings 5th maintenance and reliability conference (MARCON)

  11. Luo J, Pattipati KR, Qiao L, Chigusa S (2003) Model-based prognostic techniques applied to a suspension system. Trans Syst Man Cybern 38:1156–1168

    Google Scholar 

  12. Medjaher K, Tobon-Mejia DA, Zerhouni N (2012) Remaining useful life estimation of critical components with application to bearings. IEEE Trans Reliab 61(2):292–302

    Article  Google Scholar 

  13. Merzouki R, Medjaher K, Djeziri MA, Ould-Bouamama B (2007) Backlash fault detection in mechatronic system. Mechatronics 17:299–310

    Article  Google Scholar 

  14. Ould Bouamama B, Medjaher K, Samantaray AK, Staroswiecki M (2006) Supervision of an industrial steam generator. Part I: bond graph modelling. Control Eng Pract 14(1):71–83

    Article  Google Scholar 

  15. Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Tech 50:297–313

    Article  Google Scholar 

  16. Samantaray AK, Ould Bouamama B (2008) Model-based process supervision: a bond graph approach. Springer, London

    Google Scholar 

  17. Sikorska J, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25:1803–1836

    Article  Google Scholar 

  18. Tobon-Mejia DA, Medjaher K, Zerhouni N (2012) CNC machine tool’s wear diagnostic and prognostic by using dynamic bayesian networks. Mech Syst Signal Process 28:167–182

    Article  Google Scholar 

  19. Venkatasubramanian V (2005) Prognostic and diagnostic monitoring of complex systems for product lifecycle management: challenges and opportunities. Comput Chem Eng 29(6):1253–1263

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Automatic Control and Micro-Mechatronic Systems Department, FEMTO-ST Institute, UMR CNRS 6174—UFC/ENSMM/UTBM, 24, rue Alain Savary, 25000, Besançon, France

    K. Medjaher & N. Zerhouni

Authors
  1. K. Medjaher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Zerhouni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Medjaher.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Medjaher, K., Zerhouni, N. Hybrid prognostic method applied to mechatronic systems. Int J Adv Manuf Technol 69, 823–834 (2013). https://doi.org/10.1007/s00170-013-5064-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-013-5064-0

Keywords

Search

Navigation