Complex Engineering Service Systems pp 297-314 | Cite as

# Component Level Replacements: Estimating Remaining Useful Life

## Abstract

Condition based maintenance modelling can be used to maximise the availability of key operational components that are subject to condition monitoring processes, such as vibration or oil based monitoring and thermography. This chapter addresses the operational need for a component replacement decision analysis problem utilising available condition monitoring data and incorporating various prognostic modelling options for the estimation of remaining useful life which is essential in a prognostics model. Guidelines are presented in the chapter which enable the selection of an appropriate prognostic model for a given application based on the characteristics of the scenario and the availability of historical data to train the model. Consideration is then given to scenarios where historical data are scarce or unavailable and new modelling developments are presented to cater for this contingency. The objective of the modeling process is to maximise availability whilst avoiding the occurrence of costly component failures. The developed model has been programmed into a prototype software package for facilitating the implementation of the methodology.

## Keywords

Failure Mode Condition Monitoring Failure Time Residual Life Failure Zone## References

- C. Bunks, D. Mccarthy, Condition-based maintenance of machines using hidden Markov models. Mech. Syst. Signal Process
**14**(4), 597–612 (2000)CrossRefGoogle Scholar - M.J. Carr, W. Wang, A case comparison of a proportional hazards model and a stochastic filter for condition-based maintenance applications using oil-based condition monitoring information. Proc. Inst. Mech. Eng. Part O J. Risk Reliab.
**222**, 47–55 (2008)Google Scholar - M.J. Carr, W. Wang,
*Failure mode analysis and residual life prediction for condition based maintenance applications*. IEEE Trans. Reliab. (forthcoming) (2010a)Google Scholar - M.J. Carr, W Wang,
*An approximate algorithm for CBM applications*. Under review (2010b)Google Scholar - R.A. Collacott,
*Mechanical fault diagnosis and condition monitoring*(Chapman & Hall, London, 1997)Google Scholar - A.K.S. Jardine, V. Makis, D. Banjevic, D. Braticevic, M. Ennis, A decision optimisation model for condition-based maintenance. J. Qual. Main. Eng.
**4**(2), 115–121 (1998)CrossRefGoogle Scholar - A.K.S. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal. Process.
**20**(7), 1483–1510 (2005)CrossRefGoogle Scholar - J.D. Kalbfleisch, R.L. Prentice,
*The statistical analysis of failure time data*(Wiley, New York, 1980)MATHGoogle Scholar - D. Kumar, U. Westberg, Maintenance scheduling under age replacement policy using proportional hazard modelling and total-time-on-testing plotting. Eur. J. Oper. Res.
**99**, 507–515 (1997)MATHCrossRefGoogle Scholar - D. Lin, V. Makis, Recursive filters for a partially observable system subject to random failure. Adv. Appl. Probab.
**35**, 207–227 (2003)MATHCrossRefMathSciNetGoogle Scholar - V. Makis, A.K.S. Jardine, Optimal replacement in the proportional hazards model. INFOR
**30**, 172–183 (1991)Google Scholar - S. Ross,
*Stochastic processes*(Wiley, New York, 1996)MATHGoogle Scholar - W. Wang, A model to predict the residual life of rolling element bearings given monitored condition information to date. IMA J. Manag. Math.
**13**, 3–16 (2002)MATHCrossRefMathSciNetGoogle Scholar - W. Wang, Modelling the probability assessment of the system state using available condition information. IMA. J. Manag. Math.
**17**(3), 225–234 (2006)MATHCrossRefGoogle Scholar - W. Wang, M.J. Carr, An adaptive Brownian model for plant residual life prediction. Proceedings of the 2010PHM Conference, Macau (2010)Google Scholar
- W. Wang, A.H. Christer, Towards a general condition based maintenance model for a stochastic dynamic system. J. Oper. Res. Soc.
**51**, 145–155 (2000)MATHGoogle Scholar