Skip to main content
Log in

Remaining useful life in theory and practice

  • Published:
Metrika Aims and scope Submit manuscript

Abstract

Remaining useful life (RUL) is nowadays in fashion, both in theory and applications. Engineers use it mostly when they have to decide whether to do maintenance, or to delay it, due to production requirements. Most often, it is assumed that in later life of an equipment (in wear-out period), the hazard function is increasing, and then the expected RUL, μ(t), is decreasing. We noticed that the standard deviation of RUL, σ(t), is also decreasing, which was expected and known, but that the ratio σ(t)/μ(t) is also increasing, which was a surprise. Initiated by this observation, we have proved that under some general conditions, which include Weibull distribution with shape parameter  > 1, this is indeed the case. Even more, we have proved that the limiting distribution of standardized RUL is exponential, so that the variability of RUL is relatively large. The role of condition monitoring in the evaluation of RUL is discussed. Various models for RUL depending on covariates are considered.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Balkema AA, De Haan L (1974) Residual life time at great age. Ann Probab 2: 792–804

    Article  MATH  MathSciNet  Google Scholar 

  • Banjevic D, Jardine AKS (2006). Calculation of reliability function and remaining useful life for a Markov failure time process. IMA J Mngt Math 17: 115–130

    MATH  MathSciNet  Google Scholar 

  • Bradley D, Gupta R (2003) Limiting behaviour of the mean residual life. Ann I Stat Math 55: 217–226

    MATH  MathSciNet  Google Scholar 

  • Chen YQ, Jewell NP, Lei X, Cheng SC (2005) Semiparametric estimation of proportional mean residual life model in presence of censoring. Biometrics 61: 170–178

    Article  MATH  MathSciNet  Google Scholar 

  • Elsayed E (2003) Mean residual life and optimal operating conditions for industrial furnace tubes. In: Blischke WR, Murthy DNP (eds) Case studies in reliability and maintenance. Wiley, Hoboken

    Google Scholar 

  • Guess F, Proschan F (1988) Mean residual life: theory and applications. In: Krishnaiah PR, Rao CR Handbook of statistics, vol 7. North-Holland, Amsterdam

  • Gupta R (1987) On the monotonic properties of the residual variance and their applications in reliability. J Stat Plan Infer 16: 329–335

    Article  MATH  Google Scholar 

  • Jewell NP, Nielsen JP (1993) A framework for consistent prediction rules based on markers. Biometrika 80: 153–164

    Article  MATH  MathSciNet  Google Scholar 

  • Kalpakam S (1993) On the quasi-stationary distribution of the residual lifetime. IEEE T Reliab 42: 623–624

    Article  MATH  Google Scholar 

  • Li W, Cao J (1993) The limiting distribution of the residual lifetime of a Markov repairable system. Reliab Eng Syst Safe 41: 103–105

    Article  Google Scholar 

  • Lugtigheid D, Banjevic D, Jardine AKS (2004) Modelling repairable system reliability with explanatory variables and repair and maintenance actions. IMA J Mngt Math 15: 89–110

    MATH  MathSciNet  Google Scholar 

  • Maguluri M, Zhang C (1994) Estimation in the mean residual life regression model. J R Stat Soc B 56: 477–489

    MATH  MathSciNet  Google Scholar 

  • Meilijson I (1972) Limiting propeties of the mean residual lifetime function. Ann Math Stat 43: 354–357

    Article  MATH  MathSciNet  Google Scholar 

  • Müller H, Zhang Y (2005) Time-varying functional regression for predicting remaining lifetime distributions from longitudinal trajectories. Biometrics 61: 1064–1075

    Article  MATH  MathSciNet  Google Scholar 

  • Muth E (1977) Reliability models with positive memory derived from the mean residual life function. In: Tsokos CP, Shimi IM The theory and applications of reliability, vol 2. Academic Press, New York

  • Myötyri E, Pulkkinen U, Simola K (2006) Application of stochastic filtering for lifetime prediction. Reliab Eng Syst Safe 91: 200–208

    Article  Google Scholar 

  • Reinertsen R (1996) Residual life of technical systems; diagnosis, prediction and life extension. Reliab Eng Syst Safe 54: 23–34

    Article  Google Scholar 

  • Sen PK (2004) HRQoL and concomitant adjusted mean residual life analysis. In: Nikulin MS, Balakrishnan N, Mesbah M, Limnios N Parametric and semiparametric models with applications to reliability, survival analysis and quality of life. Birkhauser, Boston

  • Siddiqui MM, Çağlar M (1994) Residual lifetime distribution and its applications. Microelectron Reliab 34: 211–227

    Article  Google Scholar 

  • Van Houwelingen HC (2007) Dynamic prediction by landmarking in event history analysis. Scand J Stat 34: 70–85

    Article  MATH  MathSciNet  Google Scholar 

  • Yuen KC, Zhu LX, Tang NY (2003) On the mean residual life regression model. J Stat Plan Infer 113: 685–698

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dragan Banjevic.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Banjevic, D. Remaining useful life in theory and practice. Metrika 69, 337–349 (2009). https://doi.org/10.1007/s00184-008-0220-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00184-008-0220-5

Keywords

Navigation