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System Degradation and Maintenance

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Extended Warranties, Maintenance Service and Lease Contracts

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

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Abstract

Every system (product, plant or infrastructure) is unreliable in the sense that it degrades and eventually fails. Maintenance is needed to compensate for this unreliability. Any decision-making with respect to maintenance requires a proper understanding of the degradation processes over time and the actions of maintenance from a system life cycle perspective.

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Notes

  1. 1.

    The number of stages in the PLC can vary. For more on this, see Murthy et al. (2008).

  2. 2.

    The Front End stage is also often referred to as the Feasibility stage.

  3. 3.

    See Appendix A for a definition of a random variable and an introduction to probability theory.

  4. 4.

    The numbering of states is arbitrary. One can easily reverse the order so that the lower the state the greater the degradation.

  5. 5.

    In some cases X(t) could be non-increasing with lower values corresponding to greater degradation. In this situation the curve in Fig. 2.5 would be downward sloping.

  6. 6.

    The extension of FTA to the case where the performance is based on Characterisation 2 is more complex. For further details see, Blischke and Murthy (2000) or Rausand and Høyland (2004).

  7. 7.

    The material for the remainder of this section is based on Worm and van Harten (1996). For other issues relating to road maintenance, can be found in Dekker et al. (1998) and Rose and Bennett (1992).

  8. 8.

    Compacted broken stone usually bound with tar or asphalt (also referred to as bitumen).

  9. 9.

    θ is the set of parameters for the reliability function. Often we will suppress this and use R(t) instead of \( R(t;\theta ) \) for notational ease. F(t) = 1 – R(t) is called the failure distribution function and characterises the time to first failure (a random variable).

  10. 10.

    The details can be found in many books on reliability; see, for example, Blischke and Murthy (2000) and Rausand and Høyland (2004).

  11. 11.

    This follows from simple argument based on conditional probability (see Appendix A).

  12. 12.

    Typically, the time taken to repair or replace a failed item is often very much smaller than the time between failures (in a statistical sense) so that one can ignore repair times and treat the repairs as being instantaneous for the purpose of modelling of failures over time. This is discussed in Chap. 3.

  13. 13.

    The first two types of PM are also referred to as Time-based maintenance (TBM).

  14. 14.

    Here the subscripts refer to the number of times an item has been subjected to overhauls and should not be confused with the notation in Sect. 2.4.1 where it refers to reliability of different components.

  15. 15.

    Nakagawa (2005) deals with the modelling and analysis of several maintenance policies.

  16. 16.

    For more information, see Cassady et al. (1998).

  17. 17.

    Type I and Type II policies are special cases of Type III policies.

  18. 18.

    In a 2009 report released by the American Association of State Highway and Transportation Officials (USA) about 50 % of the roads in the USA are in bad condition with urban areas worse.

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Authors and Affiliations

  1. School of Mechanical and Mining Engineering, University of Queensland, Brisbane, Australia

    D. N. P. Murthy

  2. Dundee Business School, University of Abertay Dundee, Dundee, UK

    Nat Jack

Authors
  1. D. N. P. Murthy
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  2. Nat Jack
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Correspondence to D. N. P. Murthy .

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Murthy, D.N.P., Jack, N. (2014). System Degradation and Maintenance. In: Extended Warranties, Maintenance Service and Lease Contracts. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-6440-1_2

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  • DOI: https://doi.org/10.1007/978-1-4471-6440-1_2

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6439-5

  • Online ISBN: 978-1-4471-6440-1

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