Models for Perfect Repair

  • M. Luz Gámiz
  • K. B. Kulasekera
  • Nikolaos Limnios
  • Bo Henry Lindqvist
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


In this chapter, we begin with the study of repairable systems. Specifically, we study probabilistic models for systems which after failure are replaced by a new one exactly. We say then that the system operating state is restored to “as good as new” conditions after failure. In the first approach to this kind of systems, we assume that the system is repaired (replaced) and put into new operation immediately after the failure. The sample information that we analyze consist of a sequence of random variables independent and identically distributed, which represent the time between two consecutive failures. The model that we consider in this situation is a Renewal Process, and the main purpose is to present and compare several ways of nonparametrically estimate the renewal function, that is, the expected number of failures occurring in the system up to a given time t. When the repair times are of interest, the data collected consist of a sequence of alternating up and down periods. The modeling tool indicated in this case is the Alternating Renewal Process, and we will concentrate on estimating the availability function (the probability that the system is functioning at a given time) using nonparametric techniques.


Repairable System Kernel Estimator Repair Time Empirical Distribution Function Availability Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Section 2.3 of this chapter is an extension into book-length form of the article Nonparametric estimation of the availability in a general repairable system, originally published in Reliability Engineering and System Safety93 (8), 1188–11962 (2008).

The authors express their full acknowledgement of the original publication of the paper in the journal cited above, edited by Elsevier.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • M. Luz Gámiz
    • 1
  • K. B. Kulasekera
    • 2
  • Nikolaos Limnios
    • 3
  • Bo Henry Lindqvist
    • 4
  1. 1.Facultad Ciencias, Depto. Estadistica e Investigacion OperativaUniversidad GranadaGranadaSpain
  2. 2.Department of Mathematical SciencesClemson UniversityClemsonUSA
  3. 3.Centre de Recherches de Royallieu, Laboratoire de Mathématiques AppliquéesUniversité de Technologie de CompiègneCompiègneFrance
  4. 4.Department of Mathematical SciencesNorwegian University of Science and TechnologyTrondheimNorway

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