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Imperfect maintenance in a generalized competing risks framework

Part of: Stochastic processes Survival analysis and censored data Operations research and management science Special processes

Published online by Cambridge University Press:  14 July 2016

Laurent Doyen  and
Olivier Gaudoin
Laurent Doyen*
Affiliation:
Université Pierre Mendès France Grenoble 2
Olivier Gaudoin*
Affiliation:
Institut National Polytechnique de Grenoble
*
∗Postal address: Laboratoire LABSAD, Université Pierre Mendès France Grenoble 2, BP 47-38 040 Grenoble Cedex 9, France. Email address: [email protected]
∗∗Postal address: Laboratoire LMC, Institut National Polytechnique de Grenoble, BP 53-38 041 Grenoble Cedex 9, France. Email address: [email protected]
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Abstract

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In this paper we present a general framework for the modelling of the process of corrective and condition-based preventive maintenance actions for complex repairable systems. A new class of models is proposed, the generalized virtual age models. On the one hand, these models generalize Kijima's virtual age models to the case where both preventive and corrective maintenances are present. On the other hand, they generalize the usual competing risks models to imperfect maintenance actions which do not renew the system. A generalized virtual age model is defined by both a sequence of effective ages which characterizes the effects of both types of maintenance according to a classical virtual age model, and a usual competing risks model which characterizes the dependency between the two types of maintenance. Several particular cases of the general model are derived.

Keywords

Point processreliabilityimperfect maintenancecompeting risksvirtual age

MSC classification

Primary: 60K10: Applications (reliability, demand theory, etc.) 60G55: Point processes
Secondary: 90B25: Reliability, availability, maintenance, inspection 62N05: Reliability and life testing
Type
Research Article
Information
Journal of Applied Probability , Volume 43 , Issue 3 , September 2006 , pp. 825 - 839
Copyright
© Applied Probability Trust 2006 

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