The identifiability problem for repairable systems subject to competing risks

• Published in: Advances in applied probability 2004-09, Vol.36 (3), p.774-790
• Main Authors: Bedford, Tim, Lindqvist, Bo H.
• Format: Article
• Language: English
• Subjects: 37A30; 60G35; 60J27; 60K10; 90B25; Borel sets; Competing risks; Epics; Ergodic theory; ergodicity; Failure; General Applied Probability; Identifiability; joint survival distribution; marked point process; Markov analysis; Markov chain; Markov chains; Parametric models; Poisson process; Probability; Probability distributions; Reliability; Reliability functions; Remarriage; Studies; System failures
• ISSN: 0001-8678; 1475-6064
• DOI: 10.1239/aap/1093962233

Within reliability theory, identifiability problems arise through competing risks. If we have a series system of several components, and if that system is replaced or repaired to as good as new on failure, then the different component failures represent competing risks for the system. It is well known that the underlying component failure distributions cannot be estimated from the observable data (failure time and identity of failed component) without nontestable assumptions such as independence. In practice many systems are not subject to the ‘as good as new’ repair regime. Hence, the objective of this paper is to contrast the identifiability issues arising for different repair regimes. We consider the problem of identifying a model within a given class of probabilistic models for the system. Different models corresponding to different repair strategies are considered: a partial-repair model, where only the failing component is repaired; perfect repair, where all components are as good as new after a failure; and minimal repair, where components are only minimally repaired at failures. We show that on the basis of observing a single socket, the partial-repair model is identifiable, while the perfect- and minimal-repair models are not.