Repairable system analysis in presence of covariates and random effects (Articolo in rivista)

Type
Label
  • Repairable system analysis in presence of covariates and random effects (Articolo in rivista) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.ress.2014.04.009 (literal)
Alternative label
  • Giorgio M. 1, Guida M. 2-3, Pulcini G. 3 (2014)
    Repairable system analysis in presence of covariates and random effects
    in Reliability engineering & systems safety
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Giorgio M. 1, Guida M. 2-3, Pulcini G. 3 (literal)
Pagina inizio
  • 271 (literal)
Pagina fine
  • 281 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.sciencedirect.com/science/article/pii/S0951832014000775 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 131 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 11 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1) Seconda Università di Napoli, Aversa (CE); 2) Università di Salerno, Fisciano (SA); 3) Istituto Motori, CNR, Napoli. (literal)
Titolo
  • Repairable system analysis in presence of covariates and random effects (literal)
Abstract
  • This paper aims to model the failure pattern of repairable systems in presence of explained and unex-plained heterogeneity. The failure pattern of each system is described by a Power Law Process. Part of the heterogeneity among the patterns is explained through the use of a covariate, and the residual unexplained heterogeneity (random effects) is modeled via a joint probability distribution on the PLP parameters. The proposed approach is applied to a real set of failure time data of powertrain systems mounted on 33 buses employed in urban and suburban routes. Moreover, the joint probability distri-bution on the PLP parameters estimated from the data is used as an informative prior to make Bayes-ian inference on the future failure process of a generic system belonging to the same population and employed in an urban or suburban route under randomly chosen working conditions. (literal)
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