Predição em modelos de tempo de falha acelerado com efeito aleatório para avaliação de riscos de falha em poços petrolíferos
We considered prediction techniques based on models of accelerated failure time with random e ects for correlated survival data. Besides the bayesian approach through empirical Bayes estimator, we also discussed about the use of a classical predictor, the Empirical Best Linear Unbiased Predictor...
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| Formato: | Dissertação |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/18635 |
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| Resumo: | We considered prediction techniques based on models of accelerated failure time with
random e ects for correlated survival data. Besides the bayesian approach through empirical
Bayes estimator, we also discussed about the use of a classical predictor, the Empirical
Best Linear Unbiased Predictor (EBLUP). In order to illustrate the use of these predictors,
we considered applications on a real data set coming from the oil industry. More speci -
cally, the data set involves the mean time between failure of petroleum-well equipments of
the Bacia Potiguar. The goal of this study is to predict the risk/probability of failure in
order to help a preventive maintenance program. The results show that both methods are
suitable to predict future failures, providing good decisions in relation to employment and
economy of resources for preventive maintenance. |
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