Open AccessImproved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime
Author(s) -
Francisco Louzada,
José Alberto Cuminato,
Oscar Maurício Hernandez Rodriguez,
Vera Tomazella,
Paulo Ferreira,
Pedro Ramos,
Eder Ângelo Milani,
Gustavo Bochio,
Ivan C. Perissini,
Oilson Alberto Gonzatto,
Ali Mota,
Luis Felipe Acuña Alegría,
Danilo Colombo,
Eduardo André Perondi,
André Viegas Wentz,
Anselmo Luis Silva Júnior,
Dante Augusto Couto Barone,
Hélio Santos,
Marcus V. C. Magalhães
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0255944
Subject(s) - bayesian probability , computer science , bayesian inference , inference , statistical model , statistical inference , estimator , subject (documents) , operations research , machine learning , artificial intelligence , mathematics , statistics , library science
In this paper, we propose a hierarchical statistical model for a single repairable system subject to several failure modes (competing risks). The paper describes how complex engineered systems may be modelled hierarchically by use of Bayesian methods. It is also assumed that repairs are minimal and each failure mode has a power-law intensity. Our proposed model generalizes another one already presented in the literature and continues the study initiated by us in another published paper. Some properties of the new model are discussed. We conduct statistical inference under an objective Bayesian framework. A simulation study is carried out to investigate the efficiency of the proposed methods. Finally, our methodology is illustrated by two practical situations currently addressed in a project under development arising from a partnership between Petrobras and six research institutes.