Browsing by Author "Gonzatto Junior, Oilson A."

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    Frailty model for multiple repairable systems hierarchically represented subject to competing risks
    (2024) Gonzatto Junior, Oilson A.; Fernandes, Willian R.; Ramos, Pedro L.; Tomazella, Vera L. D.; Louzada, Francisco
    In this paper, we propose a statistical model to describe the behaviour of failure times associated with groups of repairable systems hierarchically represented, under a competing risks framework, considering the existence of unobserved heterogeneity that acts individually on systems of each group, as well as the possibility of imperfect repairs whose initial failure rate is in the form of the power law. In this context for the unobserved heterogeneity in the groups, we consider the multiplicative frailty. To illustrate the use of the proposed model, we consider a database with the failures of 38 agricultural machines categorized into five different groups. We understand that the tractor fleet corresponds to the farm's agricultural system, therefore the need for intervention in this system occurs with the failure of any unit, individually, in a serial structure, of competing risks.e On the other hand, the understanding of the time in which all the machinery that makes up the fleet will have required some intervention is obtained by analysing the results under a parallel structure.
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    Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime
    (2021) Louzada, Francisco; Cuminato, Jose A.; Rodriguez, Oscar M. H.; Tomazella, Vera L. D.; Ferreira, Paulo H.; Ramos, Pedro L.; Milani, Eder A.; Bochio, Gustavo; Perissini, Ivan C.; Gonzatto Junior, Oilson A.; Mota, Alex L.; Alegria, Luis F. A.; Colombo, Danilo; Perondi, Eduardo A.; Wentz, Andre V.; Junior, Anselmo L. Silva; Barone, Dante A. C.; Santos, Hugo F. L.; Magalhaes, Marcus V. C.
    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.