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Improved objective Bayesian estimator for a PLP model hierarchically represented subject to competing risks under minimal repair regime
- Source :
- PLoS ONE, Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual), Universidade de São Paulo (USP), instacron:USP, PLoS ONE, Vol 16, Iss 8, p e0255944 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- 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.
- Subjects :
- Epidemiology
Computer science
Monte Carlo method
Reliability Engineering
Medicine and Health Sciences
Statistical inference
Annelids
Materials
Multidisciplinary
Statistical Models
Simulation and Modeling
FALHA
Statistics
Eukaryota
Robotics
Reliability
Petroleum
Physical Sciences
Medicine
Engineering and Technology
Organic Materials
Robots
Algorithms
Research Article
Mathematical optimization
Science
Reliability (computer networking)
Materials Science
Bayesian probability
Oil and Gas Industry
Research and Analysis Methods
Industrial Engineering
Animals
Models, Statistical
business.industry
Mechanical Engineering
Organisms
Biology and Life Sciences
Bayes Theorem
Statistical model
Invertebrates
Medical Risk Factors
Robot
Artificial intelligence
business
Zoology
Failure mode and effects analysis
Mathematics
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....357bda5024d80c0ec8a0bb98a10dc464