Reliability data analysis of systems in the wear-out phase using a (corrected) q-Exponential likelihood
Author
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Souza Vidal de Negreiros, Ana Cláudia
Author
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Didier Lins, Isis
Author
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das Chagas Moura, Márcio José
Author
dc.contributor.author
López Droguett, Enrique
Admission date
dc.date.accessioned
2020-05-27T13:06:53Z
Available date
dc.date.available
2020-05-27T13:06:53Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Reliability Engineering and System Safety.Vol.197: (2020): 106787
es_ES
Identifier
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10.1016/j.ress.2019.106787
Identifier
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https://repositorio.uchile.cl/handle/2250/174989
Abstract
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Maintenance-related decisions are often based on the expected number of interventions during a specified period of time. The proper estimation of this quantity relies on the choice of the probabilistic model that best fits reliability-related data. In this context, the q-Exponential probability distribution has emerged as a promising alternative. It can model each of the three phases of the bathtub curve; however, for the wear-out phase, its usage may become difficult due to the "monotone likelihood problem". Two correction methods (Firth's and resample-based) are considered and have their performances evaluated through numerical experiments. To aid the reliability analyst in applying the q-Exponential model, we devise a methodology involving original and corrected functions for point and interval estimates for the q-Exponential parameters and validation of the estimated models using the expected number of failures via Monte Carlo simulation and the bootstrapped Kolmogorov-Smirnov test. Two examples with failure data presenting increasing hazard rates are provided. The performances of the estimated q-Exponential, Weibull, q-Weibull and modified extended Weibull (MEW) models are compared. In both examples, the q-Exponential presented superior results, despite the increased flexibility of the q-Weibull and MEW distributions in modeling non-monotone hazard rates (e.g., bathtub-shaped).
es_ES
Patrocinador
dc.description.sponsorship
CAPES 001
National Council for Scientific and Technological Development (CNPq)