Search

Your search keyword '"Moura, Márcio C."' showing total 13 results
Start Over Author "Moura, Márcio C."
13 results on '"Moura, Márcio C."'

11. Stress-Strength Reliability Analysis with Extreme Values based on q-Exponential Distribution.

Author

Sales Filho, Romero L. M., López Droguett, Enrique, Lins, Isis D., Moura, Márcio C., Amiri, Mehdi, and Azevedo, Rafael Valença

Subjects
EXTREME value theory, EXPONENTIAL functions, RELIABILITY in engineering, MAXIMUM likelihood statistics, STATISTICAL bootstrapping
Abstract

When dealing with practical problems of stress-strength reliability, one can work with fatigue life data and make use of the well-known relation between stress and cycles until failure. For some materials, this kind of data can involve extremely large values. In this context, this paper discusses the problem of estimating the reliability index R = P( Y < X) for stress-strength reliability, where stress Y and strength X are independent q-exponential random variables. This choice is based on the q-exponential distribution's capability to model data with extremely large values. We develop the maximum likelihood estimator for the index R and analyze its behavior by means of simulated experiments. Moreover, confidence intervals are developed based on parametric and nonparametric bootstrap. The proposed approach is applied to two case studies involving experimental data: The first one is related to the analysis of high-cycle fatigue of ductile cast iron, whereas the second one evaluates the specimen size effects on gigacycle fatigue properties of high-strength steel. The adequacy of the q-exponential distribution for both case studies and the point and interval estimates based on maximum likelihood estimator of the index R are provided. A comparison between the q-exponential and both Weibull and exponential distributions shows that the q-exponential distribution presents better results for fitting both stress and strength experimental data as well as for the estimated R index. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

13. Variable selection and uncertainty analysis of scale growth rate under pre-salt oil wells conditions using support vector regression

Author

Droguett, Enrique L, Lins, Isis D, Moura, Márcio C, Zio, Enrico, and Jacinto, Carlos M

Abstract

The formation of inorganic scale, particularly calcium carbonate (CaCO3), is a persistent and one of the most serious and costly problems in the oil and gas industries. This event may cause partial to complete plugging, block valves, tubing and flowlines, and then reduce the production rates. This article proposes the use of support vector regression to build a nonlinear mapping between a set of variables (surface cladding, material, temperature, pressure, brine composition, and fluid velocity) and the scale build-up. The support vector regression is fed with data gathered from laboratory tests carried out on coupons that simulate realistic downhole conditions encountered in oil well bores from the pre-salt fields in Brazil. The proposed failure prediction framework is comprehensive as it entails the stages of hyperparameter tuning, variable selection, and uncertainty analysis, which are addressed by a combination of particle swarm optimization and bootstrap with support vector regression. The obtained results suggest that the bootstrapped particle swarm optimization + support vector regression is a valuable tool that may be used to support condition-based maintenance-related decisions.

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results