Análise clássica e bayesiana do modelo Weibull modificado generalizado

Pós-graduação em Matematica Aplicada e Computacional - FCT

Análise estatística da vida em fadiga de compósitos estruturais pela distribuição de Weibull

Nowadays technological trend is based on finding materials that could support low weight with satisfactory mechanical properties and for this reason composite material became a very attractive topic in research projects all over the world. Due to its heterogenic properties, this type of material shows scatter in mechanical test results, especially in cyclic loading. Therefore it is important to predict its fatigue strength behaviour by statistic analysis, once fatigue causes approximately 90% of the failure in structural components. The present work aimed to investigate the fatigue behaviour of the Twill/Cycom 890 composite, which is carbon fiber reinforced with polymeric resin as matrix and manufactured via RTM process (Resin Transfer Molding). All samples were tested in different tensile level in triplicate in order to associate these values. The statistical analysis was conducted with Two-Parameter Weibull Distribution and then evaluated the fatigue life results for the composite. Weibull graphics were used to determine the scale and shape parameters. The S-N curve for the Twill/Cycom composite was drawn and indicated the number of cycles to occur the first damages in this material. The probability of failure was associated with material reliability, as shown in graphics for the different tensile levels and fatigue life. In addition, the laminate was evaluated by ultrasonic inspection showing a regular impregnation. The fractographic analysis conducted by SEM showed failure mechanisms for polymeric composites associated to cyclic loadings ... (Complete abstract click electronic access below)

Modelos de fração de cura de mistura e não-mistura na distribuição Weibull Modificada Generalizada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Weibull and generalised exponential overdispersion models with an application to ozone air pollution

We consider the problem of estimating the mean and variance of the time between occurrences of an event of interest (inter-occurrences times) where some forms of dependence between two consecutive time intervals are allowed. Two basic density functions are taken into account. They are the Weibull and the generalised exponential density functions. In order to capture the dependence between two consecutive inter-occurrences times, we assume that either the shape and/or the scale parameters of the two density functions are given by auto-regressive models. The expressions for the mean and variance of the inter-occurrences times are presented. The models are applied to the ozone data from two regions of Mexico City. The estimation of the parameters is performed using a Bayesian point of view via Markov chain Monte Carlo (MCMC) methods.

INFERENCES FOR THE CHANGE-POINT OF THE EXPONENTIATED WEIBULL HAZARD FUNCTION

In many applications of lifetime data analysis, it is important to perform inferences about the change-point of the hazard function. The change-point could be a maximum for unimodal hazard functions or a minimum for bathtub forms of hazard functions and is usually of great interest in medical or industrial applications. For lifetime distributions where this change-point of the hazard function can be analytically calculated, its maximum likelihood estimator is easily obtained from the invariance properties of the maximum likelihood estimators. From the asymptotical normality of the maximum likelihood estimators, confidence intervals can also be obtained. Considering the exponentiated Weibull distribution for the lifetime data, we have different forms for the hazard function: constant, increasing, unimodal, decreasing or bathtub forms. This model gives great flexibility of fit, but we do not have analytic expressions for the change-point of the hazard function. In this way, we consider the use of Markov Chain Monte Carlo methods to get posterior summaries for the change-point of the hazard function considering the exponentiated Weibull distribution.

The negative binomial-beta Weibull regression model to predict the cure of prostate cancer

In this article, for the first time, we propose the negative binomial-beta Weibull (BW) regression model for studying the recurrence of prostate cancer and to predict the cure fraction for patients with clinically localized prostate cancer treated by open radical prostatectomy. The cure model considers that a fraction of the survivors are cured of the disease. The survival function for the population of patients can be modeled by a cure parametric model using the BW distribution. We derive an explicit expansion for the moments of the recurrence time distribution for the uncured individuals. The proposed distribution can be used to model survival data when the hazard rate function is increasing, decreasing, unimodal and bathtub shaped. Another advantage is that the proposed model includes as special sub-models some of the well-known cure rate models discussed in the literature. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We analyze a real data set for localized prostate cancer patients after open radical prostatectomy.

Simulation study of joint transition and Weibull survival model with shared parameters

This study investigates a theoretical model where a longitudinal process, that is a stationary Markov-Chain, and a Weibull survival process share a bivariate random effect. Furthermore, a Quality-of-Life adjusted survival is calculated as the weighted sum of survival time. Theoretical values of population mean adjusted survival of the described model are computed numerically. The parameters of the bivariate random effect do significantly affect theoretical values of population mean. Maximum-Likelihood and Bayesian methods are applied on simulated data to estimate the model parameters. Based on the parameter estimates, predicated population mean adjusted survival can then be calculated numerically and compared with the theoretical values. Bayesian method and Maximum-Likelihood method provide parameter estimations and population mean prediction with comparable accuracy; however Bayesian method suffers from poor convergence due to autocorrelation and inter-variable correlation. ^

Optimum adhesive thickness in structural adhesives joints using statistical techniques base on Weibull distribution

The geometrical factors defining an adhesive joint are of great importance as its design greatly conditions the performance of the bonding. One of the most relevant geometrical factors is the thickness of the adhesive as it decisively influences the mechanical properties of the bonding and has a clear economic impact on the manufacturing processes or long runs. The traditional mechanical joints (riveting, welding, etc.) are characterised by a predictable performance, and are very reliable in service conditions. Thus, structural adhesive joints will only be selected in industrial applications demanding mechanical requirements and adverse environmental conditions if the suitable reliability (the same or higher than the mechanical joints) is guaranteed. For this purpose, the objective of this paper is to analyse the influence of the adhesive thickness on the mechanical behaviour of the joint and, by means of a statistical analysis based on Weibull distribution, propose the optimum thickness for the adhesive combining the best mechanical performance and high reliability. This procedure, which is applicable without a great deal of difficulty to other joints and adhesives, provides a general use for a more reliable use of adhesive bondings and, therefore, for a better and wider use in the industrial manufacturing processes.

Improved estimation of Weibull Parameters considering unreliability uncertainties

We propose a linear regression method for estimating Weibull parameters from life tests. The method uses stochastic models of the unreliability at each failure instant. As a result, a heteroscedastic regression problem arises that is solved by weighted least squares minimization. The main feature of our method is an innovative s-normalization of the failure data models, to obtain analytic expressions of centers and weights for the regression. The method has been Monte Carlo contrasted with Benard?s approximation, and Maximum Likelihood Estimation; and it has the highest global scores for its robustness, and performance.

Fracture strength of drilled wafers: study of the stress concentration factor and determination of the weibull distribution of the fracture stress

In the photovoltaic field, the back contact solar cells technology has appeared as an alternative to the traditional silicon modules. This new type of cells places both positive and negative contacts on the back side of the cells maximizing the exposed surface to the light and making easier the interconnection of the cells in the module. The Emitter Wrap-Through solar cell structure presents thousands of tiny holes to wrap the emitter from the front surface to the rear surface. These holes are made in a first step over the silicon wafers by means of a laser drilling process. This step is quite harmful from a mechanical point of view since holes act as stress concentrators leading to a reduction in the strength of these wafers. This paper presents the results of the strength characterization of drilled wafers. The study is carried out testing the samples with the ring on ring device. Finite Element models are developed to simulate the tests. The stress concentration factor of the drilled wafers under this load conditions is determined from the FE analysis. Moreover, the material strength is characterized fitting the fracture stress of the samples to a three-parameter Weibull cumulative distribution function. The parameters obtained are compared with the ones obtained in the analysis of a set of samples without holes to validate the method employed for the study of the strength of silicon drilled wafers.