Página 1 dos resultados de 216 itens digitais encontrados em 0.017 segundos

Failure analysis of low velocity impact on thin composite laminates: Experimental and numerical approaches

TITA, Volnel; CARVALHO, Jonas de; VANDEPITTE, Dirk
Fonte: ELSEVIER SCI LTD Publicador: ELSEVIER SCI LTD
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
35.78%
The dynamic behavior of composite laminates is very complex because there are many concurrent phenomena during composite laminate failure under impact load. Fiber breakage, delaminations, matrix cracking, plastic deformations due to contact and large displacements are some effects which should be considered when a structure made from composite material is impacted by a foreign object. Thus, an investigation of the low velocity impact on laminated composite thin disks of epoxy resin reinforced by carbon fiber is presented. The influence of stacking sequence and energy impact was investigated using load-time histories, displacement-time histories and energy-time histories as well as images from NDE. Indentation tests results were compared to dynamic results, verifying the inertia effects when thin composite laminate was impacted by foreign object with low velocity. Finite element analysis (FEA) was developed, using Hill`s model and material models implemented by UMAT (User Material Subroutine) into software ABAQUS (TM), in order to simulate the failure mechanisms under indentation tests. (C) 2007 Elsevier Ltd. All rights reserved.

Avaliação de modelos de falhas progressivas para estruturas em material compósito; Evaluation of progressive failure models for composite material structures

Angélico, Ricardo Afonso
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 26/03/2009 PT
Relevância na Pesquisa
35.79%
Este trabalho é uma contribuição à análise progressiva de falhas em materiais compósitos poliméricos. Esses materiais combinam as propriedades de seus constituintes (fibra, resina polimérica e interface) de forma a melhorar o desempenho frente à utilização das fases isoladamente. A combinação de fases permite obter características como baixa densidade e elevada rigidez, que são almejadas pelo segmento aeronáutico, pois podem proporcionar um aumento de autonomia ou da capacidade de carga das aeronaves. A anisotropia inerente aos compósitos torna possível projetá-los de forma a obter-se a rigidez e a resistência desejada. Por outro lado, a anisotropia dificulta a previsão precisa dos mecanismos de falha, e conseqüentemente, do comportamento global da estrutura. Apresenta-se, assim, com base numa revisão bibliográfica criteriosa, bem como, através de resultados experimentais, a avaliação de um modelo de material fenomenológico, onde se identificam modos de falhas intralaminares. Uma vez verificad a falha por algum critério, degradam-se as propriedades do material. O modelo de material foi implementado junto ao pacote de elementos finitos Abaqus através de uma sub-rotina UMAT ("User Material"), escrita em Fortran. Em seguida...

Projeto de um serviço configurável de detecção de defeitos; Design of a configurable failure detection service

Balbinot, Jeysonn Isaac
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
35.79%
A detecção de defeitos pode ser usada como base no projeto de algoritmos e aplicações distribuídas que dependem, de alguma forma, de informações de estado sobre processos distribuídos. O problema de acordo entre processos (consenso), que é um dos problemas fundamentais da computação distribuída, bem como difusão atômica (atomic broadcast), eleição de líder (leader election) e gerenciamento de grupos (membership) necessitam de informações de estado dos processos envolvidos, portanto, do resultado da atividade dos detectores. Esses protocolos, geralmente, são usados como blocos básicos para a construção de outros algoritmos, serviços ou aplicações distribuídas tolerantes a falhas. Os detectores de defeitos, de forma prática, têm sido desenvolvidos com base em parâmetros funcionais de redes locais e não operam bem no contexto de sistemas distribuídos de larga escala e de redes de longa distância (WANs). Sistemas conectados por WANs, geralmente, oferecem um ambiente mais hostil do que as LANs e clusters, devido aos atrasos longos e variáveis e à maior probabilidade de ocorrência de defeitos de temporização (flutuações na latência de comunicação) e omissão (perdas de mensagens), impondo um desafio na concepção de mecanismos que detectem defeitos de forma completa...

An energy criterion to predict delayed failure of multi-directional polymer matrix composites based on a non-linear viscoelastic model

guedes, rm
Fonte: Universidade do Porto Publicador: Universidade do Porto
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
35.87%
The life-time prediction of multi-directional polymer composite under creep loading is proposed and assessed. Energy failure criterion, based on the theoretical developments of Reiner and Weissenberg, is modified and incorporated into a computer program called LAMFLU. This algorithm was developed to predict the long-term behavior of viscoelastic polymer matrix composite laminates. In the present theoretical model, failure is part of the complete constitutive description of the material. The laminate failure prediction is based on the last ply failure; each ply failure promotes a stiffness degradation related with the failure mode. The quality of theoretical predictions is good compared with published experimental results. (C) 2004 Elsevier Ltd. All rights reserved.

Variable selection in the accelerated failure time model via the bridge method

Huang, Jian; Ma, Shuangge
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.8%
In high throughput genomic studies, an important goal is to identify a small number of genomic markers that are associated with development and progression of diseases. A representative example is microarray prognostic studies, where the goal is to identify genes whose expressions are associated with disease free or overall survival. Because of the high dimensionality of gene expression data, standard survival analysis techniques cannot be directly applied. In addition, among the thousands of genes surveyed, only a subset are disease-associated. Gene selection is needed along with estimation. In this article, we model the relationship between gene expressions and survival using the accelerated failure time (AFT) models. We use the bridge penalization for regularized estimation and gene selection. An efficient iterative computational algorithm is proposed. Tuning parameters are selected using V-fold cross validation. We use a resampling method to evaluate the prediction performance of bridge estimator and the relative stability of identified genes. We show that the proposed bridge estimator is selection consistent under appropriate conditions. Analysis of two lymphoma prognostic studies suggests that the bridge estimator can identify a small number of genes and can have better prediction performance than the Lasso.

Risk Prediction for Prostate Cancer Recurrence Through Regularized Estimation with Simultaneous Adjustment for Nonlinear Clinical Effects*

Long, Qi; Chung, Matthias; Moreno, Carlos S.; Johnson, Brent A.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/09/2011 EN
Relevância na Pesquisa
35.88%
In biomedical studies, it is of substantial interest to develop risk prediction scores using high-dimensional data such as gene expression data for clinical endpoints that are subject to censoring. In the presence of well-established clinical risk factors, investigators often prefer a procedure that also adjusts for these clinical variables. While accelerated failure time (AFT) models are a useful tool for the analysis of censored outcome data, it assumes that covariate effects on the logarithm of time-to-event are linear, which is often unrealistic in practice. We propose to build risk prediction scores through regularized rank estimation in partly linear AFT models, where high-dimensional data such as gene expression data are modeled linearly and important clinical variables are modeled nonlinearly using penalized regression splines. We show through simulation studies that our model has better operating characteristics compared to several existing models. In particular, we show that there is a non-negligible effect on prediction as well as feature selection when nonlinear clinical effects are misspecified as linear. This work is motivated by a recent prostate cancer study, where investigators collected gene expression data along with established prognostic clinical variables and the primary endpoint is time to prostate cancer recurrence. We analyzed the prostate cancer data and evaluated prediction performance of several models based on the extended c statistic for censored data...

Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling

Sinnott, Jennifer A.; Cai, Tianxi
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.85%
Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai et al., 2011). In this paper, we derive testing and prediction methods for KM regression under the accelerated failure time model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest...

Predictors of right ventricular failure after left ventricular assist device implantation

Koprivanac, Marijan; Kelava, Marta; Sirić, Franjo; Cruz, Vincent B.; Moazami, Nader; Mihaljević, Tomislav
Fonte: Croatian Medical Schools Publicador: Croatian Medical Schools
Tipo: Artigo de Revista Científica
Publicado em /12/2014 EN
Relevância na Pesquisa
35.8%
Number of left ventricular assist device (LVAD) implantations increases every year, particularly LVADs for destination therapy (DT). Right ventricular failure (RVF) has been recognized as a serious complication of LVAD implantation. Reported incidence of RVF after LVAD ranges from 6% to 44%, varying mostly due to differences in RVF definition, different types of LVADs, and differences in patient populations included in studies. RVF complicating LVAD implantation is associated with worse postoperative mortality and morbidity including worse end-organ function, longer hospital length of stay, and lower success of bridge to transplant (BTT) therapy. Importance of RVF and its predictors in a setting of LVAD implantation has been recognized early, as evidenced by abundant number of attempts to identify independent risk factors and develop RVF predictor scores with a common purpose to improve patient selection and outcomes by recognizing potential need for biventricular assist device (BiVAD) at the time of LVAD implantation. The aim of this article is to review and summarize current body of knowledge on risk factors and prediction scores of RVF after LVAD implantation. Despite abundance of studies and proposed risk scores for RVF following LVAD...

Kernel Machine Methods for Risk Prediction with High Dimensional Data

Sinnott, Jennifer Anne
Fonte: Harvard University Publicador: Harvard University
Tipo: Thesis or Dissertation
EN_US
Relevância na Pesquisa
45.83%
Understanding the relationship between genomic markers and complex disease could have a profound impact on medicine, but the large number of potential markers can make it hard to differentiate true biological signal from noise and false positive associations. A standard approach for relating genetic markers to complex disease is to test each marker for its association with disease outcome by comparing disease cases to healthy controls. It would be cost-effective to use control groups across studies of many different diseases; however, this can be problematic when the controls are genotyped on a platform different from the one used for cases. Since different platforms genotype different SNPs, imputation is needed to provide full genomic coverage, but introduces differential measurement error. In Chapter 1, we consider the effects of this differential error on association tests. We quantify the inflation in Type I Error by comparing two healthy control groups drawn from the same cohort study but genotyped on different platforms, and assess several methods for mitigating this error. Analyzing genomic data one marker at a time can effectively identify associations, but the resulting lists of significant SNPs or differentially expressed genes can be hard to interpret. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways reduces the dimensionality of the problem and could improve models by making them more biologically grounded and interpretable. The kernel machine framework has been proposed to model pathway effects because it allows nonlinear associations between the genes in a pathway and disease risk. In Chapter 2...

Customized Prediction of Short Length of Stay Following Elective Cardiac Surgery in Elderly Patients Using a Genetic Algorithm

Lee, Joon; Govindan, Sapna; Celi, Leo A.; Khabbaz, Kamal R.; Subramaniam, Balachundhar
Fonte: Harvard University Publicador: Harvard University
Tipo: Artigo de Revista Científica
EN_US
Relevância na Pesquisa
35.86%
Objective: To develop a customized short LOS (<6 days) prediction model for geriatric patients receiving cardiac surgery, using local data and a computational feature selection algorithm. Design: Utilization of a machine learning algorithm in a prospectively collected STS database consisting of patients who received cardiac surgery between January 2002 and June 2011. Setting: Urban tertiary-care center. Participants: Geriatric patients aged 70 years or older at the time of cardiac surgery. Interventions None. Measurements and Main Results Predefined morbidity and mortality events were collected from the STS database. 23 clinically relevant predictors were investigated for short LOS prediction with a genetic algorithm (GenAlg) in 1426 patients. Due to the absence of an STS model for their particular surgery type, STS risk scores were unavailable for 771 patients. STS prediction achieved an AUC of 0.629 while the GenAlg achieved AUCs of 0.573 (in those with STS scores) and 0.691 (in those without STS scores). Among the patients with STS scores, the GenAlg features significantly associated with shorter LOS were absence of congestive heart failure (CHF) (OR = 0.59, p = 0.04), aortic valve procedure (OR = 1.54, p = 0.04), and shorter cross clamp time (OR = 0.99...

Real-Time Reliable Prediction of Linear-Elastic Mode-I Stress Intensity Factors for Failure Analysis

Huynh, Dinh Bao Phuong; Peraire, Jaime; Patera, Anthony T.; Liu, Guirong
Fonte: MIT - Massachusetts Institute of Technology Publicador: MIT - Massachusetts Institute of Technology
Tipo: Artigo de Revista Científica Formato: 310913 bytes; application/pdf
EN
Relevância na Pesquisa
35.85%
Modern engineering analysis requires accurate, reliable and efficient evaluation of outputs of interest. These outputs are functions of "input" parameter that serve to describe a particular configuration of the system, typical input geometry, material properties, or boundary conditions and loads. In many cases, the input-output relationship is a functional of the field variable - which is the solution to an input-parametrized partial differential equations (PDE). The reduced-basis approximation, adopting off-line/on-line computational procedures, allows us to compute accurate and reliable functional outputs of PDEs with rigorous error estimations. The operation count for the on-line stage depends only on a small number N and the parametric complexity of the problem, which make the reduced-basis approximation especially suitable for complex analysis such as optimizations and designs. In this work we focus on the development of finite-element and reduced-basis methodology for the accurate, fast, and reliable prediction of the stress intensity factors or strain-energy release rate of a mode-I linear elastic fracture problem. With the use of off-line/on-line computational strategy, the stress intensity factor for a particular problem can be obtained in miliseconds. The method opens a new promising prospect: not only are the numerical results obtained only in miliseconds with great savings in computational time; the results are also reliable - thanks to the rigorous and sharp a posteriori error bounds. The practical uses of our prediction are presented through several example problems.; Singapore-MIT Alliance (SMA)

Predição em modelos de tempo de falha acelerado com efeito aleatório para avaliação de riscos de falha em poços petrolíferos

Carvalho, João Batista
Fonte: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Matemática Aplicada e Estatística; Probabilidade e Estatística; Modelagem Matemática Publicador: Universidade Federal do Rio Grande do Norte; BR; UFRN; Programa de Pós-Graduação em Matemática Aplicada e Estatística; Probabilidade e Estatística; Modelagem Matemática
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
35.85%
We considered prediction techniques based on models of accelerated failure time with random e ects for correlated survival data. Besides the bayesian approach through empirical Bayes estimator, we also discussed about the use of a classical predictor, the Empirical Best Linear Unbiased Predictor (EBLUP). In order to illustrate the use of these predictors, we considered applications on a real data set coming from the oil industry. More speci - cally, the data set involves the mean time between failure of petroleum-well equipments of the Bacia Potiguar. The goal of this study is to predict the risk/probability of failure in order to help a preventive maintenance program. The results show that both methods are suitable to predict future failures, providing good decisions in relation to employment and economy of resources for preventive maintenance.; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Consideramos técnicas de predição baseadas em modelos de tempo de falha acelerado com efeito aleatório para dados de sobrevivência correlacionados. Além do enfoque bayesiano através do Estimador de Bayes Empírico, também discutimos sobre o uso de um método clássico, o Melhor Preditor Linear Não Viciado Empírico (EBLUP)...

Online Failure Prediction in Air Traffic Control Systems

Montanari, Luca
Fonte: La Sapienza Universidade de Roma Publicador: La Sapienza Universidade de Roma
Tipo: Tese de Doutorado
EN
Relevância na Pesquisa
36%
This thesis introduces a novel approach to online failure prediction for mission critical distributed systems that has the distinctive features to be black-box, non-intrusive and online. The approach combines Complex Event Processing (CEP) and Hidden Markov Models (HMM) so as to analyze symptoms of failures that might occur in the form of anomalous conditions of performance metrics identified for such purpose. The thesis presents an architecture named CASPER, based on CEP and HMM, that relies on sniffed information from the communication network of a mission critical system, only, for predicting anomalies that can lead to software failures. An instance of Casper has been implemented, trained and tuned to monitor a real Air Traffic Control (ATC) system developed by Selex ES, a Finmeccanica Company. An extensive experimental evaluation of CASPER is presented. The obtained results show (i) a very low percentage of false positives over both normal and under stress conditions, and (ii) a sufficiently high failure prediction time that allows the system to apply appropriate recovery procedures.

Mechanism and Prediction of Post-Operative Atrial Fibrillation Based on Atrial Electrograms

Xiong, Feng
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Thèse ou Mémoire numérique / Electronic Thesis or Dissertation
EN
Relevância na Pesquisa
35.83%
La fibrillation auriculaire (FA) est une arythmie touchant les oreillettes. En FA, la contraction auriculaire est rapide et irrégulière. Le remplissage des ventricules devient incomplet, ce qui réduit le débit cardiaque. La FA peut entraîner des palpitations, des évanouissements, des douleurs thoraciques ou l’insuffisance cardiaque. Elle augmente aussi le risque d'accident vasculaire. Le pontage coronarien est une intervention chirurgicale réalisée pour restaurer le flux sanguin dans les cas de maladie coronarienne sévère. 10% à 65% des patients qui n'ont jamais subi de FA, en sont victime le plus souvent lors du deuxième ou troisième jour postopératoire. La FA est particulièrement fréquente après une chirurgie de la valve mitrale, survenant alors dans environ 64% des patients. L'apparition de la FA postopératoire est associée à une augmentation de la morbidité, de la durée et des coûts d'hospitalisation. Les mécanismes responsables de la FA postopératoire ne sont pas bien compris. L'identification des patients à haut risque de FA après un pontage coronarien serait utile pour sa prévention. Le présent projet est basé sur l'analyse d’électrogrammes cardiaques enregistrées chez les patients après pontage un aorte-coronaire. Le premier objectif de la recherche est d'étudier si les enregistrements affichent des changements typiques avant l'apparition de la FA. Le deuxième objectif est d'identifier des facteurs prédictifs permettant d’identifier les patients qui vont développer une FA. Les enregistrements ont été réalisés par l'équipe du Dr Pierre Pagé sur 137 patients traités par pontage coronarien. Trois électrodes unipolaires ont été suturées sur l'épicarde des oreillettes pour enregistrer en continu pendant les 4 premiers jours postopératoires. La première tâche était de développer un algorithme pour détecter et distinguer les activations auriculaires et ventriculaires sur chaque canal...

Time to failure prediction in rubber components subjected to thermal ageing: A combined approach based upon the intrinsic defect concept and the fracture mechanics

BEN HASSINE, Mouna; NAÏT-ABDELAZIZ, M; ZAÏRI, F; COLIN, XAVIER; TOURCHER, C; MARQUE, G
Fonte: Elsevier Publicador: Elsevier
EN
Relevância na Pesquisa
35.89%
In this contribution, we attempt to derive a tool allowing the prediction of the stretch ratioat failure in rubber components subjected to thermal ageing. To achieve this goal, the mainidea is to combine the fracture mechanics approach and the intrinsic defect concept. Using an accelerated ageing procedure for an Ethylene–Propylene–Diene Monomer (EPDM), it is first shown that the average molar mass of the elastically active chains (i.e. between crosslinks) can be used as the main indicator of the macromolecular network degradation. Byintroducing the time–temperature equivalence principle, a shift factor obeying to an Arrhenius law is derived, and master curves are built as well for the average molar mass as for the ultimate mechanical properties. Fracture mechanics tests are also achieved and the square root dependence of the fracture energy with the average molar mass is pointed out. Moreover, it is shown that the mechanical response could be approximated by the phantom network theory, which allows to relate the strain energy density function to the average molar mass. Assuming that the fracture of a smooth specimen is the consequence of a virtual intrinsic defect whose the size can be easily estimated, the stretch ratio at break can be therefore computed for any thermal ageing condition. The estimated values are found in a very nice agreement with EPDM experimental data...

New Equation for Prediction of Reverse Remodeling after Cardiac Resynchronization Therapy

Hotta, Viviane Tiemi; Martinelli Filho, Martino; Mathias Júnior, Wilson; Vieira, Marcelo L. C.
Fonte: WILEY-BLACKWELL; HOBOKEN Publicador: WILEY-BLACKWELL; HOBOKEN
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
35.88%
Objectives: To integrate data from two-dimensional echocardiography (2D ECHO), three-dimensional echocardiography (3D ECHO), and tissue Doppler imaging (TDI) for prediction of left ventricular (LV) reverse remodeling (LVRR) after cardiac resynchronization therapy (CRT). It was also compared the evaluation of cardiac dyssynchrony by TDI and 3D ECHO. Methods: Twenty-four consecutive patients with heart failure, sinus rhythm, QRS = 120 msec, functional class III or IV and LV ejection fraction (LVEF) = 0.35 underwent CRT. 2D ECHO, 3D ECHO with systolic dyssynchrony index (SDI) analysis, and TDI were performed before, 3 and 6 months after CRT. Cardiac dyssynchrony analyses by TDI and SDI were compared with the Pearson's correlation test. Before CRT, a univariate analysis of baseline characteristics was performed for the construction of a logistic regression model to identify the best predictors of LVRR. Results: After 3 months of CRT, there was a moderate correlation between TDI and SDI (r = 0.52). At other time points, there was no strong correlation. Nine of twenty-four (38%) patients presented with LVRR 6 months after CRT. After logistic regression analysis, SDI (SDI > 11%) was the only independent factor in the prediction of LVRR 6 months of CRT (sensitivity = 0.89 and specificity = 0.73). After construction of receiver operator characteristic (ROC) curves...

Risk prediction for prostate cancer recurrence through regularized estimation with simultaneous adjustment for nonlinear clinical effects

Long, Qi; Chung, Matthias; Moreno, Carlos S.; Johnson, Brent A.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 23/11/2011
Relevância na Pesquisa
35.82%
In biomedical studies it is of substantial interest to develop risk prediction scores using high-dimensional data such as gene expression data for clinical endpoints that are subject to censoring. In the presence of well-established clinical risk factors, investigators often prefer a procedure that also adjusts for these clinical variables. While accelerated failure time (AFT) models are a useful tool for the analysis of censored outcome data, it assumes that covariate effects on the logarithm of time-to-event are linear, which is often unrealistic in practice. We propose to build risk prediction scores through regularized rank estimation in partly linear AFT models, where high-dimensional data such as gene expression data are modeled linearly and important clinical variables are modeled nonlinearly using penalized regression splines. We show through simulation studies that our model has better operating characteristics compared to several existing models. In particular, we show that there is a nonnegligible effect on prediction as well as feature selection when nonlinear clinical effects are misspecified as linear. This work is motivated by a recent prostate cancer study, where investigators collected gene expression data along with established prognostic clinical variables and the primary endpoint is time to prostate cancer recurrence.; Comment: Published in at http://dx.doi.org/10.1214/11-AOAS458 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

A Heteroscedastic Accelerated Failure Time Model for Survival Data

Wang, Yifan; You, Tian; Lysy, Martin
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 20/08/2015
Relevância na Pesquisa
45.71%
While the Cox Proportional Hazard model is a fundamental tool in survival analysis, its semi-parametric nature precludes the estimation of upper survival quantiles in the presence of heavy censoring. In contrast, fully parametric models do not suffer from this issue - at the expense of additional modeling assumptions. In this article, we extend a popular family of parametric models which make the Accelerated Failure Time (AFT) assumption to account for heteroscedasticity in the log-survival times. This adds substantial modeling flexibility, and we show how to easily and rapidly compute maximum likelihood estimators for the proposed model in the presence of censoring. In an application to the analysis of a colon cancer study, we found that heteroscedastic modeling greatly diminished the significance of outliers, while even slightly decreasing the average size of prediction intervals.; Comment: 14 pages, 4 figures

Optimal Prediction of Time-to-Failure from Information Revealed by Damage

Sornette, D.; Andersen, J. V.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 04/11/2005
Relevância na Pesquisa
36%
We present a general prediction scheme of failure times based on updating continuously with time the probability for failure of the global system, conditioned on the information revealed on the pre-existing idiosyncratic realization of the system by the damage that has occurred until the present time. Its implementation on a simple prototype system of interacting elements with unknown random lifetimes undergoing irreversible damage until a global rupture occurs shows that the most probable predicted failure time (mode) may evolve non-monotonically with time as information is incorporated in the prediction scheme. In addition, both the mode, its standard deviation and, in fact, the full distribution of predicted failure times exhibit sensitive dependence on the realization of the system, similarly to ``chaos'' in spinglasses, providing a multi-dimensional dynamical explanation for the broad distribution of failure times observed in many empirical situations.; Comment: 7 pages with 2 figures

BARRIER PROPERTY DETERMINATION AND LIFETIME PREDICTION BY ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY OF A HIGH PERFORMANCE ORGANIC COATING

CALDERÓN-GUTIERREZ,JORGE ANDRÉS; BEDOYA-LORA,FRANKY ESTEBAN
Fonte: DYNA Publicador: DYNA
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/02/2014 EN
Relevância na Pesquisa
55.76%
The anticorrosion performance of an Epoxy-Mastic organic coating was evaluated during continuous immersion in saline solution using electrochemical impedance spectroscopy (EIS). The typical parameters of pore resistance and charge transfer resistance were determined employing an equivalent electric circuit. Constant phase elements (CPE) were used in order to determine fraction of water absorbed, mass diffusion, solubility and the swelling coefficients, as well as to predict the failure times of the coating. The results found by EIS measurements match very well with the high resistance to deterioration exhibited by the coating. It was also found that the excellent protection performance of the coating was mainly due to low water solubility and low permeability.