• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.27-34
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• 2005

We consider the problem of estimating the system reliability noninformative priors when both stress and strength follow generalized gamma distributions. We first derive Jeffreys' prior, group ordering reference priors, and matching priors. We investigate the propriety of posterior distributions and provide marginal posterior distributions under those noninformative priors. We also examine whether the reference priors satisfy the probability matching criterion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.22
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• pp.25-34
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• 1990

This paper is to maximize the reliability subject to certain constraints on amounts of resources available for control of the parameters. The lagrange multiplier method is used to optimize t-th lognormal stress-lognormal strength problem. This optimization problem can be reduced to a search problem in one variable. A numerical example is presented to illustrate the optimization problem.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.152-162
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• 1999

In this paper we develope a new nonparametric estimator of the reliability in strength-stress model. This estimator is constructed using the maximum likelihood estimate of cumulative failure rate in the class of distributions which have piecewise linear failure rate functions between each pair of observations. Large sample properties of our estimator are examined. The proposed estimator is compared with previously known estimator by Monte Carlo study.

• Journal of Korean Society for Quality Management
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• v.13 no.1
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• pp.2-11
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• 1985

We study some Bayes esimators of the reliability of k out of m stress-strength model under quadratic loss and various prior distributions. We obtain Bayes estimators, Bayes risk, predictive bounds and asymtotic distribution of Bayes estimator. We investigate behaviours of Bayes estimator in moderate samples.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.73-83
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• 1995

We construct the approximate bootstrap confidence intervals for reliability (R) when the distributions of strength and stress are both normal. Also we propose percentile, bias correct (BC), bias correct acceleration (BCa), and percentile-t intervals for R. We compare with the accuracy of the proposed bootstrap confidence intervals and classical confidence interval based on asymptotic normal distribution through Monte Carlo simulation. Results indicate that the confidence intervals by bootstrap method work better than classical confidence interval. In particular, confidence intervals by BC and BCa method work well for small sample and/or large value of true reliability.

• Earthquakes and Structures
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• v.7 no.6
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• pp.1061-1070
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• 2014

An iterative hybrid structural dynamic reliability prediction model has been developed under multiple-time interval loads with and without consideration of stochastic structural strength degradation. Firstly, multiple-time interval loads have been substituted by the equivalent interval load. The equivalent interval load and structural strength are assumed as random variables. For structural reliability problem with random and interval variables, the interval variables can be converted to uniformly distributed random variables. Secondly, structural reliability with interval and stochastic variables is computed iteratively using the first order second moment method according to the stress-strength interference theory. Finally, the proposed method is verified by three examples which show that the method is practicable, rational and gives accurate prediction.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.89-98
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• 1999

In this paper we estimate the reliability of parallel system with two components. We assume that the strengths of these components follow bivariate exponential(BVE) models proposed by Marshall-Olkin(1967) Block-Basu(1974) Freund(1961) and Proschan-Sullo(1974) These two components are subjected to a normally distributed random stress which is independent of the strength of the components. If the strengths ($\textit{X}_1$, $\textit{X}_2$) are subjected to a stress($\textit{Y}$) then the system reliability ($\textit{R}$) is given by $\textit{R}=\textit{P}[\textit{Y} We present some numerical results and compare the bias and the mean square error of the maximum likelihood estimator and proposed estimators for a moderate sized samples when $(\textit{X}_1, \textit{X}_2)$ follow BVE of Marshall-Olkin.

• Journal of Korean Society for Quality Management
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• v.10 no.1
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• pp.2-6
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• 1982

Suppose a system with m components is subjected to a random stress. We consider the estimation of reliability when data consist of random samples from the stress distribution and the strength distributions. All the distributions are assumed to be independent exponential with unknown scale parameters. An explicit form of system reliability and the minimun variance unbiased estimator are obtained. The asymptotic distribution is also obtained by expanding the minimum variance unbiased estimator about the maximum likelihood estimator and establishing their equivalance. The performance of the two estimators is compared by Monte Carlo Simulation.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.4
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• pp.59-67
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• 2006

Recently, application of friction welded SUH35/SUH3 is increasing in the manufacturing process of automotive engine valves For securing its reliability and a reasonable strength evaluation method, it is necessary to assess stress singularity under the residual stress condition on the friction welded interface between dissimilar materials. In this paper, strength evaluation method of friction welded materials was investigated by boundary element method and static tensile testing. An advanced method of quantitative strength evaluation for SUH35/SUH3 friction welded material is to be suggested by establishing fracture criterion by using stress singularity factors.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.187-194
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• 1996

We treat the problem of estimating reliability R = P[Y < X] in the stress-strength model in which a unit of strength X is subfected to enviromental stress Y./ In this paper several nonparametric approaches to estimation of R are analyzed and compared by simulations.