• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.427-436
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• 2000

In this paper, we consider a Bayesian model selection problem of lifetime distributions using fractional Bayes factor with noninformative prior when type II censored data are given. For a given type II censored data, we calculate the posterior probability of exponential, Weibull and lognormal distributions and select the model which gives the highest posterior probability. Our proposed methodology is explained and applied to real data and simulated data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.141-147
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• 2016

NHPP software reliability models for failure analysis can have, in the literature, exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, infinite failures NHPP models that repairing software failure point in time reflects the situation, was presented for comparing property. Commonly used in the field of software reliability based on Flexible Weibull extension distribution software reliability of infinite failures was presented for comparison problem. The result is that a relatively small shaping parameter was effectively. The parameters estimation using maximum likelihood estimation was conducted and model selection was performed using the mean square error and the coefficient of determination.. In this research, software developers to identify software failure property follows shape parameter, some extent be able to help is considered.

• Journal of Korean Society for Quality Management
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• v.11 no.2
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• pp.18-24
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• 1983

In this paper, subset selection procedures are proposed for selecting the Weibull population with the smallest scale parameter out of k Weibull populations with a common shape parameter. The proposed procedures are based on the maximum likelihood estimators. The constants to implement the procedures are tabulated using Monte Carlo methods. Also, the results of a comparison study are given.

• Convergence Security Journal
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• v.6 no.3
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• pp.117-126
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• 2006

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, could avoid multiple integration using Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference for general order statistics models in software reliability with diffuse prior information and model selection method are studied. For model determination and selection, explored goodness of fit (the error sum of squares), trend tests. The methodology developed in this paper is exemplified with a software reliability random data set introduced by of Weibull distribution(shape 2 & scale 5) of Minitab (version 14) statistical package.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.82-88
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• 2018

The decoy defensive weapon system is a one-shot system. Reliability is maintained through periodic inspection and high reliability is required to confirm whether or not the functioning is normal after launch. The maintenance cycle of a decoy was set up without target reliability and reliability prediction during the development period. However, the number of operations in the military has been increasing, necessitating the optimization of the maintenance cycle. Reliability is analyzed using the maintenance data of a decoy operated for several decades and the optimal maintenance cycle is suggested. In chapter 2, data collection and classification methods are presented and analysis methodology is briefly introduced. In chapter 3, the data distribution analysis and fitness verification confirmed that applying the Weibull distribution is the most suitable for the maintenance data of the decoy. In chapter 4, we present the analysis result of percentile, survival probability and MTBF and the optimal maintenance cycle was derived from the reliability analysis. Finally, we suggest the application methods for this paper in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.594-599
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• 2011

In this paper, the effect of the boundary conditions on the reliability and the cumulative distribution characteristics of the fatigue failure life is analyzed in a magnesium alloy AZ31. The boundary conditions are specimen thickness, stress ratio, and maximum fatigue load. The statistical data of the fatigue failure life are obtained by fatigue crack propagation tests under the detail conditions for each boundary condition. The 3-parameter Weibull distribution is used to analyze a statistical characteristics of the fatigue failure life in magnesium alloy AZ31. It is found that the statistical fatigue failure life is long in the case of a thicker specimen, a larger stress ratio, and a smaller maximum fatigue load. Under the opposite cases, the reliability on the fatigue failure life is rapidly dropped.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.9-18
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• 2006

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the exponentiated exponential distribution reliability model, which maked out efficiency substituted for gamma and Weibull model(2 parameter shape illustrated by Gupta and Kundu(2001) Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE, AIC statistics and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using NTDS data set for the sake of proposing shape parameter of the exponentiated exponential distribution was employed. This analysis of failure data compared with the exponentiated exponential distribution model and the existing model (using arithmetic and Laplace trend tests, bias tests) is presented.

• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.133-145
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• 1995

This paper is concerned with the method of estimating lifetime distribution from field data for repairable products with multiple modes of failure, and is an extension of Bai et al.(1995). The log linear function is considered as a model for describing the relation between failure time of a product and covariates. Using the nonhomogeneous poisson process, general methods for obtaining pseudo maximum likelihood estimators(PMLEs) for the parameters are outlined and specific formulas for Weibull distribution are obtained. Effects of follow-up percentage on the PMLEs are investigated. Extension to case-cohort design is also considered.

• Convergence Security Journal
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• v.8 no.2
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• pp.35-40
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• 2008

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. The used software failure time data for forecasting failure time is random number of Weibull distribution(shaper parameter 1, scale parameter 0.5), Using this data, we are proposed to ARIMA(AR(1)) and simulation method for forecasting failure time. The practical ARIMA method is presented.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.183-194
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• 1997

In accelerated life tests, the failure time of an item is observed under a high stress level and based on the time, the failure rates of items we estimated at the normal stress level. In this paper, when the mean of the prior distribution of a parameter is known in Weibull lifetime model with censored failure time data, we study various estimating methods to obtain the empirical Bayes estimator of a parameter from the empirical Bayes approach under the normal stress level by considering the fact that the Bayes estimator is the function of prior parameters and of the acceleration parameter representing the effect of acceleration. And we compare the performance of several empirical Bayes estimators of a parameter in terms of the Bayes risk.