• International Journal of Reliability and Applications
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• v.9 no.2
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• pp.153-165
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• 2008

This paper discusses the reliability equivalences of a series-parallel system. The system components are assumed to be independent and identical. The failure rates of the system components are functions of time and follow Weibull distribution. Three different methods are used to improve the given system reliability. The reliability equivalence factor is obtained using the reliability function. The fractiles of the original and improved systems are also obtained. Numerical example is presented to interpret how to utilize the obtained results.

• Journal of Applied Reliability
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• v.11 no.3
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• pp.225-234
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• 2011

In the fields of reliability application, the most commonly used test methods for reliability qualification are zero-failure acceptance tests since they require fewer test samples and less test time compared to other test methods that guarantee the same reliability with a given confidence level. Usually values of shape parameters are assumed to be known in designing reliability qualification tests for Weibull lifetime distribution. It is important to select correct values of shape parameters to guarantee the specified reliability with given confidence level exactly. The effect of using wrong values of shape parameters in designing reliability qualification test for products with Weibull lifetime distribution is examined and selecting proper values of shape parameters for conservative reliability qualification is discussed.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.859-870
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• 2009

We obtain a skewed double Weibull distribution by a double Weibull distribution, and evaluate its coefficient of skewness. And we obtain the approximate maximum likelihood estimator(AML) and moment estimator of skew parameter in the skewed double Weibull distribution, and hence compare simulated mean squared errors(MSE) of those estimators. We compare simulated MSE of two proposed reliability estimators in two independent skewed double Weibull distributions each with different skew parameters. Finally we introduce a skewed double Weibull distribution generated by a uniform kernel.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.39-44
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• 2002

We compare MISE of the MLE, UMVUE, invariantly optimal estimator and Jacknife estimator for the reliability function of the Weibull distribution when the sample size is small.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.303-312
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• 2000

We compare the MLE, UMVUE and Jackknife Estimators for the reliability function of the weibull distribution when the sample size is small.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.783-792
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• 2007

We consider estimation of reliability P(Y

• Journal of Applied Reliability
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• v.14 no.4
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• pp.220-224
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• 2014

A Bayesian zero-failure reliability demonstration test method for products with Weibull lifetime distribution is presented. Inverted gamma prior distribution for the scale parameter of the Weibull distribution is used to design the Bayesian test plan and selecting a prior distribution using a prior test information is discussed. A test procedure with zero-failure acceptance criterion is developed that guarantee specified reliability of a product with given confidence level. An example is provided to illustrate the use of the developed Bayesian reliability demonstration test method.

• International Journal of Reliability and Applications
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• v.16 no.1
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• pp.15-26
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• 2015

Present study investigates the fuzzy reliability of some systems using intuitionistic fuzzy Weibull lifetime distribution, in which the lifetime parameters are assumed to be fuzzy parameter due to uncertainty and inaccuracy of data. Expressions for fuzzy reliability, fuzzy mean time to failure, fuzzy hazard function and their ${\alpha}$-cut have been discussed when systems follow intuitionistic fuzzy Weibull lifetime distribution. A numerical example is also taken to illustrate the methodology to calculate the fuzzy reliability characteristics of systems.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.90-96
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• 1998

Helical gear system is widely used to transmit heavy duty power with harmonies and silences between parallel shafts. This paper predicts a life with Weibull distribution and estimates a reliability based on recycle principle of helical gear systems. 2-parameter Weibull distribution is generally adopted to estimate the mechanical life and the reliability of most gear systems, because this Weibull distribution is proper to explain a characteristics or a life of parts of gear systems with linearity of probability density data on weibull data sheet. For a high reliability, this paper estimates a number of overhaul times and a number of needed substitutes (exchange attachment,1 or parts) with following renewal theory, One is make an exchange of whole module include failure attachments/parts and second estimating method is only exchange of a failure attachments / parts.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.4
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• pp.346-352
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• 2009

In the present study, the Weibull statistical analysis using the Monte-Carlo simulation has been performed to investigate the micro-Vickers hardness measurement reliability considering the variability. Experimental indentation test were performed with a micro-Vickers hardness tester for as-received and quenching and tempering specimens in SCM440 steels. The distribution of micro-Vickers hardness is found to be 2-parameter Weibull distribution function. The mean values and coefficients of variation (COV) for both data set are compared with results based on Weibull statistical analysis. Finally, Monte-Carlo simulation was performed in order to evaluate the effect of sample size on the micro-Vickers hardness measurement reliability. For the parent distribution with shape parameter 30.0 and scale parameter 200.0 (COV=0.040), the number of sample data required to obtain the true Weibull parameters was founded by 20. For the parent distribution with shape parameter 10.0 and scale parameter 200.0 (COV=0.1240), the number of sample data required to obtain the true Weibull parameters was founded by 30.