• International Journal of Reliability and Applications
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• v.18 no.2
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• pp.83-98
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• 2017

Stress-strength reliability problems arise frequently in applied statistics and related fields. In the context of reliability, the stress-strength model describes the life of a component, which has a random strength X and is subjected to random stress Y. The component fails at the instant that the stress applied to it exceeds the strength and the component will function satisfactorily whenever X > Y. The problem of estimation the reliability parameter in a stress-strength model R = P[Y < X], when X and Y are two independent two-parameter Lindley random variables is considered in this paper. The maximum likelihood estimator (MLE) and Bayes estimator of R are obtained. Also, different confidence intervals of R are obtained. Simulation study is performed to compare the different proposed estimation methods. Example in real data is used as practical application of the proposed procedure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.690-693
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• 2005

In this paper, probabilistic distribution of fatigue life of chassis component is determined statistically by applying the design of experiments and the Pearson system. To construct $p-\varepsilon-N$ curve, the case that fatigue data are random variables is attempted. Probabilistic density function(p.d.f) for fatigue life is obtained by design of experiment and using this p.d.f fatigue reliability about any aimed fatigue life can be calculated. Lower control arm and rear torsion bar of chassis component are selected as examples for analysis. Component load histories, which are obtained by multi-body dynamic simulation for Belsian load history, are used. Finite element analysis are performed using commercial software MSC Nastran and fatigue analysis are performed using FE Fatigue. When strain-life curve itself is random variable, probability density function of fatigue life has very little difference from log-normal distribution. And the case of fatigue data are random variables, probability density functions are approximated to Beta distribution. Each p.d.f is verified by Monte-Carlo simulation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.313-319
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• 2009

The blasting has a lot of economic efficiency and speediness but it can damage to a neighbor structure, a domestic animal and a cultured fish due to the blasting vibration, then the public grievance is increased. Therefore, we need to manage the blasting vibration efficiently. The prediction of the correct vibration velocity is not easy because there are lots of different kinds of the scale of blasting vibration and it has a number of a variable effect. So we figure the optimum line through the least-squares regression by using the vibration data measured in hard rock blasting and compared with the design vibration prediction equation. As a result, we confirm that the vibration estimated in this paper is bigger than the design vibration prediction equation in the same charge and distance. If there is a Gaussian normal distribution data on the left-right side of the least squares regression, then we can estimate the vibration prediction equation on reliability 50%(${\beta}=0$), 90%(${\beta}=1.28$), 95%(${\beta}=1.64$). 99.9%(${\beta}=3.09$). As a result, it appears to be suitable that the reliability is 99% at the transverse component, the reliability 95% is at the vertical component, the reliability 90% is at the longitudinal component and the reliability is 95% at the peak vector sum component.

• Journal of the Korean Society of Safety
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• v.12 no.4
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• pp.241-248
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• 1997

This paper presents the development procedure and application of the generic component reliability data base considering the dependency among dependent generic compendia in NPPs (Nuclear Power Plants) PSA (Probabilistic Safety Assessment) under construction or without operating history. We use MPRDP (Multi-Purpose Reliability Data Processor) code developed in KAERI (Korea Atomic Energy Research Institute) based on a PEB (Parametric Empirical Bayesian) procedure to estimate the reliability. The employed model in this study accounts for the relative credibility as well as the dependency among generic estimates. Numerical examples and the part of summarized reliability data table are provided as the application.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2004.09a
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• pp.201-209
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• 2004

As the European Community's Directive on the Restriction of Hazardous Substances in Electrical and Electronic Equipment banning lead (Pb) in electronics products will take effect on July 1, 2006, most electronics manufacturers will be commencing with volume production of Pb-free components by the middle of 2004. Electrodeposited pure tin finishes on electronic components are a leading contender to replace the industry standard tin-lead. Commensurate with this shift will be a somewhat steep learning curve as manufacturers adapt a variety of equipment and processes to contend with the issues surrounding this critical, industry-wide material conversion. Since the electrodeposited finish directly influences the critical reliability characteristics of the component itself, the nature of the Pb-free component finish must be well characterized and understood. Only through a thorough examination of the attributes of the electroplated tin deposit can critical decisions be made regarding component finish reliability. This paper investigates the properties of electrodeposited tin that may have an effect on component reliability, namely, grain structure (size and shape), oxide formation, tin whisker formation, and solderability. Data will be presented from laboratory and production settings, with the objective being to enable manufacturers to draw their own conclusions regarding previously established perceptions and misconceptions about electrodeposited tin properties.

• International Journal of Reliability and Applications
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• v.18 no.2
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• pp.45-63
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• 2017

In this paper, we have formulated optimum Accelerated Life Test (ALT) plan for a parallel system with two independent components using masked data with ramp-stress loading scheme and Type-I censoring. Consider a system of two independent and non-identical components connected in parallel. Such a system fails whenever all of its components has failed. The exact component that causes the system to fail is often unknown due to cost and time constraint. For each parallel system at test, we observe its system's failure time and a set of component that includes the component actually causing the system to fail. The stress-life relationship is modelled using inverse power law, and cumulative exposure model is assumed to model the effect of changing stress. The optimal plan consists in finding out the optimum stress rate using D-optimality criterion. The method developed has been explained using a numerical example and sensitivity analysis carried out.

• Proceedings of the Korean Society for Quality Management Conference
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• 2007.04a
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• pp.331-333
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• 2007

Product reliability is important to quality and competitiveness. Much management and engineering efforts go into evaluating reliability, assessing new designs and changes of manufacturing processes, identifying cause of failure. Major methods are based on environmental test. In this study, we analysis the environmental test data of the electrical component, unit, set. These data were gathered for 7 years.

• Journal of the Korean Data and Information Science Society
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• v.6 no.2
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• pp.1-7
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• 1995

This paper considers the reliability estimation of a two-component shared-load system based on Freund model. Maximum likelihood estimator, order restricted maximum likelihood estimator and uniformly minimum variance unbiased estimator of the reliability function for the system are obtained. Performance of three estimators for moderate sample sizes is studied by simulation.

• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.96-107
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• 1997

The recent stream to reliability prediction is that it is totally inclusive in depth to consider even the operating and environmental condition at the level of finished goods as well as component itselves. In this study, firstly we present the reliability prediction methods by entire failure rate model which failure rate at the system level is added to the failure rate model at the component level. Secondly we build up the improved bases of reliability demonstration through a, pp.ication of Kaplan-Meier, Cumulative hazard, Johnson's methods as non-parametric and Maximum Likelihood Estimator under exponential & Weibull distribution as parametric. And also present the methods of curve fitting to piecewise failure rate under Weibull distribution, PRST (Probability Ratio Sequential Test), curve fitting to S-shaped reliability growth curve, computer programs of each methods. Lastly we show the practical for determination of optimal burn-in time as a method of reliability enhancement, and also verify the practical usefulness of the above study through the a, pp.ication of failure and test data during 1 year.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.2 s.191
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• pp.110-117
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• 2007

In this paper, probabilistic distribution of chassis component fatigue life is determined statistically by applying the design of experiments and the Pearson system. To construct p - ${\varepsilon}$ - N curve, the case that fatigue data are random variables is attempted. Probabilistic density function (p.d.f) for fatigue life is obtained by the design of experiment and using this p.d.f fatigue reliability, any aimed fatigue life can be calculated. Lower control arm and rear torsion bar of chassis components are selected as examples for analysis. Component load histories which are obtained by multi-body dynamic simulation for Belsian load history are used. Finite element analysis is performed by using commercial software MSC Nastran and fatigue analysis is performed by using FE Fatigue. When strain-life curve itself is random variable, the probability density function of fatigue life has very little difference from log-normal distribution. And the cases of fatigue data are random variables, probability density functions are approximated to Beta distribution. Each p.d.f is verified by Monte-Carlo simulation.