• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.147-161
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• 2016

This paper introduces the four parameter new generalized inverse Weibull distribution and investigates the potential usefulness of this model with application to reliability data from engineering studies. The new extended model has upside-down hazard rate function and provides an alternative to existing lifetime distributions. Various structural properties of the new distribution are derived that include explicit expressions for the moments, moment generating function, quantile function and the moments of order statistics. The estimation of model parameters are performed by the method of maximum likelihood and evaluate the performance of maximum likelihood estimation using simulation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.05a
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• pp.46-49
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• 2000

This paper studied development of the program for estimation the life time of insullating materials and the longtime breakdown voltage. First, short-time breakdown voltage of Epoxy and insulating oil was measured. Life exponent was gained from measurement of insulating breakdown time of the specimens. Life time is presumed from program. The estimation program is based on the "Inverse Power Law", defined $V^nt$ is constant. After gaining the life exponent n, it is mapping the longtime breakdown voltages. On the base of life exponent, the estimation the lifetime and usefulness of the insulation systems are possible, furthermore easy calculation is possible.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.161-164
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• 2008

We proposed 2-phase regression of power function inside exponential for PDP lifetime data analysis. In introducing our method we discussed the reason why PDP degradation behavior is described by exponential function basically. By applying our method to 50HD and 50FHD PDP lifetime experiment data, we obtained more than 100,000Hr lifetime. From these results, we claim that PDP lifetime is more than 100,000Hr.

• Proceedings of the KIEE Conference
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• 2008.05a
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• pp.131-132
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• 2008

This paper describes a method to estimate the thermal lifetime of coil used in the dry-type molded transformer which is widely used in the domestic distribution system. In order to reduce the testing time, temperature accelerated aging test is planned. Finally, the thermal lifetime estimation method is composed of a temperature aging test and a cyclic test of temperature, humidly and lightning impulse voltage withstand test.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.299-308
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• 1997

In the simple linear regression model Y = .alpha.$_{0}$ + .beta.$_{0}$Z + .epsilon. under the right censorship of the response variables, the estimation of the mean lifetime E(Y) is an interesting problem. In this paper we propose a method of estimating E(Y) based on the observations modified by the arguments of Buckley and James (1979). It is shown that the proposed estimator is consistent and our proposed procedure in the simple linear regression case can be naturally extended to the multiple linear regression. Finally, we perform simulation studies to compare the proposed estimator with the estimator introduced by Gill (1983).83).

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.116.2-116.2
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• 2005

• Journal of Korean Society for Quality Management
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• v.16 no.2
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• pp.56-67
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• 1988

It is wellknown that the left truncated exponential distribution with positivity constraint on the location parameter is appropriate as a lifetime distribution model, In this paper, some Bayes estimators of the parameters and reliability for the left truncated exponential lifetime distribution when an unidentified-failure outlier is included and it is excluded in the exchangeable outlier model are proposed, and the performances of these proposed Bayes estimators are also discussed.

• Journal of Applied Reliability
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• v.13 no.3
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• pp.191-206
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• 2013

Application areas for Lifetime Performance Index(LPI), a kind of process capability index to be frequently used as a means of measuring process performance are illustrated with examples. Statistical properties for maximum likelihood and unbiased estimators of LPI are evaluated and discussed under Weibull distribution with known shape parameter. Furthermore, guidelines for selecting an estimator of LPI are also presented.

• Journal of Hydrogen and New Energy
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• v.26 no.2
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• pp.192-198
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• 2015

Fault Tree Analysis to predict the lifetime in the design process of LNG compressor is considered. Fault Trees for P & ID of the compressor are created. Individual components that comprise the compressor are configured with the basic event. The failure rates in the PDS and OREDA are applied. As results, the system failure rate and the reliability over time are obtained. Further, the power transmission and the shaft seal system is confirmed to confidentially importantly contribute to the overall lifetime of the system. These techniques will help to improve the reliability of design of large scale machinery such as a plant.

• Journal of Computing Science and Engineering
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• v.4 no.2
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• pp.128-152
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• 2010

With the advance of computer and communication technologies, wireless sensor networks (WSNs) are increasingly used in many aspects of our daily life. However, since the battery lifetime of WSN nodes is restricted, the WSN lifetime is also limited. Therefore, it is crucial to determine this limited lifetime in advance for preventing service interruptions in critical applications. This paper proposes a feasible static analysis approach to estimating the worstcase lifetime of a WSN. Assuming known routes with a given sensor network topology and SMAC as the underlying MAC protocol, we statically estimate the lifetime of each sensor node with a fixed initial energy budget. These estimations are then compared with the results obtained through simulation which run with the same energy budget on each node. Experimental results of our research on TinyOS applications indicate that our approach can safely and accurately estimate worst-case lifetime of the WSN. To the best of our knowledge, our work is the first one to estimate the worst-case lifetime of WSNs through a static analysis method.