• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1451-1458
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• 2004

Current software reliability growth models based on Gompertz growth curve are all logarithmic type. Software reliability growth models based on logarithmic type Gompertz growth curve has difficulties in parameter estimation. Therefore this paper proposes a software reliability growth model based on the logistic type Gompertz growth curie. Its usefulness is empirically verified by analyzing the failure data sets obtained from 13 different software projects. The parameters of model are estimated by linear regression through variable transformation or Virene's method. The proposed model is compared with respect to the average relative prediction error criterion. Experimental results show that the pro-posed model performs better the models based on the logarithmic type Gompertz growth curve.

• Journal of the Korean Society for Railway
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• v.18 no.3
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• pp.186-193
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• 2015

In this paper, a reliability growth analysis for a next-generation high-speed train was conducted. First, the high-speed train was decomposed into 6 sub-systems and main equipment of the high-speed train was derived from functional diagrams. Next, failure rates were calculated for each sub-system from the failure data obtained during commissioning tests. Then, reliability growth analysis was conducted for the high-speed train using the Duane model. The results show that activities to increase reliability were carried out throughout the test runs from the reliability growth results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.747-752
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• 2019

By using the failure information and the cumulative test execution time obtained by performing the reliability growth test, it is possible to estimate the parameter of the reliability growth model, and the Mean Time Between Failure (MTBF) of the product can be predicted through the parameter estimation. However the failure information could be acquired periodically or the number of sample data of the obtained failure information could be small. Because there are various constraints such as the cost and time of test or the characteristics of the product. This may cause the error of the parameter estimation of the reliability growth model to increase. In this study, the Bayesian method is applied to estimating the parameters of the reliability growth model when the number of sample data for the fault information is small. Simulation results show that the estimation accuracy of Bayesian method is more accurate than that of Maximum Likelihood Estimation (MLE) respectively in estimation the parameters of the reliability growth model.

• Journal of Applied Reliability
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• v.5 no.3
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• pp.381-391
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• 2005

The level of reliability attained largely depends upon the investment in reliability growth programs during development phase. In order to find the relationship between reliability growth test time and BRTE(basic reliability tasks effectiveness) in a reliability improvement program that minimizes LCC in which contains the reliability growth cost, repair and replacement costs, and spare parts ordering costs in service with given service rate in management policy, the growth rate has been suggested proper LCC versus growth rate. This model employs the reliability growth projection with delayed fixes in avionic equipment based on AMSAA.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.4
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• pp.336-345
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• 2012

In crack growth life, uncertainties are caused by variance of geometry, applied loads and material properties. Therefore, the reliability estimation for these uncertainties is required to keep the robustness of calculated life. The stress intensity factors are the most important variable in crack growth life calculation, but its equation is hard to know for complex geometry, therefore they are processed by the finite element analysis which takes long time. In this paper, the response surface is considered to increase efficiency of the reliability analysis for crack growth life of a turbine wheel. The approximation model of the stress intensity factors is obtained by the regression analysis for FEA data and the response surface of crack growth life is generated for selected factors. The reliability analysis is operated by the Monte Carlo Simulation for the response surface. The results indicate that the response surface could reduce computations that need for reliability analysis for the turbine wheel, which is hard to derive stress intensity factor equation, successfully.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1287-1294
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• 2004

Recently relationships between reliability measures and the coverage have been developed for evaluation of software reliability. Particularly the mean value function of the coverage-based software reliability growth model is important because of its key role in rep-resenting the software reliability growth. In this paper, we first review the problems of the existing mean value functions with respect to the assumptions on which they are based. Then a new mean value function is proposed. The new mean value function is developed for a general testing environment in which imperfect fault detection and repeated construct execution are allowed. Finally performance of the proposed model is empirically evaluated by applying it to a real data set.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.406-418
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• 2020

When developing weapons systems, it is important to implement the functions and performance of equipment suitable for development purposes, but it is very important to ensure that the equipment is capable of operating without any vacuum with reliability after development. Therefore, various activities are carried out to enhance reliability of equipment. Reliability is enhanced by using high-specification parts in development, reliability verification through analysis, and testing using development prototypes to reinforce and improve the parts that are lacking in equipment. However, recently, development schedules are shortened due to rapidly changing external conditions and technologies, and there are cases where sufficient reliability growth activities were not carried out due to problems such as cost. Examples are projects that perform reliability activities only in analytical methods (reliability, FMECA). In this paper, analyzing and testing methods for analysis and testing were carried out on the same equipment through FMECA and super-accelerated life test, the contents of reliability growth activity were derived, the results of design change/review were accordingly compared, the differences between the two methods were analyzed, and measures were proposed to strengthen reliable growth activities. It was concluded that reliable growth activities through analysis from the beginning of development and reliable growth activities through testing should be carried out at the completion of initial prototype production.

• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.157-163
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• 2005

신뢰성성장시험관리는 제품개발프로그램의 초기단계에서 고장모드를 확인하고, 이를 개선 또는 제거하기 위해 설계를 변경하고, 그 결과 진행되는 신뢰성이 향상되는 변화를 추적할 수 있는 실용적인 방법이다. 본 연구의 목적은 AMSAA(Army Materiel Systems Analysis Activity)모델을 이용하여 신뢰성 성장을 계획하고 평가할 수 있는 실용적인 방법을 제시하는데 있다. 시험-개선 과정을 통하여 성장하는 신뢰성 수준의 변화에 대한 추적과 예측 가이드라인을 제시함으로써 현장에서 활용할 수 있는 방법을 보여준다.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.659-666
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• 2002

Park, Seo and Kim (12) developed the input domain-based SRGM, which was able to quantitatively assess the reliability of a software system during the testing and operational phases. They assumed perfect debugging during testing and debugging phase. To make this input domain-based SRGM more realistic, this assumption should be relaxed. In this paper we generalize the input domain-based SRGM under imperfect debugging. Then its statistical characteristics are investigated.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.829-836
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• 2003

Software Reliability Growth Model that have been studied variously. But measurement of correct parameter of this model is not easy. Specially, estimation of correct model about failure data must be establish and estimation of parameter can consist exactly. To get correct testing, we calculate the normal score and describe the normal probability plot. Use the normal probability plot, we estimate the distribution for failure data. In this paper, we estimate the software reliability growth model for through the normal probability plot. In this research, we applies software reliability growth model through distribution characteristics of failure data. If we see plot, we determine the software reliability growth model, we can make sure superior in model's performance estimation.