• 대한기계학회논문집A
• /
• 제30권8호
• /
• pp.941-948
• /
• 2006

In this study, the new method for reliability estimation is proposed using kriging metamodel. Kriging metamodel can be determined by appropriate sampling range and sampling numbers because there are no random errors in the Design and Analysis of Computer Experiments(DACE) model. The first kriging metamodel is made based on widely ranged sampling points. The Advanced First Order Reliability Method(AFORM) is applied to the first kriging metamodel to estimate the reliability approximately. Then, the second kriging metamodel is constructed using additional sampling points with updated sampling range. The Monte-Carlo Simulation(MCS) is applied to the second kriging metamodel to evaluate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

• International Journal of Reliability and Applications
• /
• 제9권1호
• /
• pp.53-70
• /
• 2008

In this paper, we discuss the plethora of uses for the software package R, and focus specifically on its helpful applications in reliability data analyses. Examples are presented; including the R coding protocol, R code, and plots for various statistical as well as reliability analyses. We explore Kaplan-Meier estimates and maximum likelihood estimation for distributions including the Weibull. Finally, we discuss future applications of R, and usages of quantile regression in reliability.

• 한국신뢰성학회지:신뢰성응용연구
• /
• 제13권2호
• /
• pp.99-107
• /
• 2013

One-shot device is required to successfully perform its function only once at the moment of use. The reliability of a one-shot device should be expressed as a probability of success. In this paper, we propose a bayesian approach for estimating reliability of one-shot devices with small sample size. We employ a gamma prior to obtain the posterior distribution. Finally, we compare the accuracy of the proposed method with general maximum likelihood method.

• 대한안전경영과학회지
• /
• 제9권2호
• /
• pp.161-169
• /
• 2007

This paper presents three methods for expression of reliability measures for large and small data. First method is to express parametric estimation of cardinal reliability measure data for large sample, which requires numerous sample. Second is to obtain nonparametric distribution classification of ordinal reliability measure data for small sample. However it is difficult for field user to understand this method. Last method is to acquire parametric estimation of ordinal reliability measure data for small data. Because this method requires small sample and is comprehensive, we recommend this one among the proposed methods. Various reliability rank measures are presented.

• 대한기계학회논문집A
• /
• 제30권9호
• /
• pp.1116-1123
• /
• 2006

In this study, the effective method for reliability estimation is proposed using tow-staged kriging metamodel and genetic algorithm. Kriging metamodel can be determined by appropriate sampling range and the number of sampling points. The first kriging metamodel is made based on the proposed sampling points. The advanced f'=rst order reliability method is applied to the first kriging metamodel to determine the reliability and most probable failure point(MPFP) approximately. Then, the second kriging metamodel is constructed using additional sampling points near the MPFP. These points are selected using genetic algorithm that have the maximum mean squared error. The Monte-Carlo simulation is applied to the second kriging metamodel to estimate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

• 대한산업공학회지
• /
• 제42권4호
• /
• pp.256-262
• /
• 2016

The Weibull is widely used in reliability analysis, and several studies have attempted to improve estimation of the distribution's parameters. least squares estimation (LSE) or Maximum likelihood estimation (MLE) are often used to estimate distribution parameters. However, it has been proven that Bayesian methods are more suitable for small sample sizes than LSE and MLE. In this work, the Weibull parameter estimation accuracy of LSE, MLE, and Bayesian method are compared for sample sets with 3 to 30 data points. The Bayesian method was most accurate for sample sizes under 25, and the accuracy of the Bayesian method was similar to LSE and MLE as the sample size increased.

• 정보처리학회논문지D
• /
• 제10D권5호
• /
• pp.829-836
• /
• 2003

소프트웨어 신뢰도 성장 모델은 다양하게 연구되어져 있다. 그러나 이러한 모델에서 정확한 모수를 측정하는 것은 그리 쉽지 않다. 특히 고장 데이터에 대하여 소프트웨어 신뢰도 성장 모델의 추정이 정확히 이루어져야만 모델을 설명하는 모수의 추정도 정확하게 이루어질 수 있다. 이러한 측면에서 테스팅을 통해서 얻어진 소프트웨어의 고장 데이터의 정규확률점수를 구해서 두 개의 값에 대한 플롯을 그려보고 그려진 결과를 이용해서 분포를 예측하여 예측된 분포에 적합한 소프트웨어 신뢰도 성장 모델을 적용한다면 상당히 정확한 테스팅 결과론 얻을 수 있을 것이다. 본 논문에서는 고장 테이터의 플롯을 통한 결과를 통해서 분포를 예측하고 모델을 성능평가 척도에 따라서 모의실험을 하여 그 결과를 통해서 소프트웨어 신뢰도 성장 모델의 적합성을 검정하는 연구이다. 연구결과 고장데이터의 정규점수를 이용한 플롯을 보고 소프트웨어 신뢰도 성장 모델을 예측할 수 있었고 이러한 예측을 통해서 모델 선정한다면 모델의 성능평가에서도 우수함을 확인할 수 있다.

• International Journal of Reliability and Applications
• /
• 제1권2호
• /
• pp.133-143
• /
• 2000

Data from weighted distributions appear, among other situations, when some of the data are missing or are damaged, a case that is important in reliability and life testing. The kernel method for hazard rate estimation is discussed for these data where the basic large sample properties are given. As a by product, the basic properties of the kernel estimate of the distribution function for data from weighted distribution are presented.

• Journal of the Korean Data and Information Science Society
• /
• 제19권3호
• /
• pp.967-977
• /
• 2008

We consider densities of quotient Y/X and ratio X/(X+Y), and estimation of reliability P(Y

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 1998년도 추계학술대회 논문집
• /
• pp.210-215
• /
• 1998

In this paper, systemaic approach is studied about assembility and disassemblility of parts of the gear system in order to reduce the assembly cost, and to disassemble products easily which is possible to recycle the parts. That is, an estimation of disassembly and assembly with considering of reliability life. In this study, we use symbolic chart method for an economic model for optimal disassembly and assembly.