• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.933-939
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• 2009

The bivariate data in clinical research fields often has two types of failure times, which are mark variable for the first failure time and the final failure time. This paper showed how to generate bootstrap data to get Bayesian estimation for the joint distribution of bivariate survival times. The observed data was generated by Frank's family and the fake date is simulated with the Gamma prior of survival time. The bootstrap data was obtained by combining the mimic data with the observed data and the simulated fake data from the observed data.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.997-1005
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• 2003

In this paper, the problem of determining optimal burn-in time is considered under a general failure model. There are two types of failure in the general failure model. One is Type I failure (minor failure) which can be removed by a minimal repair and the other is Type II failure (catastrophic failure) which can be removed only by a complete repair. In this model, when the unit fails at its age t, Type I failure occurs with probability 1 - p(t) and Type II failure occurs with probability p(t), $0{\leq}p(t)\leq1$. Under the model, the properties of optimal burn-in time maximizing mean time to the catastrophic failure during field operation are obtained. The obtained results are also applied to some illustrative examples.

• 대한전기학회논문지:전력기술부문A
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• 제54권10호
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• pp.480-486
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• 2005

This paper proposes a method to consider an aging failure probability and survival probability of power system components, though only aging failure probability has been considered in existing mean life calculation. The estimates of the mean and its standard deviation is calculated by using Weibull distribution, and each estimated parameters is obtained from Data Analytic Method (Type H Censoring). The parameter estimation using Data Analytic Method is simpler and faster than the traditional calculation method using gradient descent algorithm. This paper shows calculation procedure of the mean life and its standard deviation by the proposed method and illustrates that the estimated results are close enough to real historical data of combustion turbine generating units in Korean systems. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an are-related reliability index.

• 대한산업공학회지
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• 제33권1호
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• pp.52-60
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• 2007

Estimating parameters of the lifetime distribution is investigated when field failure data are not completelyreported. To take into account the reality and the accuracy of the estimates in such a case, the failure reportingprobability is incorporated in estimating parameters, Firstly, method of maximum likelihood estimate (MLE) isused to estimate parameters of the lifetime distribution when failure reporting probability is known, Secondly,Expectation and Maximization (EM) algorithm is used to estimate the failure reporting probability and parame-ters of the lifetime distribution simultaneously when failure reporting probability is unknown. For both cases,procedures of estimation are illustrated for single Weibull distribution and mixed Weibull distribution. Simula-tion results show that MLE obtained by the proposed method is more accurate than the conventional MLE.

• 산업경영시스템학회지
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• 제42권4호
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• pp.194-202
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• 2019

Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters' posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.393-403
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• 2003

In this paper, various replacement policies for the general failure model are considered. There are two types of failure in the general failure model. One is Type I failure (minor failure) which can be removed by a minimal repair and the other is Type II failure (catastrophic failure) which can be removed only by a complete repair. In this model, when the unit fails at its age t, Type I failure occurs with probability 1-p(t) and Type II failure occurs with probability p(t), $0{\leq}p(t){\leq}1$. Under the model, optimal replacement policies for the long-run average cost rate and the limiting efficiency are considered. Also taking the cost and the efficiency into consideration at the same time, the properties of the optimal policies under the Cost-Priority-Criterion and the Efficiency-Priority-Criterion are obtained.

• 한국산학기술학회논문지
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• 제19권8호
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• pp.64-70
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• 2018

무기 체계(or 구성품) 개발은 한정된 개발기간과 비용 등의 제한으로 시험 횟수가 많지 않아, 고장관련 축적된 데이터의 규모도 적다. 그러나 운용 중 발생한 고장 및 정비내역은 많은 부분 전산 데이터로 관리하고 있기 때문에 이를 활용한 무기 체계(or 구성품)의 고장원인 분석은 가능하다. 다만 다양한 무기체계의 고장 및 정비내역 작성 규격이 각 군 별, 업체별 상이하고, 고장 원인의 구체적 내역은 비정형 텍스트 데이터로 기술되어 있기 때문에 이를 분석하는데 어려움이 있었다. 그러나 오늘날 빅데이터 처리 기술과 기계학습(Machine Learning) 알고리즘의 발전, HW연산 능력의 개선과 맞물려, 상기와 같은 비정형 데이터를 처리 할 수 있는 여러 가지 방법들이 시도 되고 있으며, 주요한 연구 분야로 활발히 연구되고 있다. 본 논문에서는 국방 무기 체계(or 구성품)의 고장/정비 관련 비정형 데이터를 기계학습 기법 중 하나인 doc2vec을 적용하여 고장사례 분석 방안에 대하여 제시한다.

• 정보처리학회논문지D
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• 제8D권4호
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• pp.387-392
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• 2001

많은 소프트웨어 프로젝트는 시험이나 운영단계에서 고장시간이나 고장 수 데이타보다 그룹 고장 데이터(여러 고장 간격에서 또는 가변적인 시간 간격에서의 고장들)가 수집된다. 본 논문은 그룹 고장 데이터에 대해 가변적인 미래의 시간에서 누적 고장 수를 예측할 수 있는 신경망 모델을 제시한다. 2개의 변형된 캐스케이드-상관 학습 알고리즘을 제안하였다. 제안된 신경망 모델들은 다른 잘 알려진 신경망 모델과 통계적 소프트웨어 신뢰도 성장 모델과 비교되었다. 실험결과, 그룹 데이터에 대해 변형된 캐스케이드-상관 학습 알고리즘이 좋은 예측 결과를 나타내었다.

• 전기학회논문지P
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• 제59권2호
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• pp.154-157
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• 2010

In this paper, the Time-varying Failure Rates(TFR) of power distribution system components are extracted from the recorded failure data of KEPCO(Korea Electric Power Corporation) and the reliability of power distribution system is evaluated using Mean Failure Rate(MFR) and TFR. The TFR is approximated to bathtub curve using the exponential and Weibull distribution function. In addition, Kaplan-Meier estimation is applied to TFR extraction because of incomplete failure data of KEPCO. Also the reliability of the real power distribution system of Korea is evaluated using the MFR and TFR extracted from real failure data, respectively and the results of each case are compared with each other. As a result, it is proved that the reliability evaluation using the TFR is more realistic than MFR. In addition, it is presented that the application method at power distribution system maintenance and repair using the result of TFR.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 춘계학술대회 논문집
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• pp.366-368
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• 2007

In this paper, the Time-varying Failure Rate (TFR) of power distribution system equipment is extracted from the recorded failure data of Korea Electric Power Corporation (KEPCO). For TFR extraction, it is used that the fault data accumulated by KEPCO during 10 years. The TFR is approximated to bathtub curve using the exponential (random failure) and Weibull (aging failure) distribution function. In addition, Kaplan-Meier estimation is applied to TFR extraction because of incomplete failure data of KEPCO. Finally, Probability plot and regression analysis is applied. It is presented that the extracted TFR is more effective and useful than Mean Failure Rate (MFR) through the comparison between TFR and MFR.