• Title/Summary/Keyword: MTBF

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MTBF Estimator in Reliability Growth Model with Application to Weibull Process (와이블과정을 응용한 신뢰성 성장 모형에서의 MTBF 추정$^+$)

  • 이현우;김재주;박성현
    • Journal of the Korean Society for Quality Management
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    • v.26 no.3
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    • pp.71-81
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    • 1998
  • In reliability analysis, the time difference between the expected next failure time and the current failure time or the Mean Time Between Failure(MTBF) is of significant interest. Until recently, in reliability growth studies, the reciprocal of the intensity function at current failure time has been used as being equal to MTBE($t_n$)at the n-th failure time $t_n$. That is MTBF($t_n$)=l/$\lambda (t_n)$. However, such a relationship is only true for Homogeneous Poisson Process(HPP). Tsokos(1995) obtained the upper bound and lower bound for the MTBF($t_n$) and proposed an estimator for the MTBF($t_n$) as the mean of the two bounds. In this paper, we provide the estimator for the MTBF($t_n$) which does not depend on the value of the shape parameter. The result of the Monte Carlo simulation shows that the proposed estimator has better efficiency than Tsokos's estimator.

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Reliability Analysis of cooler in Thermal Observation Device (열상감시장비의 냉각기 신뢰도 분석)

  • Hong, Seok-Jin;Jung, Yun-Sik;Kim, Jin-Hwan
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.11
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    • pp.432-436
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    • 2016
  • The cooler, which is the main part in a Thermal Observation Device (TOD), makes the TOD function by reducing the temperature. As the cooler is imported, overseas enterprises presented 20,000 hours as the operation time and the military have used the cooler as presented. However, failures have occurred occasionally after mass production stage. Therefore, we need to analyze the MTBF of the TOD cooler. So, military and defense industry companies collected the failure data of the TOD cooler. We analyze the MTBF of the TOD cooler using survival probability function and failure data. We find the optimal distribution by applying parametric method and estimate parameters. We determine that the Log-logistic distribution is the most appropriate for this data. Also, we analyze the reliability per hour of the TOD cooler. The result of MTBF of the TOD cooler was higher than that of presented by oversee enterprises.

A Comparative Study of the Parameter Estimation Method about the Software Mean Time Between Failure Depending on Makeham Life Distribution (메이크헴 수명분포에 의존한 소프트웨어 평균고장간격시간에 관한 모수 추정법 비교 연구)

  • Kim, Hee Cheul;Moon, Song Chul
    • Journal of Information Technology Applications and Management
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    • v.24 no.1
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    • pp.25-32
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    • 2017
  • For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.

A Study on the Failure Definition for the MTBF Evaluation (MTBF 평가를 위한 고장정의 소고)

  • Kim, Cheol
    • IE interfaces
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    • v.1 no.2
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    • pp.67-74
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    • 1988
  • MTBF (Mean Time Between Failures) is one of the measures to express the reliability for a repairable system, especially for a military weapon system. But MTBF is meaningless without a clear definition of the system failures. In this paper we discuss two failure definitions, one is defined by US Army Training and Doctrine Command jointly with US Army Materiel Command and the other one is used to M1 Tank.

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Reliability Prediction Based on Field Failure Data of Guided Missile (필드데이터 기반의 유도탄 신뢰도 예측)

  • Seo, Yangwoo;Lee, Kyeshin;Lee, Younho;Kim, Jeyong
    • Journal of Applied Reliability
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    • v.18 no.3
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    • pp.250-259
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    • 2018
  • Purpose: Previously, missile reliability prediction is based on theoretical failure prediction model. It has shown that the predicted reliability is inadequate to real field data. Although an MTTF based reliability prediction method using real field data has recently been studied to overcome this issue. In this paper, we present a more realistic method, considering MTBF concept, to predict missile reliability. Methods: In this paper we proposed a modified survival model. This model is considering MTBF as its core concept, and failed missiles in the model are to be repaired and redeployed. We compared the modified model (MTBF) and the previous model (MTTF) in terms of fitness against the real failure data. Results: The reliability prediction result of MTBF based model is closer to fields failure data set than that of MTTF based model. Conclusion: The proposed MTBF concept is more fitted to real failure data of missile than MTTF concept. The methodology of this study can be applied to analyze field failure data of other similar missiles.

Reliability Growth Planning for a Military System Using PM2-Continuous Model (예측방법론 기반 연속형 계획 모델을 적용한 무기체계의 신뢰도 성장 계획)

  • Seo, Yangwoo;Park, Eunshim;Kim, Youngkuk;Lee, Kwanyoung;Kim, Myungsoo
    • Journal of Applied Reliability
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    • v.18 no.3
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    • pp.201-207
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    • 2018
  • Purpose: To develop the reliability growth planning for a subsystem of guided weapon system using PM2-Continuous model. Methods: The target MTBF of the subsystem is set by allocating the system target MTBF to the lower level, where ARINC method is applied. Other model parameters such as initial MTBF, management strategy ratio and average fix effectiveness factor are chosen from historical growth parameter estimates. Given the values of model parameters, the reliability growth planning curve using PM2-Continuous model is constructed and the sensitivity analyses are performed for the changes of model parameters. Results: We have developed the reliability growth plan for a subsystem of guided weapon system using PM2-Continuous model. It was found that the smaller the ratio of initial MTBF to target MTBF, the smaller the management strategy ratio, the smaller the average fix effectiveness factor, and the shorter the development test period, the higher reliability growth is required. Conclusion: The result of this study will be used as a basis for establishing the reliability growth plan, the test period setting and the budget appropriation for the similar system entering the system development stage in the future.

예방정비 체제하에서의 공정별 고장시간 간격분석

  • Kim, Chang-Hyun;Kim, Jong-Han
    • IE interfaces
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    • v.4 no.2
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    • pp.35-42
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    • 1991
  • Many authors derived MTTF/MTBF for an operating system by analyzing its actual life time data. However, it is difficult to derive MTTF/MTBF when few breakdowns accur throughout a year. In this paper, we address a new approach to solve that problem under a preventive maintenance policy, in which few breakdowns occur, and also introduce a case study using the results obtained.

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Heuristic Method for RAM Design of Multifunctional System (다기능 시스템의 RAM 목표값 설정을 위한 휴리스틱 기법)

  • Han, Young-Jin;Kim, Hee-Wook;Yun, Won-Young;Kim, Jong-Woon
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.36 no.2
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    • pp.157-164
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    • 2012
  • When designing a multifunctional system consisting of many components performing many functions or missions, it is important to determine the reliability, availability, and maintainability (RAM) of the system and components, and we consider system availability to be the optimization criterion. For given intervals of mean time between failure (MTBF) and mean time to repair (MTTR) of the components, we want to determine the values of MTBF and MTTR for all components that satisfy the target availability. A heuristic method is proposed for finding near-optimal solutions through simulation. We also study numerical examples to check effects of model parameters on the optimal solutions.