• Title/Summary/Keyword: type II censoring

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Calculating Mean Life of Generators with Aging failures Data Using Data Analytic Method (통계적 분석 방법을 이용한 발전설비의 평균수명 계산)

  • Lee Sung Hoon;Lee Seung Hyuk;Kim Jin O;Jeon Dong Hoon;Kim Tae Kyun
    • Proceedings of the KIEE Conference
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    • summer
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    • pp.464-466
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    • 2004
  • This paper proposes a method to consider an aging failure probability and survival probability of power system components unlike uses only aging failure probability 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 II Censoring). The parameter estimation using Data Analytic Method is simpler and faster than a traditional calculation method. This paper shows how to calculate the mean life and its standard deviation by the proposed method and illustrates a exactness using real historical records of generator utilities in korea.

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Failure Probability Prediction based on probabilistic and stochastic methods in generating units (확률 통계적 기법을 이용한 발전설비 고장확률 예측)

  • Lee, Sung-Hoon;Lee, Seung-Hyuk;Kim, Jin-O;Cha, Seung-Tae;Kim, Tae-Kyun
    • Proceedings of the KIEE Conference
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    • 2004.11b
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    • pp.69-71
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    • 2004
  • This paper presents a method to predict failure probability related to aging. To calculate failure probability, the Weibull distribution is used due to age-related reliability. The Weibull distribution has shape and scale parameters. Each estimated parameter is obtained from Data Analytic Method (Type II Censoring) which is relatively simpler and faster than the traditional calculation ways for estimating parameters. 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 age-related reliability index.

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GENERALIZED LINDLEY DISTRIBUTION USING PROPORTIONAL HAZARD FAMILY AND INFERENCE OF FAILURE TIME DATA

  • Ahmed AL-Adilee;Hawraa A. AL-Challabi;Hassanein Falah;Dalael Saad Abdul-Zahra
    • Nonlinear Functional Analysis and Applications
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    • v.28 no.3
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    • pp.793-800
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    • 2023
  • In this paper, we propose a generalization of Lindley distribution (GLD) via a special structure that is concern with progressively Type-II right censoring and time failure data. We study the modern properties that we have built by such combination, for example, survival function, hazard function, moments, and estimation by non-Bayesian methods. Application on some selected data related to Lindley distribution (LD) and (ED) have been employed to find out the best distribution that can fit data comparing with the GLD.

Optimum failure-censored step-stress partially accelerated life test for the truncated logistic life distribution

  • Srivastava, P.W.;Mittal, N.
    • International Journal of Reliability and Applications
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    • v.13 no.1
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    • pp.19-35
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    • 2012
  • This paper presents an optimum design of step-stress partially accelerated life test (PALT) plan which allows the test condition to be changed from use to accelerated condition on the occurrence of fixed number of failures. Various life distribution models such as exponential, Weibull, log-logistic, Burr type-Xii, etc have been used in the literature to analyze the PALT data. The need of different life distribution models is necessitated as in the presence of a limited source of data as typically occurs with modern devices having high reliability, the use of correct life distribution model helps in preventing the choice of unnecessary and expensive planned replacements. Truncated distributions arise when sample selection is not possible in some sub-region of sample space. In this paper it is assumed that the lifetimes of the items follow Truncated Logistic distribution truncated at point zero since time to failure of an item cannot be negative. Optimum step-stress PALT plan that finds the optimal proportion of units failed at normal use condition is determined by using the D-optimality criterion. The method developed has been explained using a numerical example. Sensitivity analysis and comparative study have also been carried out.

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