• Transactions on Electrical and Electronic Materials
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• v.7 no.5
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• pp.217-223
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• 2006

When only partial information is available about equipment failures (installation date and amount, as well as failure and replacement rates), data on sufficiently large number of yearly populations of the components can be combined, and estimation of model parameters may be possible. The parametric models may then be used for forecasting of the system's short term future failure and for formulation of replacement strategies. We employ the Weibull distribution and show how we estimate its parameters from past failure data. Using Monte Carlo simulations, it is possible to assess confidence ranges of the forecasted component performance data.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.311-316
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• 2016

Breast cancer is one of the most common causes of cancer mortality in Iran. Social determinants of health are among the key factors affecting the pathogenesis of diseases. This cross-sectional study aimed to determine the social determinants of breast cancer survival time with parametric and semi-parametric regression models. It was conducted on male and female patients diagnosed with breast cancer presenting to the Cancer Research Center of Shohada-E-Tajrish Hospital from 2006 to 2010. The Cox proportional hazard model and parametric models including the Weibull, log normal and log-logistic models were applied to determine the social determinants of survival time of breast cancer patients. The Akaike information criterion (AIC) was used to assess the best fit. Statistical analysis was performed with STATA (version 11) software. This study was performed on 797 breast cancer patients, aged 25-93 years with a mean age of 54.7 (${\pm}11.9$) years. In both semi-parametric and parametric models, the three-year survival was related to level of education and municipal district of residence (P<0.05). The AIC suggested that log normal distribution was the best fit for the three-year survival time of breast cancer patients. Social determinants of health such as level of education and municipal district of residence affect the survival of breast cancer cases. Future studies must focus on the effect of childhood social class on the survival times of cancers, which have hitherto only been paid limited attention.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.445-461
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• 2019

It is possible that data are not always fitted with sufficient precision by the existing distributions; therefore this article presents a methodology that enables the use of families of asymmetric distributions as alternative probabilistic models for survival analysis, with censorship on the right, different from those usually studied (the Exponential, Gamma, Weibull, and Lognormal distributions). We use a more flexible parametric model in terms of density behavior, assuming that data can be fit by a distribution of stable distribution families considered unconventional in the analyses of survival data that are appropriate when extreme values occur, with small probabilities that should not be ignored. In the methodology, the determination of the analytical expression of the risk function h(t) of the $L{\acute{e}}vy$ distribution is included, as it is not usually reported in the literature. A simulation was conducted to evaluate the performance of the candidate distribution when modeling survival times, including the estimation of parameters via the maximum likelihood method, survival function ${\hat{S}}$(t) and Kaplan-Meier estimator. The obtained estimates did not exhibit significant changes for different sample sizes and censorship fractions in the sample. To illustrate the usefulness of the proposed methodology, an application with real data, regarding the survival times of patients with colon cancer, was considered.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.6
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• pp.2037-2047
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• 1991

Strength is not simply a single given value but rather is a statistical one with certain distribution functions. This is because it is affected by many unknown factors such as size, shape, stress distribution, and combined stresses. In this study, a model of loss probability is proposed in view of the fact that one of the fundamental configuration of nature is hexagonal, for example, the shapes of lattice unit, grain, and so on. The model sues the concept of loss of certain element in place of Jayatilaka-Trustrum's length and angle of cracks. Using this model, the loss probability due to each loss of certain elements is obtained. Then, the maximum principal stress is calculated by the finite element method at the centroid of the elements under the tensile load for the 4,095 models of analysis. Finally, the failure probability of the brittle materials is obtained by multiplying the loss probability by the ratio of the maximum principal stress to theoretical tensile strength. Comparison of the result of the Jayatilaka-Trustrum's model and the proposed model shows that the failure probabilities by the two methods are in good agreement. Further, it is shown that the parametric relationship of semi-crack lengths for various degrees of birittleness can be determined. Therefore, the analysis of the failure probability suing the proposed model is shown to be promising as a new method for the study of the failure probability of birttle materials.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.151-165
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• 1997

We propose a method for estimating the probability of perfect PM from successive failure times of a repairable system. The system under study is maintained preventively at periodic times, and it undergoes minimal repair at failure. We consider Brown-Proschan imperfect PM model in which the system is restored to a condition as good as new with probability P and is otherwise restored to its condition just prior to failure. We discuss the identifiability problem when the PM modes are not recorded. The expectation-maximization principle is employed to handle the incomplete data problem. We assume that the lifetime distribution belongs to a parametric family with increasing failure rate. For the two parameter Weibull lifetime distribution, we propose a specific algorithm for finding the maximum lifelihood estimates of the reliability parameters : the probability of perfect PM (P), as well as the distribution parameters. The estimation method will provide useful results for maintaining real systems.

• Journal of the Korean Society of Safety
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• v.29 no.1
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• pp.7-14
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• 2014

Inverter module, which feeds the converted power to the traction motor for EMU. Consists of the power semiconductors with their gate drive unit(GDU)s and the control computer for driving, voltage, current and speed controls. Electrolytic condenser, connected to the gate drive unit and a core component to drive the power semiconductor, has problems such as reduction in lifetime and malfunction caused by electrical and mechanical characteristic changes from heat generation during high speed switching for generation of stable power. In this study, To check the service life of electrolytic condenser, the test was carried out in two ways. First, In the case of accelerated life testing of condenser, the Arrhenius model is a way of life testing. Another way is to analyze the reliability of the failure data by the method of parametric data analysis. Eventually, life time by accelerated life test than a method of failure data analysis(Weibull distribution) was found to be slightly larger output.