Browsing by Subject "Weibull distribution"
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Item A comparison of tolerance distributions in bioassay(Texas Tech University, 1997-05) Gomez, ElviaBioassay deals with the problem of determining the potency of a stimulus by analyzing the responses it produces in biological organisms, such as experimental animals, plants, bacteria, etc. The same methods used to determine the potency of a stimulus in biological organisms can be used to determine the sensitivity of non-biological organisms to various stresses and will be discussed in more detail in Chapter III. Nevertheless, responses vary in type and intensity from one member of the test population to the next. Each test subject, biological or non-biological, has a threshold or critical stress level, which must be exceeded in order to produce the response of interest. For example, in testing the potency of an insecticide, there is a certain dosage above which an insect will die and below which it will not. In testing the sensitivity of explosives, there is a certain height above which a dropped explosive will detonate, and below which it will not. This gives rise to the concept of a continuous distribution function F, which models the response of interest. The distributions used to model the responses are referred to in the literature as tolerance distributions and are described in detail in Chapter IV. This paper will begin to compare how well five specific distributions; the normal, logistic, WeibuU, Gumbel, and Gompertz, fit quantal-response data in sensitivity experiments, which readily apply the methods of bioassay to non-biological investigations, as those dealing with explosives.Item Bayes and empirical Bayes estimations of reliability for the Weibull model.(Texas Tech University, 1975-05) Lian, Mingwei GeorgeNot availableItem Equipment data analysis study : failure time data modeling and analysis(2012-05) Zhu, Chen, master of science in engineering; Popova, Elmira; Bickel, J.EricThis report presents the descriptive data analysis and failure time modeling that can be used to find out the characteristics and pattern of failure time. Descriptive data analysis includes the mean, median, 1st quartile, 3rd quartile, frequency, standard deviation, skewness, kurtosis, minimum, maximum and range. Models like exponential distribution, gamma distribution, normal distribution, lognormal distribution, Weibull distribution and log-logistic distribution have been studied for failure time data. The data in this report comes from the South Texas Project that was collected during the last 40 years. We generated more than 1000 groups for STP failure time data based on Mfg Part Number. In all, the top twelve groups of failure time data have been selected as the study group. For each group, we were able to perform different models and obtain the parameters. The significant level and p-value were gained by Kolmogorov-Smirnov test, which is a method of goodness of fit test that represents how well the distribution fits the data. The In this report, Weibull distribution has been proved as the most appropriate model for STP dataset. Among twelve groups, eight groups come from Weibull distribution. In general, Weibull distribution is powerful in failure time modeling.Item Reliability analysis for nonrepairable systems subject to dormancy(Texas Tech University, 1985-05) Landers, Thomas Lee.Item Reliability prediction of the series system with spares subject to Weibull failure(Texas Tech University, 1984-12) Pan, Jeh-nanThis research effort has developed a mathematical model for predicting the system reliability of a series system with spares subject to Weibull failures. Many modern equipment systems are designed in a series configuration with spares available when necessary. The Weibull distribution is one of the most widely applied and flexible failure distributions used to describe and model present day single component systems. Due to the theoretical intractability of the Weibull distribution in spares models, it is extremely difficult to solve the multiple integration involved in this type of model analytically. Therefore, a numerical integration method using Simpson's Rule was selected as a tool to address the problem of multiple integration of the Weibull distribution. Then, a recursive algorithm was developed for the reliability prediction of a series system with spares subject to a general failure model (including the Weibull distribution). The developed model has been validated for accuracy using a two component series system with spares. A sensitivity analysis has also been performed on the two parameters of the Weibull distribution, and the effect of adding spares or stages on the system reliability has been assessed. This research also demonstrates the application of the developed reliability prediction model with a realistic example pertaining to a tool reliability problem common to manufacturing industries.Item Weathered window glass strength evaluation using the ring-on-ring tester(Texas Tech University, 1986-12) McFarquhar, Dudley GeorgeNot availableItem Weibull parameter estimation from noisy failure data(Texas Tech University, 1975-05) Ezzat, Mohamed OmarNot available