The use of reliability in the design of experiments with practical application
Journal of Al-Qadisiyah for Computer Science and Mathematics,
2015, Volume 7, Issue 2, Pages 43-57
AbstractMany concerned and pay attention to when the term reliability or estimate the reliability of existing product , we will examine how to improve the reliability of products and processes through the use of designed experiments, in such experiments, each unit is tested up to fail or are still working at the end of the experiment. If it fails to respond will be the time of failure .reliability and design of experiments (DOE) different types of threads. Reliability is character the product , and design of experiments is the advantage of access to knowledge and organization , but they share a midwife application on a range of product designs , and are also the most effective when used together as tools by professionals in disciplines such as design engineering and process engineering and even product purchase marketing . We have addressed in this paper combine design experiments and reliability to improve product . Using design of experiments is to know and determine the level of the factors affecting the reliability it is certain that there are specific factors affecting theReliability . In the theoretical side touched on some basic concepts about the design of experiments and reliability and evolve overlapping and the relationship between them , It was possible to derive the greatest natural function of the logarithmic as algorithm to program (-R-) used to estimate model parameters and factors affecting the time of the failure (product life) through likelihood ratio test , The practical side of the data recorded for the time of the failure of the two types of tires after exposure to various Quicken and sample number (51) Show from the lab records Tires Babylon in the province of Najaf to see the estimated values of the parameters ( β_0 ، β_1 ، β_2 ، σ ( and the limits of confidence and ratio test possible main factor (B & A) and statistical significance of this ratio and the level of significance (5% and 1%). have been identified variance for every the ability of a parameter [ Var((β ) ̂_0 ) ، Var((β ) ̂_1 ) ، Var((β ) ̂_2 ) ، Var((σ^' ) ̂ )] Add to estimate the covariance between each two parameters , through a matrix calculation Fisher (F) and (F^(-1)) depending on the logarithmic function of the possibility of the greatest .
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