绝热压缩试验的统计考虑

Barry Newton, T. Steinberg
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摘要

ASTM G74多年来一直用于评估用于氧气服务的非金属材料和部件。1982年最初发布时,该标准认为“合格”的结果是在20个测试样品中材料的零点火。然而,研究人员已经认识到,最初规定的方法导致的累积二项置信度约为36%的合格结果。因此,除非通过可用的统计方法对数据进行分析,否则通过结果的低置信度可能会对用于氧气服务的材料或部件的合格性产生潜在的误导。本文总结了评估气体流体冲击试验统计方面的研究,以便在试验方法中考虑点火概率。测试方法得出的数据通过逻辑回归方法进行评估,以描述被测试材料的行为,并比较不同的材料或测试条件。因此,测试的统计方面对理解和应用所获得的数据至关重要。本文论证了ASTM G74试验和所有具有类似性质的国际试验,因为它们都使用相同的试验实施例,并且具有固有的概率性和可变性,如果不应用适当的统计分析,这些可变性似乎是随机的。然而,当使用适当的统计工具时,可以得出有意义的结果。逻辑回归分析是分析二项数据(点火/不点火)的唯一方法;但它是一个强大的工具,可以帮助揭示数据的趋势,这些趋势有时被看似随机的行为所掩盖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical Considerations for Adiabatic Compression Testing
ASTM G74 has been used for many years to evaluate nonmetallic materials and components for oxygen service. When originally published in 1982, this standard considered a “passing” result to be zero ignitions of a material out of 20 samples tested. However, researchers have recognized that the originally prescribed methodology results in a cumulative binomial confidence of about 36 % for a passing result. As a result, the low confidence for a passing result could be potentially misleading when results are used to qualify materials or components for oxygen service, unless the data is analyzed through available statistical approaches. This paper summarizes research performed to evaluate the statistical aspects of gaseous fluid impact testing so that ignition probabilities can be considered in the test methodology. Data derived by the test method are evaluated by a logistic regression approach in order to describe the behavior of the materials being tested and to compare different materials or test conditions. Therefore, the statistical aspects of the test are shown to be crucial to understanding and applying the data obtained. This paper demonstrates that the ASTM G74 test and all international tests of a similar nature because all use the same test embodiment and are inherently probabilistic and subject to variability that seems random without application of appropriate statistical analysis. However, meaningful results can be developed when the appropriate statistical tools are utilized. Logistic regression analysis is only one available method to analyze binomial data (ignition/no-ignition); but it is a powerful tool that can help to bring clarity to the trends in data that are obscured by sometimes seemingly random behavior.
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