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ASTM G166-00(R2020) pdf free download

ASTM G166-00(R2020) pdf free download.Standard Guide for Statistical Analysis of Service Life Data
1. Scope
1.1 This guide presents briefly some generally accepted methods of statistical analyses which are useful in the inter- pretation of service life data. It is intended to produce a common terminology as well as developing a common meth- odology and quantitative expressions relating to service life estimation. 1.2 This guide does not cover detailed derivations, or special cases, but rather covers a range of approaches which have found application in service life data analyses. 1.3 Only those statistical methods that have found wide acceptance in service life data analyses have been considered in this guide. 1.4 The Weibull life distribution model is emphasized in this guide and example calculations of situations commonly en- countered in analysis of service life data are covered in detail. 1.5 The choice and use ofa particular life distribution model should be based primarily on how well it fits the data and whether it leads to reasonable projections when extrapolating beyond the range of data. Further justification for selecting a model should be based on theoretical considerations. 1.6 This international standard was developed in accor- dance with internationally recognized principles on standard- ization established in the Decision on Principles for the Development of International Standards, Guides and Recom- mendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
3. Terminology
3.1 Definitions: 3.1.1 material property—customarily, service life is consid- ered to be the period of time during which a system meets critical specifications. Correct measurements are essential to producing meaningful and accurate service life estimates. Discussion—There exists many ASTM recognized and standardized measurement procedures for determining material properties. As these practices have been developed within committees with appropriate expertise, no further elabo- ration will be provided. 3.1.2 beginning oflife—this is usually determined to be the time of manufacture. Exceptions may include time of delivery to the end user or installation into field service. 3.1.3 end of life—Occasionally this is simple and obvious such as the breaking of a chain or burning out of a light bulb filament. In other instances, the end of life may not be so catastrophic and free from argument. Examples may include fading, yellowing, cracking, crazing, etc. Such cases need quantitative measurements and agreement between evaluator and user as to the precise definition offailure. It is also possible to model more than one failure mode for the same specimen. (For example, the time to produce a given amount ofyellowing may be measured on the same specimen that is also tested for cracking.) 3.1.4 F(t)—The probability that a random unit drawn from the population will fail by time (t). Also F(t) = the decimal fraction of units in the population that will fail by time (t). The decimal fraction multiplied by 100 is numerically equal to the percent failure by time (t). The shape parameter (b), section 3.1.6, is so called because this parameter determines the overall shape of the curve. Examples of the effect of this parameter on the distri- bution curve are shown in Fig. 1, section 5.3. The scale parameter (c), section 3.1.6, is so called because it positions the distribution along the scale of the time axis. It is equal to the time for 63.2 % failure. N OTE 1—This is arrived at by allowing t to equal c in the above expression. This then reduces to Failure Probability = 1−e −1 , which further reduces to equal 1−0.368 or .632. 3.1.9 complete data—Acomplete data set is one where all of the specimens placed on test fail by the end ofthe allocated test time. 3.1.10 Incomplete data—An incomplete data set is one where (a) there are some specimens that are still surviving at the expiration of the allowed test time, (b) where one or more specimens is removed from the test prior to expiration of the allowed test time. The shape and scale parameters of the above distributions may be estimated even if some of the test specimens did not fail. There are three distinct cases where this might occur.

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