ASTM G172 加速運(yùn)行的壽命數(shù)據(jù)的統(tǒng)計(jì)分析指南

ASTM G172-19發(fā)行信息

標(biāo)準(zhǔn)號(hào)ASTM G172-19

中文名加速運(yùn)行的壽命數(shù)據(jù)的統(tǒng)計(jì)分析指南

英文名 Standard Guide for Statistical Analysis of Accelerated Service Life Data

發(fā)布日期2019

實(shí)施日期

廢止日期無

中國標(biāo)準(zhǔn)分類號(hào)A41

國際標(biāo)準(zhǔn)分類號(hào)03.120.30

發(fā)布單位US-ASTM

ASTM G172-19適用范圍

The nature of accelerated service life estimation normally requires that stresses higher than those experienced during service conditions are applied to the material being evaluated. For non-constant use stress, such as experienced by time varying weather outdoors, it may in fact be useful to choose an accelerated stress fixed at a level slightly lower than (say 90 % of) the maximum experienced outdoors. By controlling all variables other than the one used for accelerating degradation, one may model the expected effect of that variable at normal, or usage conditions. If laboratory accelerated test devices are used, it is essential to provide precise control of the variables used in order to obtain useful information for service life prediction. It is assumed that the same failure mechanism operating at the higher stress is also the life determining mechanism at the usage stress. It must be noted that the validity of this assumption is crucial to the validity of the final estimate.

Accelerated service life test data often show different distribution shapes than many other types of data. This is due to the effects of measurement error (typically normally distributed), combined with those unique effects which skew service life data towards early failure time (infant mortality failures) or late failure times (aging or wear-out failures). Applications of the principles in this guide can be helpful in allowing investigators to interpret such data.

The choice and use of a particular acceleration model and 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 models should be based on theoretical considerations.

Note 28212;Accelerated service life or reliability data analysis packages are becoming more readily available in common computer software packages. This makes data reduction and analyses more directly accessible to a growing number of investigators. This is not necessarily a good thing as the ability to perform the mathematical calculation, without the fundamental understanding of the mechanics may produce some serious errors. See Ref (1).

1.1 This guide describes general statistical methods for analyses of accelerated service life data. It provides a common terminology and a common methodology for calculating a quantitative estimate of functional service life.

1.2 This guide covers the application of two general models for determining service life distribution at usage condition. The Arrhenius model serves as a general model where a single stress variable, specifically temperature, affects the service life. It also covers the Eyring Model for applications where multiple stress variables act simultaneously to affect the service life.

1.3 This guide emphasizes the use of the Weibull life distribution and is written to be used in combination with Guide G166.

1.4 The uncertainty and reliability of every accelerated service life model becomes more critical as the number of stress variables increases and the extent of extrapolation from the accelerated stress levels to the usage level increases, or both. The models and methodology used in this guide are to provide examples of data analysis techniques only. The fundamental requirements of proper variable selection and measurement must still be met by the users for a meaningful model to result.

1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

1.1本指南描述了加速使用壽命數(shù)據(jù)分析的一般統(tǒng)計(jì)方法。它為計(jì)算功能使用壽命的定量估計(jì)提供了通用術(shù)語和通用方法。

1.2本指南涵蓋了確定使用條件下使用壽命分布的兩種通用模型的應(yīng)用。Arrhenius模型作為一般模型，其中單個(gè)應(yīng)力變量，特別是溫度，影響使用壽命。它還涵蓋了Eyring模型，適用于多個(gè)應(yīng)力變量同時(shí)作用以影響使用壽命的應(yīng)用。

1.3本指南強(qiáng)調(diào)使用威布爾壽命分布，并與指南G166結(jié)合使用。

1.4隨著應(yīng)力變量數(shù)量的增加以及從加速應(yīng)力水平到使用水平的外推范圍的增加，或兩者同時(shí)增加，每個(gè)加速使用壽命模型的不確定性和可靠性變得更加關(guān)鍵。本指南中使用的模型和方法僅提供數(shù)據(jù)分析技術(shù)的示例。用戶仍然必須滿足適當(dāng)變量選擇和測(cè)量的基本要求，才能得到有意義的模型。

1.5本國際標(biāo)準(zhǔn)是根據(jù)世界貿(mào)易組織技術(shù)性貿(mào)易壁壘（TBT）委員會(huì)發(fā)布的《關(guān)于制定國際標(biāo)準(zhǔn)、指南和建議的原則的決定》中確立的國際公認(rèn)標(biāo)準(zhǔn)化原則制定的。