1.1 本指南介绍了加速使用寿命数据分析的一般统计方法。它为计算功能使用寿命的定量估计提供了通用术语和通用方法。 1.2 本指南介绍了确定使用条件下使用寿命分布的两个通用模型的应用。Arrhenius模型是一个通用模型，其中单个应力变量，特别是温度，会影响使用寿命。它还涵盖了Eyring模型，适用于多个应力变量同时作用影响使用寿命的应用。 1.3 本指南强调威布尔寿命分布的使用，旨在与指南结合使用 G166 . 1.4 随着应力变量数量的增加以及从加速应力水平到使用水平的外推程度的增加，或两者兼而有之，每个加速使用寿命模型的不确定性和可靠性变得更加关键。 本指南中使用的模型和方法仅提供数据分析技术的示例。用户仍然必须满足适当的变量选择和测量的基本要求，才能产生有意义的模型。 1.5 本国际标准是根据世界贸易组织技术性贸易壁垒委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认的标准化原则制定的。 ====意义和用途====== 4.1 加速使用寿命估计的性质通常要求将高于使用条件下所经历的应力的应力应用于所评估的材料。 对于非恒定的使用压力，例如户外时变天气所经历的压力，事实上，选择固定在略低于户外最大压力水平（例如90%）的加速压力可能是有用的。通过控制除用于加速降解的变量之外的所有变量，可以对该变量在正常或使用条件下的预期效果进行建模。如果使用实验室加速测试设备，则必须对所使用的变量进行精确控制，以便获得用于寿命预测的有用信息。假设在较高应力下运行的相同失效机制也是在使用应力下的寿命决定机制。必须指出的是，这一假设的有效性对最终估计的有效性至关重要。 4.2 加速使用寿命测试数据通常显示出与许多其他类型的数据不同的分布形状。这是由于测量误差的影响（通常是正态分布的），再加上那些独特的影响，这些影响使使用寿命数据偏向早期故障时间（婴儿死亡率故障）或晚期故障时间（老化或磨损故障）。应用本指南中的原则有助于研究人员解释此类数据。 4.3 特定加速度模型和寿命分布模型的选择和使用应主要基于其与数据的拟合程度，以及在超出数据范围进行外推时是否能得出合理的预测。选择模型的进一步理由应基于理论考虑。 注2: 在通用计算机软件包中，加速使用寿命或可靠性数据分析包变得越来越容易获得。这使得越来越多的研究人员能够更直接地访问数据缩减和分析。这不一定是一件好事，因为如果没有对力学的基本理解，进行数学计算的能力可能会产生一些严重的错误。 3.

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. ====== Significance And Use ====== 4.1 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. 4.2 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. 4.3 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 2: 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. 3