1.1本指南简要介绍了一些公认的统计分析方法，这些方法有助于解释加速使用寿命数据。其目的是产生一个通用术语，并开发与使用寿命估计有关的通用方法和定量表达式。 1.2本指南涵盖了阿伦尼乌斯方程在使用寿命数据中的应用。它可作为确定使用条件（如温度）下速率的通用模型。它是确定使用条件下使用寿命分布的一般指南。 它还涵盖了多个变量同时作用以影响使用寿命的应用。在本指南中，用于多个应力变量的加速度模型是Eyring模型。该模型由热力学基本定律推导而来，已被证明可用于模拟一些双变量加速使用寿命数据。它可以扩展到两个以上的变量。 1.3本指南仅考虑在使用寿命数据分析中广泛接受的统计方法。 1.4本指南强调了威布尔寿命分布，并详细介绍了使用寿命数据分析中常见情况的示例计算。本指南旨在与指南一起使用 G166 . 1.5随着变量数量的增加和/或从加速应力水平到使用水平的外推程度的增加，模型的准确性变得更为关键。本指南中使用的模型和方法仅供数据分析技术使用。 仍然必须满足适当变量选择和测量的基本要求，才能产生有意义的模型。 ====意义和用途====== 加速使用寿命估算的性质通常要求对被评估材料施加高于使用条件下经历的应力。对于非恒定使用应力，例如室外时变天气所经历的应力，实际上，选择一个固定在略低于（比如90%）室外最大应力水平的加速应力可能是有用的。 通过控制除用于加速降解的变量外的所有变量，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试验装置，则必须对所用变量进行精确控制，以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。必须注意的是，该假设的有效性对最终估计的有效性至关重要。 加速使用寿命试验数据通常显示出与许多其他类型数据不同的分布形状。这是由于测量误差（通常为正态分布）的影响，以及将使用寿命数据向早期故障时间（婴儿死亡率故障）或晚期故障时间（老化或磨损故障）倾斜的独特影响。本指南中原则的应用有助于研究人员解释此类数据。 特定加速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的外推时是否会导致合理的预测。 选择模型的进一步理由应基于理论考虑。 笔记 2-加速使用寿命或可靠性数据分析包在通用计算机软件包中变得更容易获得。这使得越来越多的研究人员能够更直接地进行数据简化和分析。这不一定是一件好事，因为如果没有对力学的基本理解，执行数学计算的能力可能会产生一些严重的错误。参见Ref (1) .

1.1 This guide briefly presents some generally accepted methods of statistical analyses that are useful in the interpretation of accelerated service life data. It is intended to produce a common terminology as well as developing a common methodology and quantitative expressions relating to service life estimation. 1.2 This guide covers the application of the Arrhenius equation to service life data. It serves as a general model for determining rates at usage conditions, such as temperature. It serves as a general guide for determining service life distribution at usage condition. It also covers applications where more than one variable act simultaneously to affect the service life. For the purposes of this guide, the acceleration model used for multiple stress variables is the Eyring Model. This model was derived from the fundamental laws of thermodynamics and has been shown to be useful for modeling some two variable accelerated service life data. It can be extended to more than two variables. 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 is emphasized in this guide and example calculations of situations commonly encountered in analysis of service life data are covered in detail. It is the intention of this guide that it be used in conjunction with Guide G166 . 1.5 The accuracy of the model becomes more critical as the number of variables increases and/or the extent of extrapolation from the accelerated stress levels to the usage level increases. The models and methodology used in this guide are shown for the purpose of data analysis techniques only. The fundamental requirements of proper variable selection and measurement must still be met for a meaningful model to result. ====== Significance And Use ====== 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 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. See Ref (1) .