ASTM G172-19
(Guide)Standard Guide for Statistical Analysis of Accelerated Service Life Data
Standard Guide for Statistical Analysis of Accelerated Service Life Data
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 direct...
SCOPE
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.
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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.
Designation: G172 − 19
Standard Guide for
1
Statistical Analysis of Accelerated Service Life Data
This standard is issued under the fixed designation G172; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision.Anumber in parentheses indicates the year of last reapproval.A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope 3. Terminology
1.1 This guide describes general statistical methods for 3.1 Terms Commonly Used in Service Life Estimation:
analyses of accelerated service life data. It provides a common 3.1.1 accelerated stress, n—a stress variable, such as tem-
terminology and a common methodology for calculating a perature or irradiance, applied to the test material at levels
quantitative estimate of functional service life. intensified over those encountered in the service environment.
3.1.2 F(t),n—theprobabilitythatarandomunitdrawnfrom
1.2 Thisguidecoverstheapplicationoftwogeneralmodels
the population will fail by time (t).
fordeterminingservicelifedistributionatusagecondition.The
Arrhenius model serves as a general model where a single
3.1.2.1 Discussion—Also F(t) = the decimal fraction of
stressvariable,specificallytemperature,affectstheservicelife.
units in the population that will fail by time (t). The decimal
ItalsocoverstheEyringModelforapplicationswheremultiple
fraction multiplied by 100 is numerically equal to the percent
stress variables act simultaneously to affect the service life.
failure by time (t).
1.3 This guide emphasizes the use of the Weibull life
3.1.3 usage stress, n—the level of the experimental variable
distribution and is written to be used in combination with
that is considered to represent the stress occurring in normal
Guide G166.
use.
1.4 The uncertainty and reliability of every accelerated
3.1.3.1 Discussion—This value must be determined quanti-
service life model becomes more critical as the number of
tatively for accurate estimates to be made. In actual practice,
stress variables increases and the extent of extrapolation from
usage stress may be highly variable, such as those encountered
the accelerated stress levels to the usage level increases, or
in outdoor environments.
both. The models and methodology used in this guide are to
3.1.4 Weibulldistribution,n—forthepurposesofthisguide,
provide examples of data analysis techniques only. The funda-
the Weibull distribution is represented by the equation:
mental requirements of proper variable selection and measure-
t b
2S D
ment must still be met by the users for a meaningful model to F~t! 51 2 e c (1)
result.
where:
1.5 This international standard was developed in accor-
F(t) = probability of failure by time (t) as defined in 3.1.2,
dance with internationally recognized principles on standard-
t = units of time used for service life,
ization established in the Decision on Principles for the
c = scale parameter, and
Development of International Standards, Guides and Recom-
b = shape parameter.
mendations issued by the World Trade Organization Technical
3.1.4.1 Discussion—The shape parameter (b), 3.1.4,isso
Barriers to Trade (TBT) Committee.
called because this parameter determines the overall shape of
2. Referenced Documents the curve. Examples of the effect of this parameter on the
distribution curve are shown in Fig. 1.
2
2.1 ASTM Standards:
3.1.4.2 Discussion—The scale parameter (c), 3.1.4,isso
G166Guide for Statistical Analysis of Service Life Data
called because it positions the distribution along the scale of
the time axis. It is equal to the time for 63.2% failure.
1
This guide is under the jurisdiction of ASTM Committee G03 on Weathering
NOTE 1—This is arrived at by allowing t to equal c in Eq 1. This then
and Durability and is the direct responsibility of Subcommittee G03.08 on Service
-1
reduces to Failure Probability=1− e , which further reduces to equal 1
Life Prediction.
− 0.368 or 0.632.
Current edition approved Jan. 1, 2019. Published February 2019. Originally
Ɛ1
approved in 2002. Last previous edition approved in 2010 as G172-02(2010) .
4. Significance and Use
DOI: 10.1520/G0172-19.
2
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
4.1 The nature of accelerated service life estimation nor-
contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
mally requires that stresses higher than those experienced
Standards volume information, refer to the standard’s Document Summary page on
the ASTM website. during service conditions are applied to the material being
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box
...
This document is not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
´1
Designation: G172 − 02 (Reapproved 2010) G172 − 19
Standard Guide for
1
Statistical Analysis of Accelerated Service Life Data
This standard is issued under the fixed designation G172; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1
ε NOTE—Editorially corrected designation and footnote 1 in November 2013
1. Scope
1.1 This guide briefly presents some generally accepted methods of statistical analyses that are useful in the interpretation
describes general statistical methods for analyses of accelerated service life data. It is intended to produce provides a common
terminology as well as developing and a common methodology and quantitative expressions relating to service life estimation.for
calculating a quantitative estimate of functional service life.
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. 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 applications where more than
one variable the Eyring Model for applications where multiple stress variables 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.
1.3 The This guide emphasizes the use of 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 and
is written to be used in conjunctioncombination with Guide G166.
1.4 The accuracy of the uncertainty and reliability of every accelerated service life model becomes more critical as the number
of stress variables increases and/orand the extent of extrapolation from the accelerated stress levels to the usage level increases.
increases, or both. The models and methodology used in this guide are shown for the purpose 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.
2. Referenced Documents
2
2.1 ASTM Standards:
G166 Guide for Statistical Analysis of Service Life Data
G169 Guide for Application of Basic Statistical Methods to Weathering Tests
3. Terminology
3.1 Terms Commonly Used in Service Life Estimation:
1
This guide is under the jurisdiction of ASTM Committee G03 on Weathering and Durability and is the direct responsibility of Subcommittee G03.08 on Service Life
Prediction.
Current edition approved July 1, 2010Jan. 1, 2019. Published July 2010February 2019. Originally approved in 2002. Last previous edition approved in 20022010 as
Ɛ1
G172 - 02.G172 - 02(2010) . DOI: 10.1520/G0172-02R10.10.1520/G0172-19.
2
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
1
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G172 − 19
3.1.1 accelerated stress, n—that experimentala stress variable, such as temperature, which istemperature or irradiance, applied
to the test material at leve
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