ASTM International - ASTM G166-00(2011)
Standard Guide for Statistical Analysis of Service Life Data
|Publication Date:||1 July 2011|
|ICS Code (Product life-cycles):||13.020.60|
significance And Use:
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... View More
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 (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.
Note 2-Service life or reliability data analysis packages are becoming more readily available in standard or common computer software packages. This puts data reduction and analyses more readily into the hands of a growing number of investigators.View Less
1.1 This guide presents briefly some generally accepted methods of statistical analyses which are useful in the interpretation of 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 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 encountered in analysis of service life data are covered in detail.
1.5 The choice and use of a 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.