Work Item
ASTM WK88010
New Guide for Statistical Analysis of Fatigue-Life and Fatigue-Strength
1. Scope
1.1 This guide presents methodological options to analyze experimental fatigue data for the estimation of fatigue life and/or fatigue strength.
1.2 These techniques enable the use of nonlinear S-N relationships.
1.3 This guide covers Maximum Likelihood and Bayesian computational techniques.
1.4 The techniques are applicable to data containing runouts.
1.5 This guide is not a test method. Although the guide provides a procedure for the implementation of modern fatigue data analysis methods, it does not establish a standard practice to follow in all cases.
1.6 It is intended that users of this guide will implement the portions of the guide that are found relevant, without any implication of adhering to the entirety of the content.
1.7 This guide provides a general framework for fatigue data analysis. For the exposition of examples, it utilizes commonly used S-N relationships and statistical distributions. The framework is applicable to other relationships and statistical distributions, which may be substituted as justified.
1.8 Fatigue experimental methods are beyond the scope of this guide.
1.9 The effects of variable amplitude cycling, and mean stress is not covered.
1.10 This guide does not cover ordinary least squares techniques as they are incompatible with estimation when data contain runouts. In addition, least squares techniques do not have an established, accepted statistical method for calculating the uncertainty of estimates.
1.11 Units - The values stated in SI units are to be regarded as the standard. No other units of measurement are included in this standard.
1.12 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.
Keywords
Fatigue statistics; Censored data; Runout; Bayesian inference; Maximum likelihood; Nonlinear regression; Reliability; S-N; e-N
Rationale
• “Current” standards are based on statistical methods developed from the late 1940s to the late 1960s - unable to handle censored data (runouts) and nonlinear regression.
• Modern statistical methods and the availability of computational power allow engineers to fit needed nonlinear regression models and properly handle runouts.
The title and scope are in draft form and are under development within this ASTM Committee.
Work Item Status
Date Initiated: 09-29-2023
Technical Contact: Wayne Falk
Item: 000
Ballot:
Status: