Fatigue Life Distribution Estimation

D. Gary Harlow ()
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D. Gary Harlow: Lehigh University

A chapter in Reliability and Statistical Computing, 2020, pp 1-16 from Springer

Abstract: Abstract Modeling fatigue life is complex whether it is applied to structures or experimental programs. Through the years several empirical approaches have been utilized. Each approach has positive aspects; however, none have been acceptable for every circumstance. On many occasions the primary shortcoming for an empirical method is the lack of a sufficiently robust database for statistical modeling. The modeling is exacerbated for loading near typical operating conditions because the scatter in the fatigue lives is quite large. The scatter may be attributed to microstructure, manufacturing, or experimental inconsistencies, or a combination thereof. Empirical modeling is more challenging for extreme life estimation because those events are rare. The primary purpose herein is to propose an empirically based methodology for estimating the cumulative distribution functions for fatigue life, given the applied load. The methodology incorporates available fatigue life data for various stresses or strains using a statistical transformation to merge all the life data so that distribution estimation is more accurate than traditional approaches. Subsequently, the distribution for the transformed and merged data is converted using change-of-variables to estimate the distribution for each applied load. To assess the validity of the proposed methodology percentile bounds are estimated for the life data. The development of the methodology and its subsequent validation is illustrated using three sets of fatigue life data which are readily available in the open literature.

Keywords: Cycle dependent parameters; Data fusion; Fatigue life transformation; Weibull distribution (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-030-43412-0_1

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DOI: 10.1007/978-3-030-43412-0_1

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