Systematic statistical characterisation of stress-life datasets using 3-Parameter distributions

Joshua Hoole; Pia Sartor; Julian Booker; Jonathan Cooper; Xenofon V. Gogouvitis; R. Kyle Schmidt

doi:10.1016/j.ijfatigue.2019.105216

Systematic statistical characterisation of stress-life datasets using 3-Parameter distributions

Joshua Hoole^*, Pia Sartor, Julian Booker, Jonathan Cooper, Xenofon V. Gogouvitis, R. Kyle Schmidt

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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Abstract

The variability present in S-N datasets is typically characterised using probability distributions to enable the construction of Probability-S-N curves for design. 3-Parameter Log-Normal and Weibull distributions have been proposed as alternative distributions to the commonly assumed 2-Parameter Log-Normal distribution. This paper performs statistical characterisation of a 4340 steel S-N dataset from the Engineering Sciences Data Unit using a systematic methodology. The 3-Parameter Weibull distribution provided improved characterisation of the S-N dataset. Using a case study, it was also demonstrated that use of a 3-Parameter Weibull distribution can increase component safe-life values by 20% when compared to the 2-Parameter Log-Normal distribution.

Original language	English
Article number	105216
Number of pages	15
Journal	International Journal of Fatigue
Volume	129
Early online date	31 Jul 2019
DOIs	https://doi.org/10.1016/j.ijfatigue.2019.105216
Publication status	Published - 1 Dec 2019

Keywords

Fatigue Design
Probabilistic Analysis
S-N Curves
Statistics

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10.1016/j.ijfatigue.2019.105216

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@article{dec2664772f845549e2d31bf8e76aaa5,

title = "Systematic statistical characterisation of stress-life datasets using 3-Parameter distributions",

abstract = "The variability present in S-N datasets is typically characterised using probability distributions to enable the construction of Probability-S-N curves for design. 3-Parameter Log-Normal and Weibull distributions have been proposed as alternative distributions to the commonly assumed 2-Parameter Log-Normal distribution. This paper performs statistical characterisation of a 4340 steel S-N dataset from the Engineering Sciences Data Unit using a systematic methodology. The 3-Parameter Weibull distribution provided improved characterisation of the S-N dataset. Using a case study, it was also demonstrated that use of a 3-Parameter Weibull distribution can increase component safe-life values by 20\% when compared to the 2-Parameter Log-Normal distribution.",

keywords = "Fatigue Design, Probabilistic Analysis, S-N Curves, Statistics",

author = "Joshua Hoole and Pia Sartor and Julian Booker and Jonathan Cooper and Gogouvitis, \{Xenofon V.\} and Schmidt, \{R. Kyle\}",

year = "2019",

month = dec,

day = "1",

doi = "10.1016/j.ijfatigue.2019.105216",

language = "English",

volume = "129",

journal = "International Journal of Fatigue",

issn = "0142-1123",

publisher = "Elsevier Limited",

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TY - JOUR

T1 - Systematic statistical characterisation of stress-life datasets using 3-Parameter distributions

AU - Hoole, Joshua

AU - Sartor, Pia

AU - Booker, Julian

AU - Cooper, Jonathan

AU - Gogouvitis, Xenofon V.

AU - Schmidt, R. Kyle

PY - 2019/12/1

Y1 - 2019/12/1

N2 - The variability present in S-N datasets is typically characterised using probability distributions to enable the construction of Probability-S-N curves for design. 3-Parameter Log-Normal and Weibull distributions have been proposed as alternative distributions to the commonly assumed 2-Parameter Log-Normal distribution. This paper performs statistical characterisation of a 4340 steel S-N dataset from the Engineering Sciences Data Unit using a systematic methodology. The 3-Parameter Weibull distribution provided improved characterisation of the S-N dataset. Using a case study, it was also demonstrated that use of a 3-Parameter Weibull distribution can increase component safe-life values by 20% when compared to the 2-Parameter Log-Normal distribution.

AB - The variability present in S-N datasets is typically characterised using probability distributions to enable the construction of Probability-S-N curves for design. 3-Parameter Log-Normal and Weibull distributions have been proposed as alternative distributions to the commonly assumed 2-Parameter Log-Normal distribution. This paper performs statistical characterisation of a 4340 steel S-N dataset from the Engineering Sciences Data Unit using a systematic methodology. The 3-Parameter Weibull distribution provided improved characterisation of the S-N dataset. Using a case study, it was also demonstrated that use of a 3-Parameter Weibull distribution can increase component safe-life values by 20% when compared to the 2-Parameter Log-Normal distribution.

KW - Fatigue Design

KW - Probabilistic Analysis

KW - S-N Curves

KW - Statistics

UR - https://www.scopus.com/pages/publications/85070226577

U2 - 10.1016/j.ijfatigue.2019.105216

DO - 10.1016/j.ijfatigue.2019.105216

M3 - Article (Academic Journal)

AN - SCOPUS:85070226577

SN - 0142-1123

VL - 129

JO - International Journal of Fatigue

JF - International Journal of Fatigue

M1 - 105216

ER -

Systematic statistical characterisation of stress-life datasets using 3-Parameter distributions

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Probabilistic Fatigue Methodology for Aircraft Landing Gear

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Systematic statistical characterisation of stress-life datasets using 3-Parameter distributions

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Probabilistic Fatigue Methodology for Aircraft Landing Gear

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