Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data

João Paulo Dias; Stephen Ekwaro-Osire; Americo Cunha; Shweta Dabetwar; Abraham Nispel; Fisseha M. Alemayehu; Haileyesus B. Endeshaw

doi:10.1016/j.ijfatigue.2019.06.011

Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data

João Paulo Dias, Stephen Ekwaro-Osire, Americo Cunha, Shweta Dabetwar, Abraham Nispel, Fisseha M. Alemayehu, Haileyesus B. Endeshaw

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

This work proposes a parametric probabilistic approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed approach is validated using fatigue life experiments available in the literature.

Original language	English
Pages (from-to)	246-258
Number of pages	13
Journal	International Journal of Fatigue
Volume	127
DOIs	https://doi.org/10.1016/j.ijfatigue.2019.06.011
State	Published - Oct 2019

Keywords

Cumulative fatigue damage
Double linear damage rule
Limited data experiments
Maximum Entropy Principle
Uncertainty quantification

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10.1016/j.ijfatigue.2019.06.011

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title = "Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data",

abstract = "This work proposes a parametric probabilistic approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed approach is validated using fatigue life experiments available in the literature.",

keywords = "Cumulative fatigue damage, Double linear damage rule, Limited data experiments, Maximum Entropy Principle, Uncertainty quantification",

author = "Dias, {Jo{\~a}o Paulo} and Stephen Ekwaro-Osire and Americo Cunha and Shweta Dabetwar and Abraham Nispel and Alemayehu, {Fisseha M.} and Endeshaw, {Haileyesus B.}",

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year = "2019",

month = oct,

doi = "10.1016/j.ijfatigue.2019.06.011",

language = "English",

volume = "127",

pages = "246--258",

journal = "International Journal of Fatigue",

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Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data. / Dias, João Paulo; Ekwaro-Osire, Stephen; Cunha, Americo; Dabetwar, Shweta; Nispel, Abraham; Alemayehu, Fisseha M.; Endeshaw, Haileyesus B.

In: International Journal of Fatigue, Vol. 127, 10.2019, p. 246-258.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data

AU - Dias, João Paulo

AU - Ekwaro-Osire, Stephen

AU - Cunha, Americo

AU - Dabetwar, Shweta

AU - Nispel, Abraham

AU - Alemayehu, Fisseha M.

AU - Endeshaw, Haileyesus B.

PY - 2019/10

Y1 - 2019/10

N2 - This work proposes a parametric probabilistic approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed approach is validated using fatigue life experiments available in the literature.

AB - This work proposes a parametric probabilistic approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed approach is validated using fatigue life experiments available in the literature.

KW - Cumulative fatigue damage

KW - Double linear damage rule

KW - Limited data experiments

KW - Maximum Entropy Principle

KW - Uncertainty quantification

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SP - 246

EP - 258

JO - International Journal of Fatigue

JF - International Journal of Fatigue

SN - 0142-1123

ER -

Parametric probabilistic approach for cumulative fatigue damage using double linear damage rule considering limited data

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