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

TL;DR

This paper introduces a probabilistic method for modeling cumulative fatigue damage with limited data, utilizing the double linear damage rule and uncertainty quantification techniques to improve fatigue life predictions.

Contribution

It develops a probabilistic version of the double linear damage rule that accounts for data scarcity using the Maximum Entropy Principle and Monte Carlo simulations.

Findings

Validated approach with literature fatigue data

Provides probabilistic fatigue life distribution

Enhances damage modeling under limited data conditions

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.