Modern Statistical Models and Methods for Estimating Fatigue-Life and Fatigue-Strength Distributions from Experimental Data
William Q. Meeker, Luis A. Escobar, Francis G. Pascual, Yili, Hong, Peng Liu, Wayne M. Falk, Balajee Ananthasayanam

TL;DR
This paper reviews traditional and introduces a novel statistical modeling approach for estimating fatigue-life and fatigue-strength distributions from experimental data, improving reliability assessments in engineering applications.
Contribution
It presents a new modeling framework that specifies fatigue-strength models to induce fatigue-life models, offering advantages over traditional methods.
Findings
Traditional models often misapply statistical methods.
The new approach provides more accurate fatigue distribution estimates.
Advantages include better handling of censored data and small quantiles.
Abstract
Engineers and scientists have been collecting and analyzing fatigue data since the 1800s to ensure the reliability of life-critical structures. Applications include (but are not limited to) bridges, building structures, aircraft and spacecraft components, ships, ground-based vehicles, and medical devices. Engineers need to estimate S-N relationships (Stress or Strain versus Number of cycles to failure), typically with a focus on estimating small quantiles of the fatigue-life distribution. Estimates from this kind of model are used as input to models (e.g., cumulative damage models) that predict failure-time distributions under varying stress patterns. Also, design engineers need to estimate lower-tail quantiles of the closely related fatigue-strength distribution. The history of applying incorrect statistical methods is nearly as long and such practices continue to the present. Examples…
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Taxonomy
TopicsProbabilistic and Robust Engineering Design · Fatigue and fracture mechanics · Structural Health Monitoring Techniques
