Machine Learning Based Prediction of Fatigue Events in Railway Rails

Laurent, Vincent; Van, Olivier Vo; Mougeot, Mathilde; Ghidaglia, Jean‐Michel

doi:10.1007/978-3-030-96794-9_42

Cited by 1 publication

(1 citation statement)

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“…With the data explosion, data‐driven methods have been applied to various industries, and the advantages of data‐driven method in induction and summary of data lead to the reliable analysis and prediction of fatigue properties. [ 23,33–36 ] Additionally, the data‐driven algorithms can reflect the relationship between IFs and fatigue performance, thus providing a basic framework for performance evaluation. [ 37–39 ] As the current period in the development process of science and technology ( Figure ), the data‐driven method can characterize the fatigue behavior of welded components.…”

Section: Introductionmentioning

confidence: 99%

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

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Section: Introductionmentioning

confidence: 99%