Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem-Reference-Cited by-同舟云学术

Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem

Published:2023-04 Issue: Volume:120 Page:105837
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Akrim Anass^ORCID,Gogu Christian,Vingerhoeds Rob^ORCID,Salaün Michel

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference74 articles.

1. Intuitive explanation of skip connections in deep learning;Adaloglou;AI Summer,2020

2. A framework for generating large data sets for fatigue damage prognostic problems;Akrim,2022

3. Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation;Atamuradov;Int. J. Progn. Health Manag.,2017

4. Layer normalization;Ba,2016

5. A systematic review on data scarcity problem in deep learning: solution and applications;Bansal;ACM Comput. Surv.,2022

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