Transferring damage detection knowledge across rotating machines and framed structures: Harnessing domain adaptation and contrastive learning-Reference-Cited by-同舟云学术

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Transferring damage detection knowledge across rotating machines and framed structures: Harnessing domain adaptation and contrastive learning

Published:2024-12 Issue: Volume:221 Page:111743
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Soleimani-Babakamali Roksana,Soleimani-Babakamali Mohammad Hesam,Ali Heravi Mohammad,Askari Mohammad^ORCID,Avci Onur^ORCID,Taciroglu Ertugrul^ORCID

Publisher

Elsevier BV

Reference55 articles.

1. Three decades of statistical pattern recognition paradigm for SHM of bridges;Figueiredo;Struct. Health Monit.,2022

2. SHM under varying environmental conditions: An approach based on model order reduction and deep learning;Torzoni;Comput. Struct.,2022

3. Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage;Wang;Struct. Health Monit.,2021

4. Machine learning for structural health monitoring: challenges and opportunities;Yuan,2020

5. Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection;Sun;J. Struct. Eng.,2020

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