Anomaly detection and representation learning in an instrumented railway bridge

Onderzoeksoutput: Conference paperResearch

Samenvatting

In this contribution, the strain measurements of a railway bridge are used for anomaly detection, in the context of Structural Health Monitoring (SHM). The methodology used is a combination of a sparse convolutional autoencoder (CSAE) and a Mahalanobis distance. Due to the lack of labeled anomalous data, a simulated fault is used to evaluate the performance of the algorithm. The proposed approach far outperforms the classical feature-based approach. Finally, the latent dimension of the autoencoder is studied and shown to be structured and representative of
the underlying physics of the problem.
Originele taal-2English
TitelESANN 2022 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 5-7 October 2022, i6doc.com publ., ISBN 978287587084-1. Available from http://www.i6doc.com/en/
UitgeverijCiaco-i6doc.com, l'édition universitaire en ligne
Pagina's299-304
Aantal pagina's6
StatusPublished - 2022
Evenement30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022 - Bruges, Belgium
Duur: 5 okt 20227 okt 2022

Conference

Conference30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022
Land/RegioBelgium
StadBruges
Periode5/10/227/10/22

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