Anomaly detection and representation learning in an instrumented railway bridge

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

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.
Original languageEnglish
Title of host publicationESANN 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/
PublisherCiaco-i6doc.com, l'édition universitaire en ligne
Pages299-304
Number of pages6
Publication statusPublished - 2022
Event30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022 - Bruges, Belgium
Duration: 5 Oct 20227 Oct 2022

Conference

Conference30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022
Country/TerritoryBelgium
CityBruges
Period5/10/227/10/22

Keywords

  • Representation learning
  • Anomaly detecion
  • Autoencoder
  • Mahalanobis distance
  • Railway data

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