Anomaly detection and representation learning in an instrumented railway bridge
Yacine Bel-Hadj, Wout Weijtjens, Francisco de Nolasco Santos · 2022
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.