Modern Structural Health Monitoring (SHM) systems are becoming of pervasive use in civil engineering because they can track the structural condition and detect damages of critical and civil infrastructures such as buildings, viaducts, and tunnels.This paper presents a new framework that exploits compression techniques to identify anomalies in the structure, avoiding continuous streaming of raw data to the cloud. The authors trained three compression models, namely a Principal Component Analysis (PCA), a fully-connected autoencoder, and a convolutional autoencoder.
J. Oskarsson, P. Sidén, and F. Lindsten. (2023)cite arxiv:2302.08415Comment: 17 pages, 4 figures. Accepted to AISTATS 2023. Code available at https://github.com/joeloskarsson/tgnn4i.
M. Jin, Y. Zheng, Y. Li, S. Chen, B. Yang, and S. Pan. https://github.com/GRAND-Lab/MTGODE, (2022)cite arxiv:2202.08408Comment: 14 pages, 6 figures, 5 tables.
M. Liu, A. Zeng, M. Chen, Z. Xu, Q. Lai, L. Ma, and Q. Xu. (2021)cite arxiv:2106.09305Comment: This paper presents a novel convolutional neural network for time series forecasting, achieving significant accuracy improvements.