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.
Local Outlier Factor (LOF) is an anomaly detection algorithm presented as "LOF: Identifying Density-based Local Outliers" by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander[1]. The key idea of LOF is comparing the local density of a point's neighborhood with the local density of its neighbors.
B. Ghojogh, F. Karray, and M. Crowley. (2020)cite arxiv:2004.02137Comment: Accepted (to appear) in Canadian Conference on Artificial Intelligence (Canadian AI conference) 2020, Springer. This version includes supplementary material for derivation of an equation.