Three-way analysis of Structural Health Monitoring data

Structural Health Monitoring aims to identify damages in engineering structures by monitoring changes in their vibration response. Unsupervised learning algorithms can be used to obtain a model of the undamaged condition and detect which samples are not in agreement with it. However, in real structures with a sensor network configuration, the number of candidate features usually becomes large. Therefore, complexity increases and it is necessary to perform feature selection and/or dimensionality reduction. We propose to exploit the three-way structure of data and apply a true multi-way data analysis algorithm: parallel factor analysis. A simple model is obtained and used to train accurate novelty detectors. The methods are tested both with real and simulated structural data to assess that three-way analysis can be successfully used in structural health monitoring.