A Non-linear Manifold Strategy for SHM Approaches

Abstract

In the data-based approach to structural health monitoring (SHM) when novelty detection is utilised as a means of diagnosis, benign operational and environmental variations of the structure can lead to false alarms and mask the presence of damage. The key element of this paper is to demonstrate a series of pattern recognition approaches which investigate complex correlations between the variables and thus potentially shed light on the variations within the data that are of interest for SHM. The non-linear manifold learning techniques discussed here, like locally linear embedding combined with robust discordance measures like the minimum covariance determinant and regression techniques like Gaussian processes offer a strategy that includes reliable novelty detection analysis but also a method of investigating the space where structural data clusters are lying.