Linear Projection Techniques in Damage Detection Under a Changing Environment

Abstract

The merit of linear projections as a way to improve the resolution in damage detection under changing environmental conditions is examined. It is contended that if the data from the reference condition is balanced, in the sense that the number of feature vectors available for the various temperatures is similar, then projections, such as those in Principal Component Analysis and Factor Analysis, will not improve performance. Projections, however, help to control the false positive rate when the reference data set is not balanced. Analysis and simulation results suggest that previous claims on the merit of projection as a way to improve damage detection resolution under environmental variability may be too optimistic.

Addressing practicalities in multivariate nonlinear regression for mitigating environmental and operational variations

“…7,8 For example, temperature has been shown to have a larger effect on the dynamics of a structure than small amounts of damage. 9 The problem associated with the EOVs can be further exacerbated when studying offshore structures since they experience more extreme conditions. 10 As such, an ideal VSHM method can remove the influence of the EOVs without discarding information about the damage.…”

An automated hypersphere-based healthy subspace method for robust and unsupervised damage detection via random vibration response signals

“…As already mentioned, indirect (implicit) methods employ a reduced dimensionality healthy subspace, based on features that are, presumably, sensitive only to damage, while being insensitive to changes in the EOCs. Feature selection is based on proper decomposition techniques such as principal component analysis (PCA), auto-associative neural networks (AANN), 12 factor analysis (FA), 9–26 and cointegration. 27–30…”