  Structural health monitoring is a condition-based field of study utilised to
monitor infrastructure, via sensing systems. It is therefore used in the field
of aerospace engineering to assist in monitoring the health of aerospace
structures. A difficulty however is that in structural health monitoring the
data input is usually from sensor arrays, which results in data which are
highly redundant and correlated, an area in which traditional two-way matrix
approaches have had difficulty in deconstructing and interpreting. Newer
methods involving tensor analysis allow us to analyse this multi-way structural
data in a coherent manner. In our approach, we demonstrate the usefulness of
tensor-based learning coupled with for damage detection, on a novel $N$-DoF
Lagrangian aeroservoelastic model.
