  Structural health monitoring (SHM) has been an active research area for the
last three decades, and has accumulated a number of critical advances over that
period, as can be seen in the literature. However, SHM is still facing
challenges because of the paucity of damage-state data, operational and
environmental fluctuations, repeatability issues, and changes in boundary
conditions. These issues present as inconsistencies in the captured features
and can have a huge impact on the practical implementation, but more
critically, on the generalisation of the technology. Population-based SHM has
been designed to address some of these concerns by modelling and transferring
missing information using data collected from groups of similar structures.
  In this work, vibration data were collected from four healthy,
nominally-identical, full-scale composite helicopter blades. Manufacturing
differences (e.g., slight differences in geometry and/or material properties),
among the blades presented as variability in their structural dynamics, which
can be very problematic for SHM based on machine learning from vibration data.
This work aims to address this variability by defining a general model for the
frequency response functions of the blades, called a form, using mixtures of
Gaussian processes.
