  Population-based structural health monitoring (PBSHM), aims to share
information between members of a population. An offshore wind (OW) farm could
be considered as a population of nominally-identical wind-turbine structures.
However, benign variations exist among members, such as geometry, sea-bed
conditions and temperature differences. These factors could influence
structural properties and therefore the dynamic response, making it more
difficult to detect structural problems via traditional SHM techniques. This
paper explores the use of a hierarchical Bayesian model to infer expected soil
stiffness distributions at both population and local levels, as a basis to
perform anomaly detection, in the form of scour, for new and existing turbines.
To do this, observations of natural frequency will be generated as though they
are from a small population of wind turbines. Differences between individual
observations will be introduced by postulating distributions over the soil
stiffness and measurement noise, as well as reducing soil depth (to represent
scour), in the case of anomaly detection.
