  The application of reliable structural health monitoring (SHM) technologies
to operational wind turbine blades is a challenging task, due to the uncertain
nature of the environments they operate in. In this paper, a novel SHM
methodology, which uses Gaussian Processes (GPs) is proposed. The methodology
takes advantage of the fact that the blades on a turbine are nominally
identical in structural properties and encounter the same environmental and
operational variables (EOVs). The properties of interest are the first edgewise
frequencies of the blades. The GPs are used to predict the edge frequencies of
one blade given that of another, after these relationships between the pairs of
blades have been learned when the blades are in a healthy state. In using this
approach, the proposed SHM methodology is able to identify when the blades
start behaving differently from one another over time. To validate the concept,
the proposed SHM system is applied to real onshore wind turbine blade data,
where some form of damage was known to have taken place. X-bar control chart
analysis of the residual errors between the GP predictions and actual
frequencies show that the system successfully identified early onset of damage
as early as six months before it was identified and remedied.
