  Virtual sensing techniques have gained traction in applications to the
structural health monitoring of monopile-based offshore wind turbines, as the
strain response below the mudline, which is a primary indicator of fatigue
damage accumulation, is impractical to measure directly with physical
instrumentation. The Gaussian process latent force model (GPFLM) is a
generalized Bayesian virtual sensing technique which combines a physics-driven
model of the structure with a data-driven model of latent variables of the
system to extrapolate unmeasured strain states. In the GPLFM, modeling of
unknown sources of excitation as a Gaussian process (GP) serves to facilitate
strain estimation by providing a complete stochastic characterization of the
covariance relationship between input forces and states, using properties of
the GP covariance kernel as well as correlation information supplied by the
mechanical model. It is shown that posterior inference of the latent inputs and
states is performed by Gaussian process regression of measured accelerations,
computed efficiently using Kalman filtering and Rauch-Tung-Striebel smoothing
in an augmented state-space model. While the GPLFM has been previously
demonstrated in numerical studies to improve upon other virtual sensing
techniques in terms of accuracy, robustness, and numerical stability, this work
provides one of the first cases of in-situ validation of the GPLFM. The
predicted strain response by the GPLFM is compared to subsoil strain data
collected from an operating offshore wind turbine in the Westermeerwind Park in
the Netherlands.
