Search papers, labs, and topics across Lattice.
This paper introduces a hierarchical Bayesian framework that utilizes a state-space Gaussian process to simultaneously identify and remove unmeasured environmental and operational variability (EOV) in population-based structural health monitoring (PBSHM). By leveraging the long temporal correlation of the latent EOV process, the method enables efficient inference through a Kalman filter, addressing the limitations of conventional projection-based approaches that can inadvertently mask damage signals. Validation on both a laboratory-scale benchmark and a simulated offshore wind farm demonstrates significant improvements in damage detection and EOV recovery compared to existing methods.
A novel Bayesian approach reveals that robust damage detection in structural health monitoring can be achieved even in the presence of unmeasured environmental variability.
The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM). The difficulty is compounded in the case that the EOV signals are unmeasured. A common approach in conventional SHM is to apply \emph{projection-based} methods that discard subspaces of healthy feature data, reasoning that the EOV signal dominates the variance of the measured features. However, a common pitfall of projection-based approaches is that when damage acts close to the same variance-dominant direction, damage sensitivity is removed along with the EOV. An alternative identifying assumption for the removal of particular unmeasured EOVs is slowness; the latent EOV process is characterised by its long temporal correlation. In this paper, the latent EOV is cast as a state-space Gaussian process, enabling tractable $\mathcal{O}(T)$ inference via a Kalman filter. A robust hierarchical Bayesian identification framework is developed that enables population-level identification of latent EOVs and EOV-free residual features, using a Laplace approximation. The approach is first validated on a single laboratory-scale benchmark structure from the literature, subject to thermal EOVs, demonstrating robust damage detection and EOV recovery. The method is then applied to a simulated nine-turbine offshore wind farm with staggered deployment and damage, where it delivers a substantial true-positive uplift over projection and cointegration-based baselines at matched false-positive rates.
