Code to reproduce paper results (or as close as possible, depending on data-availability). The scripts implement both latent variable methods and a conventional fully explicit EOV procedure to address environmental and operational variability in vibration-based SHM.
- ETH_Blade_visualization.py: Plot all PSDs from the dataset between undamaged and damaged conditions.
- latent_estimation_x.py: Proposed latent variable approach only considering DSFs (X).
-
latent_estimation_x_xi.py: Proposed latent variable approach considering both DSFs (X) and EOPs (
$\xi$ ). -
ols_regression.py: Explicit EOV procedure which considers EOPs (
$\xi$ ) to mitigate the effects observed on DSFs (X). This procedure is used to benchmark the results obtained.
- A latent variable approach for mitigation of environmental and operational variability in vibration-based SHM – A linear approach (EWSHM, 2024)
- Estimation of latent variables (DSFs and EOPs) of structures to obtain a comprehensive baseline model.
- Avoid common issues with measured EOPs and DSFs: noise-influence, collinearity and/or acting EOPs not acquired during measurement.
- Application to a small-scale wind turbine blade.
- Presentation of the work at EWSHM2024.