Classification of leading-edge-erosion severity via machine learning surrogate models

Leading-edge erosion is a common form of wind turbine blade deterioration that reduces aerodynamic performance, increases maintenance demands, and shortens turbine service life. Machine-learning-based structural health monitoring systems offer a promising route for detecting erosion severity, but their performance often depends on access to large labeled training datasets. For wind turbine applications, generating these datasets with full-physics simulation can be computationally expensive, motivating the use of surrogate models for efficient data generation. In this work, we evaluate whether a Gaussian process (GP) emulator can replace full-physics simulation as a training-data generator for leading-edge-erosion classification. The surrogate is trained on a limited set of OpenFAST simulations and then used to generate large labeled datasets, essentially cost-free, for a random forest classifier. We apply a parallel partial and zero-censored emulator which extends the standard GP framework by predicting a vector of response statistics associated with aerodynamic, structural, and turbine-level outputs and incorporating output constraints to improve the physical consistency and uncertainty calibration of the predictions. We compare two random forest classifiers: one trained directly on full simulation data and one trained on emulator-generated data. Both classifiers are evaluated on held-out full-physics simulation data across five leading-edge-erosion severity levels. The emulator-trained classifier achieves accuracy comparable to the simulator-trained classifier, demonstrating that the GP surrogate can substantially reduce the cost of training-data generation without sacrificing classification performance. These results suggest that constrained, vector-valued GP emulators can support efficient simulation-based structural health monitoring workflows and may provide a useful component of future digital-twin frameworks for wind turbine maintenance.