Non-contact sensing for anomaly detection: A focus-SVDD with complex-valued auto-encoder approach
This research between EPFL and University of Glasgow seeks to use non-destructive sensing paired with AI-driven methods to conduct anomaly detection on wind turbine blade samples. The method evaluates the samples for surface and subsurface defects.
If you found this useful please ensure to cite our publication:
G. Frusque, D. Mitchell, J. Blanche, D. Flynn, and O. Fink, ‘Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued autoencoder approach’, Mech Syst Signal Process, vol. 208, p. 111022, 2024, doi: https://doi.org/10.1016/j.ymssp.2023.....