Aeroelastic simulations of wind turbines affected by leading edge erosion: datasets for multivariate time-series classification

This repository contains data generated and used for classification in the publication:
- Duthé, Gregory, Imad Abdallah, Sarah Barber, and Eleni Chatzi. 2021. “Modelling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades.” engrXiv. September 1. doi:10.31224/osf.io/mcg75. (https://engrxiv.org/mcg75)

The data is generated via OpenFAST aeroelastic simulations coupled with a Non-Homogeneous Compound Poisson Process for degradation modelling and was used to train a Transformer deep learning model. Each sample is a multivariate time-series of length 60'000, with the following 4 channels extracted from the simulations for a section at the tip of the blade:

• Inflow velocity
• Angle of attack
• Lift coefficient
• Drag coefficient

.Please see the publication above for more information as well as the included readme for information about the data and an example of how to load it into to PyTorch.