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Predictive maintenance on windmill data – in collaboration with Trønder Energi

  • Handling of erroneous input data in predictive maintenance of wind turbines with explanatory variables, e.g. vibration
  • Exploring the hybrid AI or transfer learning for predictive maintenance data-driven models (e.g. predicting the expected life time of gearboxes and bushings of wind turbines)
  • Making explainability and relationship of the prediction results and causes

Hybrid Modeling and Uncertainty Quantification in Transfer Learning
In the energy sector, wind turbines, compressors or other production facilities are typically numerically simulated. While those simulations are precise, they are too computationally expensive to use for inference about unknown parameters. The idea is to use machine learning as a surrogate model and explore the uncertainty of the trained model, both on training and real data, as well as how the model can be improved by including domain knowledge (hybrid AI). The goal of the project is to use an AI model to mimic the simulation and transfer the model to real data (transfer learning). The focus is on uncertainty, both how well the model can learn the simulation as well as how it performs on the real data The project can be adapted to students from several departments such as Computer Science, Mathematical Sciences or Engineering Cybernetics.

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