Generalizable Temperature Nowcasting with Physics-Constrained RNNs for
  Predictive Maintenance of Wind Turbine Components
Johannes Exenberger Matteo Di Salvo Thomas Hirsch Franz Wotawa Gerald Schweiger
Machine learning plays an important role in the operation of current wind
energy production systems. One central application is predictive maintenance to
increase efficiency and lower electricity costs by reducing downtimes.
Integrating physics-based knowledge in neural networks to enforce their
physical plausibilty is a promising method to improve current approaches, but
incomplete system information often impedes their application in real world
scenarios. We describe a simple and efficient way for physics-constrained deep
learning-based predictive maintenance for wind turbine gearbox bearings with
partial system knowledge. The approach is based on temperature nowcasting
constrained by physics, where unknown system coefficients are treated as
learnable neural network parameters. Results show improved generalization
performance to unseen environments compared to a baseline neural network, which
is especially important in low data scenarios often encountered in real-world
applications.