ArXiv Preprint
Terabytes of data are collected every day by wind turbine manufacturers from
their fleets. The data contain valuable real-time information for turbine
health diagnostics and performance monitoring, for predicting rare failures and
the remaining service life of critical parts. And yet, this wealth of data from
wind turbine fleets remains inaccessible to operators, utility companies, and
researchers as manufacturing companies prefer the privacy of their fleets'
turbine data for business strategic reasons. The lack of data access impedes
the exploitation of opportunities, such as improving data-driven turbine
operation and maintenance strategies and reducing downtimes. We present a
distributed federated machine learning approach that leaves the data on the
wind turbines to preserve the data privacy, as desired by manufacturers, while
still enabling fleet-wide learning on those local data. We demonstrate in a
case study that wind turbines which are scarce in representative training data
benefit from more accurate fault detection models with federated learning,
while no turbine experiences a loss in model performance by participating in
the federated learning process. When comparing conventional and federated
training processes, the average model training time rises significantly by a
factor of 7 in the federated training due to increased communication and
overhead operations. Thus, model training times might constitute an impediment
that needs to be further explored and alleviated in federated learning
applications, especially for large wind turbine fleets.
Lorin Jenkel, Stefan Jonas, Angela Meyer
2022-12-07
