Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning. (arXiv:2212.03529v1 [cs.LG])

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 …

federated learning fleet information privacy sharing turbine wind turbine