20 citations · 20 across the 2 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
CARE to Compare: A real-world dataset for anomaly detection in wind turbine data
Christian Gück, Cyriana M. A. Roelofs, Stefan Faulstich
Anomaly detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain sp…
cs.LG2024★ 20 cited
Transfer learning applications for anomaly detection in wind turbines
Cyriana M. A. Roelofs, Christian Gück, Stefan Faulstich
Anomaly detection in wind turbines typically involves using normal behaviour models to detect faults early. However, training autoencoder models for each turbine is time-consuming…