  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 and resource intensive. Thus, transfer learning
becomes essential for wind turbines with limited data or applications with
limited computational resources. This study examines how cross-turbine transfer
learning can be applied to autoencoder-based anomaly detection. Here,
autoencoders are combined with constant thresholds for the reconstruction error
to determine if input data contains an anomaly. The models are initially
trained on one year's worth of data from one or more source wind turbines. They
are then fine-tuned using smaller amounts of data from another turbine. Three
methods for fine-tuning are investigated: adjusting the entire autoencoder,
only the decoder, or only the threshold of the model. The performance of the
transfer learning models is compared to baseline models that were trained on
one year's worth of data from the target wind turbine. The results of the tests
conducted in this study indicate that models trained on data of multiple wind
turbines do not improve the anomaly detection capability compared to models
trained on data of one source wind turbine. In addition, modifying the model's
threshold can lead to comparable or even superior performance compared to the
baseline, whereas fine-tuning the decoder or autoencoder further enhances the
models' performance.
