The study explores the application of transfer learning (TL) for autoencoder-based anomaly detection in wind turbine SCADA data. Two types of TL models are investigated:
Asset-to-asset TL: A model is trained on data from one wind turbine and fine-tuned on data from another turbine within the same wind farm. This is tested with varying amounts of tuning data (1-3 months).
Multi-asset TL: A model is trained on data from multiple wind turbines and fine-tuned on a target turbine not in the original training set.
The performance of the TL models is compared to baseline models trained on 12 months of data from the target turbine. The results show that the TL models can achieve comparable or slightly better anomaly detection performance compared to the baselines, even with limited tuning data. Fine-tuning the decoder of the autoencoder provides the best results.
Three case studies are presented to demonstrate the TL models' ability to detect real-world failures in wind turbines early, using only 1-2 months of tuning data. The findings highlight the potential of TL to improve anomaly detection in wind turbines, reducing the data and resources required.
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by Cyri... om arxiv.org 04-05-2024
https://arxiv.org/pdf/2404.03011.pdfDiepere vragen