Transfer learning applications for anomaly detection in wind turbines

arXiv:2404.03011v1 Announce Type: new
Abstract: 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 …

abstract anomaly anomaly detection applications arxiv autoencoder computational cs.ai cs.lg data detection however normal resources study training transfer transfer learning type wind wind turbines