Learning Discriminative Feature Representations via Metric Learning for Early Operation of Wind Turbine Anomaly Detection Systems.

To achieve robust wind turbine anomaly detection, we attempted to incorporate metric learning into the process of learning discriminative feature representations. In the context of anomaly detection based on inlier modeling, where anomalies are identified as inputs deviating from the normal state distribution, the key to building a high-performance and robust system, even with limited training data, lies in eliminating the influence of environmental differences from the inputs and obtaining a compact normal state distribution. To address this, we designed a deep neural network-based feature extractor to mitigate the impact of environmental changes. Additionally, we integrated metric learning into its learning process to obtain a more compact distribution by embedding inputs with similar properties close to one another. We validated the effectiveness of the developed network as a feature extractor for constructing a normal model with limited data through experimental comparisons using vibration data collected from sensors. Specifically, we utilized MobileNet as a discriminative feature extractor to identify 37 wind turbines. By introducing metric learning based on t-SNE into its training process, we achieved a notable 50 % improvement in the AUC. Remarkably, this significant improvement was accomplished using only a small amount of data, approximately 13 minutes, obtained from the monitored turbine.