Speaker
Description
Early fault detection is crucial in predictive maintenance for wind turbines, yet comparing different algorithms remains challenging due to the scarcity of domain-specific public datasets. Many papers introduce sophisticated algorithms based on inaccessible data, making their results difficult to verify and hard to compare with results of similar algorithms. This presentation addresses these issues by introducing a newly published, high-quality dataset that includes data from 36 wind turbines across three wind farms, offering the most detailed fault information available for public wind turbine datasets.
Additionally, we propose a novel scoring method, CARE (Coverage, Accuracy, Reliability, and Earliness), designed to leverage the dataset's depth to evaluate early fault detection models effectively. This method assesses anomaly detection performance, the ability to recognize normal behavior, and the capability to minimize false alarms while detecting anomalies early.
