Ethan Mashburn

Machine Learning-Based Anomaly Detection in Hydrogen Fuel Cells: A Path Toward Sustainable and Reliable Energy Systems

To encourage the use of hydrogen energy and improve the dependability of hydrogen fuel cells, this study develops a two-stage semi-supervised framework for anomaly pattern discovery and codification. Isolation Forest, One-Class Support Vector Machine, and Local Outlier Factor are first applied to unlabeled time series...