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Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection

Tommaso Barbariol, Enrico Feltresi, Gian Antonio Susto
2020 Energies  
In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence  ...  The approach is validated both on real and synthetic data.  ...  In Proceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 1619 December 2019; pp. 1756–1763. 44.  ... 
doi:10.3390/en13123136 fatcat:tvyweqdua5fqjafgs6sqacvxxa