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

Author

Listed:
  • Tommaso Barbariol

    (Department of Information Engineering, University of Padova, 35131 Padova (PD), Italy)

  • Enrico Feltresi

    (Pietro Fiorentini S.p.A., 36057 Arcugnano (VI), Italy)

  • Gian Antonio Susto

    (Department of Information Engineering, University of Padova, 35131 Padova (PD), Italy
    Human Inspired Technology Research Centre, University of Padova, 35131 Padova (PD), Italy)

Abstract

Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. 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 in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams. The approach is validated both on real and synthetic data.

Suggested Citation

  • Tommaso Barbariol & Enrico Feltresi & Gian Antonio Susto, 2020. "Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection," Energies, MDPI, vol. 13(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3136-:d:372611
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    References listed on IDEAS

    as
    1. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
    2. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    3. Gian Antonio Susto & Alessandro Beghi, 2013. "A virtual metrology system based on least angle regression and statistical clustering," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(4), pages 362-376, July.
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