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Open AccessArticle

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

1
Department of Information Engineering, University of Padova, 35131 Padova (PD), Italy
2
Pietro Fiorentini S.p.A., 36057 Arcugnano (VI), Italy
3
Human Inspired Technology Research Centre, University of Padova, 35131 Padova (PD), Italy
*
Author to whom correspondence should be addressed.
Energies 2020, 13(12), 3136; https://doi.org/10.3390/en13123136
Received: 18 May 2020 / Revised: 8 June 2020 / Accepted: 12 June 2020 / Published: 17 June 2020
(This article belongs to the Special Issue Advanced Manufacturing Informatics, Energy and Sustainability)
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. View Full-Text
Keywords: anomaly detection; data fusion; data mining; edge analytics; Machine Learning; Measuring Systems; oil and gas; process monitoring; Root Cause Analysis; self-diagnosis anomaly detection; data fusion; data mining; edge analytics; Machine Learning; Measuring Systems; oil and gas; process monitoring; Root Cause Analysis; self-diagnosis
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Barbariol, T.; Feltresi, E.; Susto, G.A. Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection. Energies 2020, 13, 3136.

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