ICONS 2019 Paper Abstract


Paper ThB1SP.4

Barbariol, Tommaso (University of Padova), Feltresi, Enrico (Pietro Fiorentini), Susto, Gian Antonio (University of Padova)

Machine Learning Approaches for Anomaly Detection in Multiphase Flow Meters

Scheduled for presentation during the Regular Session "Fault Detection, Diagnosis and Fault-tolerant Control II" (ThB1SP), Thursday, August 22, 2019, 17:00−17:20,

5th IFAC International Conference on Intelligent Control and Automation Sciences, August 21-23, 2019, Queen’s University Belfast, Northern Ireland

This information is tentative and subject to change. Compiled on November 29, 2021

Keywords Diagnosis, fault detection and fault tolerant control, Emerging areas, Energy and smart grid


Multiphase Flow Meters (MPFM) are important metering tools in the oil and gas industry. A MPFM provides real-time measurements of gas, oil and water flows of a well without the need to separate the phases, a time-consuming procedure that have been classically adopted in the industry. Evaluating the composition of the flow is fundamental for the well management and productivity prediction; therefore, procedures for measure quality assessment are of crucial importance. In this work we propose an Anomaly Detection approach for MPFM that is effectively able to hand the complexity and variability associated with MPFM data. The proposed approach is designed for embedded implementation and it exploits unsupervised Anomaly Detection approaches like Cluster Based Local Outlier Factor and Isolation Forest.


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