Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry

Pier Francesco Orrù, Andrea Zoccheddu, Lorenzo Sassu, Carmine Mattia, Riccardo Cozza and Simone Arena
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Pier Francesco Orrù: Department of Mechanical, Chemical and Material Engineering, University of Cagliari, Via Marengo 2, 09134 Cagliari, Italy
Andrea Zoccheddu: Department of Mechanical, Chemical and Material Engineering, University of Cagliari, Via Marengo 2, 09134 Cagliari, Italy
Lorenzo Sassu: Sartec-Saras Ricerche e Tecnologie Srl, Via 2° Traversa Strada Est, 09032 Cagliari, Italy
Carmine Mattia: Sartec-Saras Ricerche e Tecnologie Srl, Via 2° Traversa Strada Est, 09032 Cagliari, Italy
Riccardo Cozza: Saras S.p.A.-S.S. Sulcitana n.195 Km 19, 09018 Cagliari, Italy
Simone Arena: Department of Mechanical, Chemical and Material Engineering, University of Cagliari, Via Marengo 2, 09134 Cagliari, Italy

Sustainability, 2020, vol. 12, issue 11, 1-15

Abstract: The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms—the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)—are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures.

Keywords: predictive maintenance; machine learning; artificial neural networks; oil and gas industry; fault diagnosis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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