IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i11p4776-d370054.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Pier Francesco Orrù & Andrea Zoccheddu & Lorenzo Sassu & Carmine Mattia & Riccardo Cozza & Simone Arena, 2020. "Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4776-:d:370054
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/11/4776/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/11/4776/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    2. Chenhong Zhu & J. G. Wang & Na Xu & Wei Liang & Bowen Hu & Peibo Li, 2022. "A Combination Approach of the Numerical Simulation and Data-Driven Analysis for the Impacts of Refracturing Layout and Time on Shale Gas Production," Sustainability, MDPI, vol. 14(23), pages 1-30, December.
    3. Ke-Lin Du & Chi-Sing Leung & Wai Ho Mow & M. N. S. Swamy, 2022. "Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era," Mathematics, MDPI, vol. 10(24), pages 1-46, December.
    4. Hengyang Zhao & Guobao Zhang & Xi Yang, 2022. "GIS Fault Prediction Approach Based on IPSO-LSSVM Algorithm," Sustainability, MDPI, vol. 15(1), pages 1-11, December.
    5. Mustufa Haider Abidi & Usama Umer & Muneer Khan Mohammed & Mohamed K. Aboudaif & Hisham Alkhalefah, 2020. "Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization," Mathematics, MDPI, vol. 8(11), pages 1-33, November.
    6. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
    7. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    8. Leonardo Bianchini & Alvaro Marucci & Adele Sateriano & Valerio Di Stefano & Riccardo Alemanno & Andrea Colantoni, 2021. "Urbanization and Long-Term Forest Dynamics in a Metropolitan Region of Southern Europe (1936–2018)," Sustainability, MDPI, vol. 13(21), pages 1-13, November.
    9. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
    10. Eduardo Machado & Tiago Pinto & Vanessa Guedes & Hugo Morais, 2021. "Electrical Load Demand Forecasting Using Feed-Forward Neural Networks," Energies, MDPI, vol. 14(22), pages 1-24, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4776-:d:370054. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.