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The Use of Acoustic Emission Elastic Waves for Diagnosing High Pressure Mud Pumps Used on Drilling Rigs

Author

Listed:
  • Artur Bejger

    (Department of Condition Monitoring & Maintenance of Machinery, Maritime University of Szczecin, 71–650 Szczecin, Poland)

  • Tomasz Piasecki

    (Marine Engineering Faculty, Maritime University of Szczecin, 71–650 Szczecin, Poland
    LNG tankers, Cyber driller on Ultra Deepwater Drillships, 71–650 Szczecin, Poland)

Abstract

Although mud pumps are vital components of a drilling rig, their failures are frequent. The identification of technical condition of these high-pressure piston pumps is difficult. There are no reliable criteria for the assessment of mud pump condition. In this paper, faults of the pump valve module are identified by means of acoustic emission (AE) signals. The characteristics of these signals are extracted by wavelet packet signal processing. This method has been verified by experiments conducted on a NOV (National Oilwell Varco) -made triplex 14-P-220 mud pump (mounted in the drillship). The results show that the wavelet packet signal processing method can effectively extract the frequency band energy eigenvalues of the signals. Besides, some operational problems associated with high pressure piston mud pumps are presented. A non-invasive method for diagnosing the technical condition of such pumps is being developed at the Maritime University of Szczecin.

Suggested Citation

  • Artur Bejger & Tomasz Piasecki, 2020. "The Use of Acoustic Emission Elastic Waves for Diagnosing High Pressure Mud Pumps Used on Drilling Rigs," Energies, MDPI, vol. 13(5), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1138-:d:327766
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    References listed on IDEAS

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    1. Artur Bejger & Jan Bohdan Drzewieniecki, 2019. "The Use of Acoustic Emission to Diagnosis of Fuel Injection Pumps of Marine Diesel Engines," Energies, MDPI, vol. 12(24), pages 1-11, December.
    2. Qiao Zhang & Weiwen Deng, 2016. "An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform," Energies, MDPI, vol. 9(5), pages 1-24, May.
    3. Nasha Wei & James Xi Gu & Fengshou Gu & Zhi Chen & Guoxing Li & Tie Wang & Andrew D. Ball, 2019. "An Investigation into the Acoustic Emissions of Internal Combustion Engines with Modelling and Wavelet Package Analysis for Monitoring Lubrication Conditions," Energies, MDPI, vol. 12(4), pages 1-19, February.
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    Cited by:

    1. Tomasz Piasecki & Artur Bejger & Andrzej Wieczorek, 2021. "Experimental Studies of Cargo Tank Cooldown in an LNG Carrier," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 886-895.

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