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Smart Predictive Maintenance Device for Critical In-Service Motors

Author

Listed:
  • Emil Cazacu

    (Department of Electrical Engineering, Faculty of Electrical Engineering, Polytechnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania)

  • Lucian-Gabriel Petrescu

    (Department of Electrical Engineering, Faculty of Electrical Engineering, Polytechnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania)

  • Valentin Ioniță

    (Department of Electrical Engineering, Faculty of Electrical Engineering, Polytechnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania)

Abstract

The paper proposed an innovative predictive maintenance system, designated to monitor and diagnose critical electrical equipment (generally large power electric motors) within industrial electrical installations. A smart and minimally invasive system is designed and developed. Its scope is to evaluate continuously the essential operating parameters (electrical, thermal, and mechanical) of the investigated equipment. It manages to report the deviations of inspected machine operating parameters values from the rated ones. The system also suggests the potential cause of these abnormal variations along with possible means (if the defect is identified in a database, constantly updated with each appearance of a malfunction). The developed maintenance device generates an operating report of the analyzed equipment, in which the values of power quality and energy indicators are computed and interpreted. Additionally, real-time remote transmission of analyzed data is facilitated, making them accessible from any location. The proposed maintenance system is a low-cost device that is easy to install and use in comparison with similar existing devices and equipment. The designed maintenance system was tested on dedicated to low-voltage equipment up to 100 kW.

Suggested Citation

  • Emil Cazacu & Lucian-Gabriel Petrescu & Valentin Ioniță, 2022. "Smart Predictive Maintenance Device for Critical In-Service Motors," Energies, MDPI, vol. 15(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4283-:d:836384
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    References listed on IDEAS

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    1. Arthur H.A. Melani & Carlos A. Murad & Adherbal Caminada Netto & Gilberto F.M. Souza & Silvio I. Nabeta, 2019. "Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets," Energies, MDPI, vol. 12(10), pages 1-28, May.
    2. Bishal Silwal & Abdalla Hussein Mohamed & Jasper Nonneman & Michel De Paepe & Peter Sergeant, 2019. "Assessment of Different Cooling Techniques for Reduced Mechanical Stress in the Windings of Electrical Machines," Energies, MDPI, vol. 12(10), pages 1-18, May.
    3. Amir Baklouti & Lahcen Mifdal & Sofiene Dellagi & Anis Chelbi, 2020. "An Optimal Preventive Maintenance Policy for a Solar Photovoltaic System," Sustainability, MDPI, vol. 12(10), pages 1-13, May.
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    Cited by:

    1. Ruiqi Tian & Santiago Gomez-Rosero & Miriam A. M. Capretz, 2023. "Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems," Energies, MDPI, vol. 16(20), pages 1-21, October.

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