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Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors

Author

Listed:
  • Petr Kacor

    (Department of Electrical Power Engineering at VSB, Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic)

  • Petr Bernat

    (Department of Electrical Power Engineering at VSB, Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic)

  • Petr Moldrik

    (Department of Electrical Power Engineering at VSB, Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic)

Abstract

In the manufacture squirrel-cage rotors of asynchronous motors, a high standard of quality is required in every part of the production cycle. The die casting process usually creates porosity in the rotor bars. This most common defect in the rotor often remains hidden during the entire assembly of the machine and is usually only detected during final testing of the motor, i.e., at the end of the production process. This leads to unnecessary production costs. Therefore, the aim is to conduct a continuous control immediately after the rotor has been cast before further processing. In our paper, we are interested in selecting a suitable method of offline rotor diagnostics of an asynchronous motor that would be effective for these needs. In the first step, the selection of the method and its integration into the overall manufacturing process is carried out. The arrangement of the sensors and their calibration is then simulated on a 2D Finite Element Model of the rotor. The proposed offline measurement procedures and technologies are finally validated by testing measurements on a rotor that simulates the most frequently occurring faults. A test system is also developed that provides the operator continuous information about the running rotor measurements and makes it easier to evaluate the quality of the cast rotor by means of graphical visualization of the faults.

Suggested Citation

  • Petr Kacor & Petr Bernat & Petr Moldrik, 2021. "Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors," Energies, MDPI, vol. 14(20), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6573-:d:654766
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    References listed on IDEAS

    as
    1. Pavol Rafajdus & Valeria Hrabovcova & Pavel Lehocky & Pavol Makys & Filip Holub, 2018. "Effect of Saturation on Field Oriented Control of the New Designed Reluctance Synchronous Motor," Energies, MDPI, vol. 11(11), pages 1-10, November.
    2. Estefania Artigao & Sofia Koukoura & Andrés Honrubia-Escribano & James Carroll & Alasdair McDonald & Emilio Gómez-Lázaro, 2018. "Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train," Energies, MDPI, vol. 11(4), pages 1-18, April.
    3. Konstantinos N. Gyftakis & Carlos A. Platero & Yucheng Zhang & Santiago Bernal, 2019. "Diagnosis of Static Eccentricity in 3-Phase Synchronous Machines using a Pseudo Zero-Sequence Current," Energies, MDPI, vol. 12(13), pages 1-16, June.
    4. Liling Sun & Boqiang Xu, 2018. "An Improved Method for Discerning Broken Rotor Bar Fault and Load Oscillation in Induction Motors," Energies, MDPI, vol. 11(11), pages 1-15, November.
    5. Jing Tang & Yongheng Yang & Jie Chen & Ruichang Qiu & Zhigang Liu, 2019. "Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection," Energies, MDPI, vol. 13(1), pages 1-17, December.
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    Cited by:

    1. Guy Clerc, 2022. "Failure Diagnosis and Prognosis of Induction Machines," Energies, MDPI, vol. 15(4), pages 1-2, February.
    2. Federico Gargiulo & Annalisa Liccardo & Rosario Schiano Lo Moriello, 2022. "A Non-Invasive Method Based on AI and Current Measurements for the Detection of Faults in Three-Phase Motors," Energies, MDPI, vol. 15(12), pages 1-19, June.

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