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Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients

Author

Listed:
  • Tomas A. Garcia-Calva

    (HSPdigital-Electronics Department, University of Guanajuato, Salamanca 36700, Mexico)

  • Daniel Morinigo-Sotelo

    (Research Group HSPdigital-ADIRE, Institute of Advanced Production Technologies (ITAP), University of Valladolid, 47011 Valladolid, Spain)

  • Vanessa Fernandez-Cavero

    (Miguel de Cervantes European University, 47012 Valladolid, Spain)

  • Arturo Garcia-Perez

    (HSPdigital-Electronics Department, University of Guanajuato, Salamanca 36700, Mexico)

  • Rene de J. Romero-Troncoso

    (HSPdigital-Mechatronics Department, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico)

Abstract

The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks.

Suggested Citation

  • Tomas A. Garcia-Calva & Daniel Morinigo-Sotelo & Vanessa Fernandez-Cavero & Arturo Garcia-Perez & Rene de J. Romero-Troncoso, 2021. "Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients," Energies, MDPI, vol. 14(5), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1469-:d:512776
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    Citations

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    Cited by:

    1. Bon-Gwan Gu, 2022. "Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square," Energies, MDPI, vol. 15(22), pages 1-12, November.
    2. Luis Alonso Trujillo Guajardo & Miguel Angel Platas Garza & Johnny Rodríguez Maldonado & Mario Alberto González Vázquez & Luis Humberto Rodríguez Alfaro & Fernando Salinas Salinas, 2022. "Prony Method Estimation for Motor Current Signal Analysis Diagnostics in Rotor Cage Induction Motors," Energies, MDPI, vol. 15(10), pages 1-24, May.
    3. Tomas Garcia-Calva & Daniel Morinigo-Sotelo & Vanessa Fernandez-Cavero & Rene Romero-Troncoso, 2022. "Early Detection of Faults in Induction Motors—A Review," Energies, MDPI, vol. 15(21), pages 1-18, October.
    4. Chibuzo Nwabufo Okwuosa & Jang-wook Hur, 2022. "A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions," Energies, MDPI, vol. 15(20), pages 1-24, October.
    5. Tanvir Alam Shifat & Rubiya Yasmin & Jang-Wook Hur, 2021. "A Data Driven RUL Estimation Framework of Electric Motor Using Deep Electrical Feature Learning from Current Harmonics and Apparent Power," Energies, MDPI, vol. 14(11), pages 1-21, May.

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