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Prony Method Estimation for Motor Current Signal Analysis Diagnostics in Rotor Cage Induction Motors

Author

Listed:
  • Luis Alonso Trujillo Guajardo

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Miguel Angel Platas Garza

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Johnny Rodríguez Maldonado

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Mario Alberto González Vázquez

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Luis Humberto Rodríguez Alfaro

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Fernando Salinas Salinas

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

Abstract

This article presents an evaluation of Prony method and its implementation considerations for motor current signal analysis diagnostics in rotor cage induction motors. The broken rotor bar fault signature in current signals is evaluated using Prony method, where its advantages in comparison with fast Fourier transform are presented. The broken rotor bar fault signature could occur during the life cycle operation of induction motors, so that is why an effective early detection estimation technique of this fault could prevent an insulation failure or heavy damage, leaving the motor out of service. First, an overview of cage winding defects in rotor cage induction motors is presented. Next, Prony method and its considerations for the implementation in current signature analysis are described. Then, the performance of Prony method using numerical simulations is evaluated. Lastly, an assessment of Prony method as a tool for current signal analysis diagnostics is performed using a laboratory test system where real signals of an induction motor with broken rotor bar operated with/without a variable frequency drive are analyzed. The summary results of the estimation (amplitudes and frequencies) are presented in the results and discussion section.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3513-:d:813206
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    References listed on IDEAS

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    1. 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.
    2. Xinyue Liu & Yan Yan & Kaibo Hu & Shan Zhang & Hongjie Li & Zhen Zhang & Tingna Shi, 2022. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition," Energies, MDPI, vol. 15(3), pages 1-16, February.
    3. Ana L. Martinez-Herrera & Edna R. Ferrucho-Alvarez & Luis M. Ledesma-Carrillo & Ruth I. Mata-Chavez & Misael Lopez-Ramirez & Eduardo Cabal-Yepez, 2022. "Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation," Energies, MDPI, vol. 15(4), pages 1-11, February.
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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. 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.
    3. Sarahi Aguayo-Tapia & Gerardo Avalos-Almazan & Jose de Jesus Rangel-Magdaleno & Juan Manuel Ramirez-Cortes, 2023. "Physical Variable Measurement Techniques for Fault Detection in Electric Motors," Energies, MDPI, vol. 16(12), pages 1-21, June.

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