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Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults

Author

Listed:
  • Tomas A. Garcia-Calva

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

  • Daniel Morinigo-Sotelo

    (HSPdigital-ADIRE, ITAP, University of Valladolid, 47011 Valladolid, Spain)

  • Oscar Duque-Perez

    (HSPdigital-ADIRE, ITAP, University of Valladolid, 47011 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 Querétaro, San Juan del Río 76806, Mexico)

Abstract

In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.

Suggested Citation

  • Tomas A. Garcia-Calva & Daniel Morinigo-Sotelo & Oscar Duque-Perez & Arturo Garcia-Perez & Rene de J. Romero-Troncoso, 2020. "Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults," Energies, MDPI, vol. 13(16), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4102-:d:396107
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    References listed on IDEAS

    as
    1. 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.
    2. Oscar Duque-Perez & Carlos Del Pozo-Gallego & Daniel Morinigo-Sotelo & Wagner Fontes Godoy, 2019. "Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods," Energies, MDPI, vol. 12(17), pages 1-13, September.
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