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Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation

Author

Listed:
  • Ana L. Martinez-Herrera

    (Multidisciplinary Studies Department, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Guanajuato 38944, Mexico)

  • Edna R. Ferrucho-Alvarez

    (Multidisciplinary Studies Department, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Guanajuato 38944, Mexico)

  • Luis M. Ledesma-Carrillo

    (Multidisciplinary Studies Department, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Guanajuato 38944, Mexico)

  • Ruth I. Mata-Chavez

    (Multidisciplinary Studies Department, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Guanajuato 38944, Mexico)

  • Misael Lopez-Ramirez

    (Multidisciplinary Studies Department, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Guanajuato 38944, Mexico)

  • Eduardo Cabal-Yepez

    (Multidisciplinary Studies Department, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Guanajuato 38944, Mexico)

Abstract

In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forward, backpropagation artificial neural network used as a classifier. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among five induction motor conditions, i.e., a motor in good physical condition (HLT), a motor with one broken rotor bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with damage on the bearing outer race (BRN), and a motor with an unbalanced mechanical load (UNB).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1541-:d:753400
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    References listed on IDEAS

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    1. Kowalski, Czeslaw T & Orlowska-Kowalska, Teresa, 2003. "Neural networks application for induction motor faults diagnosis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 63(3), pages 435-448.
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    Cited by:

    1. Josue A. Reyes-Malanche & Francisco J. Villalobos-Pina & Efraın Ramırez-Velasco & Eduardo Cabal-Yepez & Geovanni Hernandez-Gomez & Misael Lopez-Ramirez, 2023. "Short-Circuit Fault Diagnosis on Induction Motors through Electric Current Phasor Analysis and Fuzzy Logic," Energies, MDPI, vol. 16(1), pages 1-15, January.
    2. Ryszard Palka, 2022. "The Performance of Induction Machines," Energies, MDPI, vol. 15(9), pages 1-4, April.
    3. 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.
    4. 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.
    5. Muhammad Zuhaib & Faraz Ahmed Shaikh & Wajiha Tanweer & Abdullah M. Alnajim & Saleh Alyahya & Sheroz Khan & Muhammad Usman & Muhammad Islam & Mohammad Kamrul Hasan, 2022. "Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment," Energies, MDPI, vol. 15(21), pages 1-32, October.
    6. Angela Navarro-Navarro & Israel Zamudio-Ramirez & Vicente Biot-Monterde & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Current and Stray Flux Combined Analysis for the Automatic Detection of Rotor Faults in Soft-Started Induction Motors," Energies, MDPI, vol. 15(7), pages 1-19, March.
    7. Moritz Benninger & Marcus Liebschner & Christian Kreischer, 2023. "Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework," Energies, MDPI, vol. 16(8), pages 1-20, April.

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