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Early Detection of Faults in Induction Motors—A Review

Author

Listed:
  • Tomas Garcia-Calva

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

  • Daniel Morinigo-Sotelo

    (HSPdigital-ITAP-ADIRE, University of Valladolid, 47002 Valladolid, Spain)

  • Vanessa Fernandez-Cavero

    (Department of Electrical Engineering, University of Valladolid, 47002 Valladolid, Spain)

  • Rene Romero-Troncoso

    (HSPdigital-Mechatronics Department, Autonomous University of Querétaro, San Juan del Río 76806, Mexico)

Abstract

There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7855-:d:951057
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Guellout, O. & Rezig, A. & Touati, S. & Djerdir, A., 2020. "Elimination of broken rotor bars false indications in induction machines," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 250-266.
    4. Andre S. Barcelos & Antonio J. Marques Cardoso, 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-14, April.
    5. Lucia Frosini, 2020. "Novel Diagnostic Techniques for Rotating Electrical Machines—A Review," Energies, MDPI, vol. 13(19), pages 1-26, September.
    6. Syaiful Bakhri & Nesimi Ertugrul, 2022. "A Negative Sequence Current Phasor Compensation Technique for the Accurate Detection of Stator Shorted Turn Faults in Induction Motors," Energies, MDPI, vol. 15(9), pages 1-17, April.
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    Cited by:

    1. 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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