IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p7855-d951057.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/7855/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/21/7855/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Tongguang Yang & Guanchen Li & Tongyu Wang & Shengyou Yuan & Xueyin Yang & Xiaoguang Yu & Qingkai Han, 2023. "A Novel 1D-Convolutional Spatial-Time Fusion Strategy for Data-Driven Fault Diagnosis of Aero-Hydraulic Pipeline Systems," Mathematics, MDPI, vol. 11(14), pages 1-21, July.
    3. Maciej Skowron & Czeslaw T. Kowalski & Teresa Orlowska-Kowalska, 2022. "Impact of the Convolutional Neural Network Structure and Training Parameters on the Effectiveness of the Diagnostic Systems of Modern AC Motor Drives," Energies, MDPI, vol. 15(19), pages 1-22, September.
    4. Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.
    5. Xiaohua Song & Jing Liu & Chaobo Chen & Song Gao, 2022. "Advanced Methods in Rotating Machines," Energies, MDPI, vol. 15(15), pages 1-3, July.
    6. Gopu Venugopal & Arun Kumar Udayakumar & Adhavan Balashanmugham & Mohamad Abou Houran & Faisal Alsaif & Rajvikram Madurai Elavarasan & Kannadasan Raju & Mohammed H. Alsharif, 2023. "Fault Identification and Classification of Asynchronous Motor Drive Using Optimization Approach with Improved Reliability," Energies, MDPI, vol. 16(6), pages 1-25, March.
    7. Marco Antonio Rodriguez-Blanco & Victor Golikov & René Osorio-Sánchez & Oleg Samovarov & Gerardo Ortiz-Torres & Rafael Sanchez-Lara & Jose Luis Vazquez-Avila, 2022. "Fault Diagnosis of Induction Motor Using D-Q Simplified Model and Parity Equations," Energies, MDPI, vol. 15(22), pages 1-19, November.
    8. 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.
    9. Muhammad Amir Khan & Bilal Asad & Karolina Kudelina & Toomas Vaimann & Ants Kallaste, 2022. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art," Energies, MDPI, vol. 16(1), pages 1-54, December.
    10. 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.
    11. 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.
    12. Muhammed Ali Gultekin & Ali Bazzi, 2023. "Review of Fault Detection and Diagnosis Techniques for AC Motor Drives," Energies, MDPI, vol. 16(15), pages 1-22, July.
    13. Artem Ermolaev & Vladimir Erofeev & Aleksandr Plekhov & Dmitry Titov, 2022. "Magnetic Vibration in Induction Motor Caused by Supply Voltage Distortion," Energies, MDPI, vol. 15(24), pages 1-11, December.
    14. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7855-:d:951057. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.