IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i12p4780-d1173724.html
   My bibliography  Save this article

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

Author

Listed:
  • Sarahi Aguayo-Tapia

    (Digital Systems Group, Electronics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro #1, Tonantzintla, Puebla 72840, Mexico)

  • Gerardo Avalos-Almazan

    (Digital Systems Group, Electronics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro #1, Tonantzintla, Puebla 72840, Mexico)

  • Jose de Jesus Rangel-Magdaleno

    (Digital Systems Group, Electronics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro #1, Tonantzintla, Puebla 72840, Mexico)

  • Juan Manuel Ramirez-Cortes

    (Digital Systems Group, Electronics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro #1, Tonantzintla, Puebla 72840, Mexico)

Abstract

Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4780-:d:1173724
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/12/4780/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/12/4780/
    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. Rahul R. Kumar & Mauro Andriollo & Giansalvo Cirrincione & Maurizio Cirrincione & Andrea Tortella, 2022. "A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors," Energies, MDPI, vol. 15(23), pages 1-36, November.
    3. Junjie Lu & Panpan Wang & Sen Duan & Liping Shi & Li Han, 2018. "Detection of Broken Rotor Bars Fault in Induction Motors by Using an Improved MUSIC and Least-Squares Amplitude Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, November.
    4. Sudip Halder & Sunil Bhat & Daria Zychma & Pawel Sowa, 2022. "Broken Rotor Bar Fault Diagnosis Techniques Based on Motor Current Signature Analysis for Induction Motor—A Review," Energies, MDPI, vol. 15(22), pages 1-20, November.
    5. Seif Eddine Chehaidia & Hakima Cherif & Musfer Alraddadi & Mohamed Ibrahim Mosaad & Abdelaziz Mahmoud Bouchelaghem, 2022. "Experimental Diagnosis of Broken Rotor Bar Faults in Induction Motors at Low Slip via Hilbert Envelope and Optimized Subtractive Clustering Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 15(18), pages 1-22, September.
    6. 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.
    7. Chun-Yao Lee & Yun-Jhan Hsieh & Truong-An Le, 2022. "Induction Motor Fault Classification Based on Combined Genetic Algorithm with Symmetrical Uncertainty Method for Feature Selection Task," Mathematics, MDPI, vol. 10(2), pages 1-22, January.
    8. Zhiyong Zhou & Junzhong Sun & Wei Cai & Wen Liu, 2023. "Test Investigation and Rule Analysis of Bearing Fault Diagnosis in Induction Motors," Energies, MDPI, vol. 16(2), pages 1-13, January.
    9. Alasmer Ibrahim & Fatih Anayi & Michael Packianather & Osama Ahmad Alomari, 2022. "New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection," Energies, MDPI, vol. 15(4), pages 1-24, February.
    10. 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.
    11. Yumin Hsueh & Veeresha Ramesha Ittangihala & Wei-Bin Wu & Hong-Chan Chang & Cheng-Chien Kuo, 2019. "Condition Monitor System for Rotation Machine by CNN with Recurrence Plot," Energies, MDPI, vol. 12(17), pages 1-13, August.
    12. 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.
    13. Khaled Farag & Abdullah Shawier & Ayman S. Abdel-Khalik & Mohamed M. Ahmed & Shehab Ahmed, 2021. "Applicability Analysis of Indices-Based Fault Detection Technique of Six-Phase Induction Motor," Energies, MDPI, vol. 14(18), pages 1-23, September.
    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. Przemyslaw Pietrzak & Piotr Pietrzak & Marcin Wolkiewicz, 2024. "Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors," Energies, MDPI, vol. 17(2), pages 1-22, January.

    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. Reza Bazghandi & Mohammad Hoseintabar Marzebali & Vahid Abolghasemi & Shahin Hedayati Kia, 2023. "A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines," Energies, MDPI, vol. 16(14), pages 1-17, July.
    2. Muhammad Usman Sardar & Toomas Vaimann & Lauri Kütt & Ants Kallaste & Bilal Asad & Siddique Akbar & Karolina Kudelina, 2023. "Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques," Energies, MDPI, vol. 16(15), pages 1-41, July.
    3. Teen-Hang Meen & Wenbing Zhao & Cheng-Fu Yang, 2020. "Special Issue on Selected Papers from IEEE ICKII 2019," Energies, MDPI, vol. 13(8), pages 1-5, April.
    4. 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.
    5. 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.
    6. Adolfo Véliz-Tejo & Juan Carlos Travieso-Torres & Andrés A. Peters & Andrés Mora & Felipe Leiva-Silva, 2022. "Normalized-Model Reference System for Parameter Estimation of Induction Motors," Energies, MDPI, vol. 15(13), pages 1-29, June.
    7. Lien-Kai Chang & Shun-Hong Wang & Mi-Ching Tsai, 2020. "Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering," Energies, MDPI, vol. 13(17), pages 1-12, August.
    8. Siddique Akbar & Toomas Vaimann & Bilal Asad & Ants Kallaste & Muhammad Usman Sardar & Karolina Kudelina, 2023. "State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions," Energies, MDPI, vol. 16(17), pages 1-44, September.
    9. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    10. 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.

    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:16:y:2023:i:12:p:4780-:d:1173724. 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.