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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    11. 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.
    12. 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.
    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.
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    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.

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