IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3513-d813206.html
   My bibliography  Save this article

Prony Method Estimation for Motor Current Signal Analysis Diagnostics in Rotor Cage Induction Motors

Author

Listed:
  • Luis Alonso Trujillo Guajardo

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Miguel Angel Platas Garza

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Johnny Rodríguez Maldonado

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Mario Alberto González Vázquez

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Luis Humberto Rodríguez Alfaro

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

  • Fernando Salinas Salinas

    (Universidad Autónoma de Nuevo León, UANL, FIME, Av. Universidad S/N Ciudad Universitaria, San Nicolás de los Garza C.P. 66451, NL, Mexico)

Abstract

This article presents an evaluation of Prony method and its implementation considerations for motor current signal analysis diagnostics in rotor cage induction motors. The broken rotor bar fault signature in current signals is evaluated using Prony method, where its advantages in comparison with fast Fourier transform are presented. The broken rotor bar fault signature could occur during the life cycle operation of induction motors, so that is why an effective early detection estimation technique of this fault could prevent an insulation failure or heavy damage, leaving the motor out of service. First, an overview of cage winding defects in rotor cage induction motors is presented. Next, Prony method and its considerations for the implementation in current signature analysis are described. Then, the performance of Prony method using numerical simulations is evaluated. Lastly, an assessment of Prony method as a tool for current signal analysis diagnostics is performed using a laboratory test system where real signals of an induction motor with broken rotor bar operated with/without a variable frequency drive are analyzed. The summary results of the estimation (amplitudes and frequencies) are presented in the results and discussion section.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3513-:d:813206
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3513/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/10/3513/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Xinyue Liu & Yan Yan & Kaibo Hu & Shan Zhang & Hongjie Li & Zhen Zhang & Tingna Shi, 2022. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition," Energies, MDPI, vol. 15(3), pages 1-16, February.
    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. 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. 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.
    3. 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. 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.
    3. Ryszard Palka, 2022. "The Performance of Induction Machines," Energies, MDPI, vol. 15(9), pages 1-4, 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. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    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. 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.

    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:10:p:3513-:d:813206. 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.