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Stray Flux Analysis for the Detection and Severity Categorization of Rotor Failures in Induction Machines Driven by Soft-Starters

Author

Listed:
  • Vicente Biot-Monterde

    (Departamento de Ingeniería Eléctrica, Universitat Politècnica de València, Cno. de Vera, S/N, 46022 Valencia, Spain)

  • Ángela Navarro-Navarro

    (Departamento de Ingeniería Eléctrica, Universitat Politècnica de València, Cno. de Vera, S/N, 46022 Valencia, Spain)

  • Jose A. Antonino-Daviu

    (Departamento de Ingeniería Eléctrica, Universitat Politècnica de València, Cno. de Vera, S/N, 46022 Valencia, Spain)

  • Hubert Razik

    (Laboratory Ampère UMR 5005, University of Lyon, 69007 Lyon, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

The condition monitoring of induction motors (IM), is an important concern for industry due to the widespread use of these machines. Magnetic Flux Analysis, has been proven to be a reliable method of diagnosing these motors. Among the IM types, squirrel-cage motors (SCIM) are one of the most commonly used. In many industrial applications, the IM are driven by different types of starters, quite often by soft-starters. Despite rotor damages are more prone to occur in line-started motors, these kind of failures have been also reported in those ones driven by soft-starters. Related to this, the use of these type of starters may introduce some harmonic components, that could veil the magnetic flux signature of the different rotor faults. So, the aim of this study is to confirm if the Stray Flux Analysis technique maintains its reliability in these cases. Thus, this article presents the results of soft-started induction motors start-up tests, both in healthy and faulty motors. The fault components are detected by analyzing the stray flux during the starting and the study is complemented by analyzing the stray flux during the steady-state. In addition to the failure patterns, numerical indicators have been found so the identification of the failures is not only qualitative, but also quantitative. The results confirm the potential of the technique for detecting electromechanical failures in soft-started SCIMs.

Suggested Citation

  • Vicente Biot-Monterde & Ángela Navarro-Navarro & Jose A. Antonino-Daviu & Hubert Razik, 2021. "Stray Flux Analysis for the Detection and Severity Categorization of Rotor Failures in Induction Machines Driven by Soft-Starters," Energies, MDPI, vol. 14(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5757-:d:634399
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    References listed on IDEAS

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    1. Haraguchi, Nobuya & Cheng, Charles Fang Chin & Smeets, Eveline, 2017. "The Importance of Manufacturing in Economic Development: Has This Changed?," World Development, Elsevier, vol. 93(C), pages 293-315.
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    Cited by:

    1. Guy Clerc, 2022. "Failure Diagnosis and Prognosis of Induction Machines," Energies, MDPI, vol. 15(4), pages 1-2, February.
    2. 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.
    3. Mathew Habyarimana & David George Dorrell & Remmy Musumpuka, 2022. "Reduction of Starting Current in Large Induction Motors," Energies, MDPI, vol. 15(10), pages 1-41, May.
    4. 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.

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