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Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Author

Listed:
  • Razan Issa

    (SNCF Voyageurs, Direction de l’Ingénierie du Matériel, 6 Rue des Frères Amadéo, 69200 Venissieux, France)

  • Guy Clerc

    (Universite Claude Bernard Lyon 1, Ecole Centrale de Lyon, INSA Lyon, CNRS, Laboratoire Ampère, UMR5005, 69100 Villeurbanne, France)

  • Malorie Hologne-Carpentier

    (ECAM Lasalle Site de Lyon, LabECAM, 69005 Lyon, France)

  • Ryan Michaud

    (SNCF Voyageurs, Direction de l’Ingénierie du Matériel, 6 Rue des Frères Amadéo, 69200 Venissieux, France)

  • Eric Lorca

    (SNCF Voyageurs, Direction de l’Ingénierie du Matériel, 6 Rue des Frères Amadéo, 69200 Venissieux, France)

  • Christophe Magnette

    (SNCF Voyageurs, Direction de l’Ingénierie du Matériel, 6 Rue des Frères Amadéo, 69200 Venissieux, France)

  • Anes Messadi

    (Universite Claude Bernard Lyon 1, Ecole Centrale de Lyon, INSA Lyon, CNRS, Laboratoire Ampère, UMR5005, 69100 Villeurbanne, France)

Abstract

Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.

Suggested Citation

  • Razan Issa & Guy Clerc & Malorie Hologne-Carpentier & Ryan Michaud & Eric Lorca & Christophe Magnette & Anes Messadi, 2024. "Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications," Energies, MDPI, vol. 17(11), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2728-:d:1408494
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    References listed on IDEAS

    as
    1. 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.
    2. Oleg Gubarevych & Stanisław Duer & Inna Melkonova & Marek Woźniak & Jacek Paś & Marek Stawowy & Krzysztof Rokosz & Konrad Zajkowski & Dariusz Bernatowicz, 2023. "Research on and Assessment of the Reliability of Railway Transport Systems with Induction Motors," Energies, MDPI, vol. 16(19), pages 1-21, September.
    Full references (including those not matched with items on IDEAS)

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