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

Razan Issa, Guy Clerc (), Malorie Hologne-Carpentier, Ryan Michaud, Eric Lorca, Christophe Magnette and Anes Messadi
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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

Energies, 2024, vol. 17, issue 11, 1-24

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.

Keywords: fault diagnosis; induction motor; fault detection; motor current signal analysis; railway (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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