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Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors

Author

Listed:
  • Hisahide Nakamura

    (Department of Electrical and Electronic Engineering, School of Engineering, Chukyo University, 101-2, Yagotohonmachi, Showa-ku, Nagoya 466-8666, Japan)

  • Yukio Mizuno

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan)

Abstract

Motors serve as the primary power sources in a wide range of industrial fields. In recent years, their application has been expanded to electric and hybrid electric vehicles. As the performance of the motors installed in electric vehicles directly affects human life, it is critical to diagnose the condition of the windings. The objective of this article is to establish a method to identify the short-circuit fault points in concentrated stator windings based on the magnetic flux density distribution near the stator windings. Unlike with distributed windings, the coils are wound around the teeth in concentrated windings. Thus, it is expected that the accurate position specification of the short circuit can be realized if a detailed magnetic flux density distribution over the teeth is obtained with an appropriate magnetic field sensor. The problem of sensor positioning is solved with two stepper motors moving the search coil in the rotational and longitudinal directions independently at specified intervals. The excellent capability of the proposed system is verified through experiments using the stator winding employed in hybrid electric vehicles. The accuracy and sensitivity of the proposed identification system for short-circuit fault points may enable its practical application in industries, for example, shipping and periodic inspections as well as the production management of motors with concentrated stator windings.

Suggested Citation

  • Hisahide Nakamura & Yukio Mizuno, 2024. "Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors," Energies, MDPI, vol. 17(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:1984-:d:1380638
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    References listed on IDEAS

    as
    1. Andre S. Barcelos & Antonio J. Marques Cardoso, 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-14, April.
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