Electromagnetic Signal Analysis for Electrical and Mechanical Fault Diagnosis in Synchronous Motor

This paper presents a comprehensive approach to fault diagnosis in synchronous motors by integrating non- invasive electromagnetic signal analysis with the Finite Element Method (FEM). The study encompasses a wide range of electrical and mechanical faults, including stator and rotor winding issues, ground faults, phase short circuits, eccentricity faults, and broken damper bars. By analyzing stator current harmonics and stray magnetic flux, the proposed method significantly enhances the accuracy of fault detection. The diagnostic process is validated through simulation results, demonstrating its effectiveness in real-time monitoring and predictive maintenance. These findings contribute to the development of more reliable and cost-efficient diagnostic systems, reducing operational disruptions and extending the lifespan of electrical machinery in industrial applications.

Authors

Publication Details

Journal
Journal of Magnetics
Published
2025-12-31
DOI
https://doi.org/10.4283/jmag.2025.30.4.596
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

Electromagnetic Signal Analysis for Electrical and Mechanical Fault Diagnosis in Synchronous Motor

Junki Park, Peter Harmony Nkwocha, Jeihoon Baek
Journal of Magnetics
Machine Fault Diagnosis Techniques
article

Electromagnetic Signal Analysis for Electrical and Mechanical Fault Diagnosis in Synchronous Motor

Junki Park, Peter Harmony Nkwocha, Jeihoon Baek
article en

Abstract

This paper presents a comprehensive approach to fault diagnosis in synchronous motors by integrating non- invasive electromagnetic signal analysis with the Finite Element Method (FEM). The study encompasses a wide range of electrical and mechanical faults, including stator and rotor winding issues, ground faults, phase short circuits, eccentricity faults, and broken damper bars. By analyzing stator current harmonics and stray magnetic flux, the proposed method significantly enhances the accuracy of fault detection. The diagnostic process is validated through simulation results, demonstrating its effectiveness in real-time monitoring and predictive maintenance. These findings contribute to the development of more reliable and cost-efficient diagnostic systems, reducing operational disruptions and extending the lifespan of electrical machinery in industrial applications.

Journal of MagneticsVol. 30(4)
Industry, innovation and infrastructure
Openalex Percentile: Top 32%
Machine Fault Diagnosis Techniques
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