Isolation of induction motor rotor fault signatures by stray magnetic flux and torque monitoring
Isolation of induction motor rotor fault signatures by stray magnetic flux and torque monitoring
Induction motors are the most established machines in the modern industry as they are broadly deployed in a variety of applications spanning several technologies in electrification, traction, and renewable energy. As such, grid-fed as well as inverter-fed induction machine applications are becoming more and more widespread. Therefore, to avoid failures caused by adverse conditions such as stresses and faults, the development of fault detection frameworks is of the uttermost urgency. This paper presents a new diagnostic technique based on torque monitoring for rotor bar faults in squirrel-cage induction motors. The proposed method is based on the isolation of harmonics during the steady-state operation of the machine by the application of a rigorous signal estimation algorithm that enables the reliable tracking of fault-related signatures in the machine's stray flux and torque signals. The application of the method is demonstrated using data from extensive finite element simulations for various bar breakage scenarios. The simulation results and experimental results indicate that the method is versatile in terms of machine geometry and power rating.
Harmonic isolation, Induction motor rotor faults, Signal estimation, Stray flux analysis, Torque monitoring
Song, Zihao
7845f866-1dda-4a44-a480-96865e954993
Panagiotou, Panagiotis A.
b90a571c-2c03-4404-9f3c-e9c3e1d5c995
Mayo-Maldonado, Jonathan C.
67a88f33-f59e-46b2-abe6-386919d4f244
Arvanitakis, Ioannis
5f4676fd-db79-49bb-9efb-798b8ba17384
Antonino-Daviu, Jose A.
e3190b71-6050-421e-bb65-fab8ddc883e0
Gyftakis, Konstantinos N.
a7aa74a2-e0bf-4863-b62c-8e2fdf530780
1 September 2024
Song, Zihao
7845f866-1dda-4a44-a480-96865e954993
Panagiotou, Panagiotis A.
b90a571c-2c03-4404-9f3c-e9c3e1d5c995
Mayo-Maldonado, Jonathan C.
67a88f33-f59e-46b2-abe6-386919d4f244
Arvanitakis, Ioannis
5f4676fd-db79-49bb-9efb-798b8ba17384
Antonino-Daviu, Jose A.
e3190b71-6050-421e-bb65-fab8ddc883e0
Gyftakis, Konstantinos N.
a7aa74a2-e0bf-4863-b62c-8e2fdf530780
Song, Zihao, Panagiotou, Panagiotis A., Mayo-Maldonado, Jonathan C., Arvanitakis, Ioannis, Antonino-Daviu, Jose A. and Gyftakis, Konstantinos N.
(2024)
Isolation of induction motor rotor fault signatures by stray magnetic flux and torque monitoring.
In 2024 International Conference on Electrical Machines, ICEM 2024.
IEEE..
(doi:10.1109/ICEM60801.2024.10700063).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Induction motors are the most established machines in the modern industry as they are broadly deployed in a variety of applications spanning several technologies in electrification, traction, and renewable energy. As such, grid-fed as well as inverter-fed induction machine applications are becoming more and more widespread. Therefore, to avoid failures caused by adverse conditions such as stresses and faults, the development of fault detection frameworks is of the uttermost urgency. This paper presents a new diagnostic technique based on torque monitoring for rotor bar faults in squirrel-cage induction motors. The proposed method is based on the isolation of harmonics during the steady-state operation of the machine by the application of a rigorous signal estimation algorithm that enables the reliable tracking of fault-related signatures in the machine's stray flux and torque signals. The application of the method is demonstrated using data from extensive finite element simulations for various bar breakage scenarios. The simulation results and experimental results indicate that the method is versatile in terms of machine geometry and power rating.
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Published date: 1 September 2024
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Publisher Copyright:
© 2024 IEEE.
Venue - Dates:
2024 International Conference on Electrical Machines, ICEM 2024, , Torino, Italy, 2024-09-01 - 2024-09-04
Keywords:
Harmonic isolation, Induction motor rotor faults, Signal estimation, Stray flux analysis, Torque monitoring
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Local EPrints ID: 503573
URI: http://eprints.soton.ac.uk/id/eprint/503573
PURE UUID: e9e367e7-99ba-4acf-8cee-b1befa6748fd
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Date deposited: 05 Aug 2025 16:51
Last modified: 05 Aug 2025 16:51
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Contributors
Author:
Zihao Song
Author:
Panagiotis A. Panagiotou
Author:
Jonathan C. Mayo-Maldonado
Author:
Ioannis Arvanitakis
Author:
Jose A. Antonino-Daviu
Author:
Konstantinos N. Gyftakis
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