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Kalman-Cyclisation Dominant Frequency Based PMSM Bearing Fault Detection

Kumar, Krishna LU orcid ; Vaiyapuri, Viswanathan and Nadarajan, Sivakumar (2025) 2025 International Conference on Machine Intelligence and Smart Innovation, ICMISI 2025 In 2025 International Conference on Machine Intelligence and Smart Innovation, ICMISI 2025 - Proceedings p.52-55
Abstract

Permanent Magnet Synchronous Motors (PMSMs) are becoming increasingly popular in traction applications. PMSMs have gained popularity due to their efficiency, compactness and excellent features related to control. This is mainly because of the permanent magnet placement in the rotor, which negates any additional rotor excitation, hence reducing energy losses considerably. This makes them more efficient compared to induction motors. PMSMs also have a high-power density, enabling them to provide more power relative to their size. These features make them highly feasible in electric vehicles and aerospace applications where size and weight are one of the critical aspects. With the possibility of precise and smooth torque control, they are... (More)

Permanent Magnet Synchronous Motors (PMSMs) are becoming increasingly popular in traction applications. PMSMs have gained popularity due to their efficiency, compactness and excellent features related to control. This is mainly because of the permanent magnet placement in the rotor, which negates any additional rotor excitation, hence reducing energy losses considerably. This makes them more efficient compared to induction motors. PMSMs also have a high-power density, enabling them to provide more power relative to their size. These features make them highly feasible in electric vehicles and aerospace applications where size and weight are one of the critical aspects. With the possibility of precise and smooth torque control, they are well adapted to applications that have strict requirements for precise speed and position control. Also, PMSMs provide superior dynamic performance with low maintenance since they do not contain brushes and commutators; hence, they are supposed to have a longer life. But PMSMs have also some challenges, the most critical problem that usually faces PMSMs is fault detection, mainly bearing faults. The bearings in PMSMs are critical components, and any defect in those components creates serious mechanical and electrical problems for the motor. Due to its defective state, the bearing may impose the emergence of vibration, raised friction, and wear, leading to inefficiencies, increased heat generation, and, finally, motor failure. Thus, early detection of bearing faults is indispensable; prolonged operation with faulty bearings results in catastrophic damage to the motor, longer downtime, and more expensive repairs. Thus, the fault diagnosis at the inception stage has become very critical now. In this paper, a technique based on cyclization dominant frequency analysis for bearing fault detection using the phase current of a motor has been proposed. The results have shown that this method can be used for monitoring and detecting bearing faults without the use of any additional sensor.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Bearing Fault, Condition Monitoring, Electrical Aircraft, Electrical Vehicles, PMSM
host publication
2025 International Conference on Machine Intelligence and Smart Innovation, ICMISI 2025 - Proceedings
series title
2025 International Conference on Machine Intelligence and Smart Innovation, ICMISI 2025 - Proceedings
pages
4 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2025 International Conference on Machine Intelligence and Smart Innovation, ICMISI 2025
conference location
Alexandria, Egypt
conference dates
2025-05-10 - 2025-05-12
external identifiers
  • scopus:105016008693
ISBN
9798331523497
DOI
10.1109/ICMISI65108.2025.11115434
language
English
LU publication?
yes
id
63e2dbb8-9224-4c6a-b009-070fcb63347f
date added to LUP
2025-11-12 12:11:05
date last changed
2025-11-12 12:11:54
@inproceedings{63e2dbb8-9224-4c6a-b009-070fcb63347f,
  abstract     = {{<p>Permanent Magnet Synchronous Motors (PMSMs) are becoming increasingly popular in traction applications. PMSMs have gained popularity due to their efficiency, compactness and excellent features related to control. This is mainly because of the permanent magnet placement in the rotor, which negates any additional rotor excitation, hence reducing energy losses considerably. This makes them more efficient compared to induction motors. PMSMs also have a high-power density, enabling them to provide more power relative to their size. These features make them highly feasible in electric vehicles and aerospace applications where size and weight are one of the critical aspects. With the possibility of precise and smooth torque control, they are well adapted to applications that have strict requirements for precise speed and position control. Also, PMSMs provide superior dynamic performance with low maintenance since they do not contain brushes and commutators; hence, they are supposed to have a longer life. But PMSMs have also some challenges, the most critical problem that usually faces PMSMs is fault detection, mainly bearing faults. The bearings in PMSMs are critical components, and any defect in those components creates serious mechanical and electrical problems for the motor. Due to its defective state, the bearing may impose the emergence of vibration, raised friction, and wear, leading to inefficiencies, increased heat generation, and, finally, motor failure. Thus, early detection of bearing faults is indispensable; prolonged operation with faulty bearings results in catastrophic damage to the motor, longer downtime, and more expensive repairs. Thus, the fault diagnosis at the inception stage has become very critical now. In this paper, a technique based on cyclization dominant frequency analysis for bearing fault detection using the phase current of a motor has been proposed. The results have shown that this method can be used for monitoring and detecting bearing faults without the use of any additional sensor.</p>}},
  author       = {{Kumar, Krishna and Vaiyapuri, Viswanathan and Nadarajan, Sivakumar}},
  booktitle    = {{2025 International Conference on Machine Intelligence and Smart Innovation, ICMISI 2025 - Proceedings}},
  isbn         = {{9798331523497}},
  keywords     = {{Bearing Fault; Condition Monitoring; Electrical Aircraft; Electrical Vehicles; PMSM}},
  language     = {{eng}},
  pages        = {{52--55}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{2025 International Conference on Machine Intelligence and Smart Innovation, ICMISI 2025 - Proceedings}},
  title        = {{Kalman-Cyclisation Dominant Frequency Based PMSM Bearing Fault Detection}},
  url          = {{http://dx.doi.org/10.1109/ICMISI65108.2025.11115434}},
  doi          = {{10.1109/ICMISI65108.2025.11115434}},
  year         = {{2025}},
}