Inter-Turn Short Circuit Fault Diagnosis and Localization of PMSM Based on Current Trajectory

The inter-turn short circuit fault (ISCF) is one of the most common faults in permanent magnet synchronous motors (PMSMs). Current research frequently relies on the amplitude of specific sub-harmonics in the current as fault diagnosis indicators. However, the signal frequency can be easily affected...

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Bibliographic Details
Published in:IEEE sensors journal 2024-11, p.1-1
Main Authors: Yu, Yanti, Yang, Shuming, Zhang, Xiaofei, Qu, Yinpeng, Qin, Guojun
Format: Article
Language:English
Subjects:
Accuracy
Circuit faults
Current trajectory
Fault diagnosis
Fault indicator
Feature extraction
Harmonic analysis
Inter-turn short circuit fault
Intersection distance
Mathematical models
Motors
Sensors
Trajectory
Vibrations
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Summary:The inter-turn short circuit fault (ISCF) is one of the most common faults in permanent magnet synchronous motors (PMSMs). Current research frequently relies on the amplitude of specific sub-harmonics in the current as fault diagnosis indicators. However, the signal frequency can be easily affected by noise in industrial environments, and the process of extracting characteristic frequencies can be computationally intensive. To address these issues, an online fault diagnosis method based on current trajectory analysis is proposed. This method utilizes the phase currents output by the motor controller, eliminating the need for additional hardware circuits and requiring only a rough estimation of the fundamental frequency range to diagnose the faults. Firstly, it is demonstrated that the current trajectory formed by the PMSM phase currents in a Cartesian coordinate system is approximately elliptical. Secondly, it is derived how changes in the current under fault conditions affect the inclination angle of the current trajectory and the corresponding intersection distances. Based on this, a fault indicator is established. Finally, the proposed method is extended to various operating conditions. Experimental results demonstrate that the proposed diagnosis method can effectively detect ISCF in PMSMs and accurately identify the faulty phases.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3495826