Vibration-current data fusion and gradient boosting classifier for enhanced stator fault diagnosis in three-phase permanent magnet synchronous motors
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s00202-023-02148-z.pdf
Reference59 articles.
1. Ma Y, Cao J, Li L (2023) Robust optimization design of permanent magnet synchronous motors for a solar airplane based on a lightweight structure. Energy Rep 9:1023–1031. https://doi.org/10.1016/j.egyr.2023.05.040
2. Xu W, Xu J, Zhang W, Wang J (2023) A combined-pole permanent magnet synchronous motor incorporating nanocomposite magnets. J Magn Magn Mater 577:170807. https://doi.org/10.1016/j.jmmm.2023.170807
3. Guo Z, Zhen S, Liu X et al (2023) Design and application of a novel approximate constraint tracking robust control for permanent magnet synchronous motor. Comput Chem Eng 173:108206. https://doi.org/10.1016/j.compchemeng.2023.108206
4. Tang J, Liang C, Wang Y et al (2021) A stator fault diagnosis method based on the offline motor parameter measurement for PMSM. World Electr Veh J. https://doi.org/10.3390/wevj12040248
5. Pietrzak P, Wolkiewicz M (2023) Demagnetization fault diagnosis of permanent magnet synchronous motors based on stator current signal processing and machine learning algorithms. Sensors. https://doi.org/10.3390/s23041757
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