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Typical fault diagnosis of permanent magnet synchronous motors
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Wen HUANG1, 2, Ke LYU1, Jinghua HU1, Junquan CHEN1, *
Journal of National Niversity of Defense Technology | 2025, 47(6) : 91 - 105
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Journal of National Niversity of Defense Technology | 2025, 47(6): 91-105
State Monitoring Technology for Electric Machine System
Typical fault diagnosis of permanent magnet synchronous motors
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Wen HUANG1, 2, Ke LYU1, Jinghua HU1, Junquan CHEN1, *
Affiliations
  • 1.National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
  • 2.Northwest Institute of Nuclear Technology, Xi′an 710024, China
Published: 2025-12-28 doi: 10.11887/j.issn.1001-2486.24090044
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For the common stator winding short circuit and rotor eccentricity faults in surface-mounted permanent magnet synchronous motors, a flexible printed circuit board with small footprint and capable of accommodating a large number of windings was used to fabricate the detection coil, which was then arranged in the stator slots to capture magnetic field information.For the stator winding short circuit fault, a winding short circuit detection method using dual orthogonal phase-locked loop to extract fault characteristic values was proposed.This method can effectively distinguish the short circuit resistance, short circuit winding number, and fault location, and was not affected by the motor′s speed fluctuations.For the rotor eccentricity fault, a differential bridge structure of the detection coil based on high-frequency injection was proposed for eccentricity detection, and ultimately, a 2% eccentricity detection can be achieved.For the composite fault, a fault discrimination scheme based on convolutional neural networks was introduced, and the performance of different learning methods was compared.The experimental results show that under the composite fault condition, a 98% correct rate of winding short circuit assessment is achieved, and the eccentricity detection error using AlexNet with a training data proportion of 60% is only 5%.

motor fault detection  /  short circuit between stator turns  /  eccentric fault  /  detection coil  /  convolutional neural network
Wen HUANG, Ke LYU, Jinghua HU, Junquan CHEN. Typical fault diagnosis of permanent magnet synchronous motors[J]. Journal of National Niversity of Defense Technology, 2025 , 47 (6) : 91 -105 . DOI: 10.11887/j.issn.1001-2486.24090044
Year 2025 volume 47 Issue 6
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doi: 10.11887/j.issn.1001-2486.24090044
  • Receive Date:2024-09-29
  • Online Date:2026-04-16
  • Published:2025-12-28
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  • Received:2024-09-29
Affiliations
    1.National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
    2.Northwest Institute of Nuclear Technology, Xi′an 710024, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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