Abstract: Through variable analysis of the electromagentic force in the motor airgap, the influencing factors of electromagnetic noise are obtained. The modal frequency of the motor was first obtained by modal analysis. Then, the static magnetic field was analyzed under the condition of motor rotor eccentricity and different thickness of permanent magnet by MAWELL2D, and the influence of the two on the electromagnetic force in the air gap of the motor was obtained. Piezoelectric impedance experiment was used to obtain the modal frequency of the motor to verify the results of modal simulation. Finally, BP neural network was used to predict the thickness of the permanent magnet of the motor through the air gap electromagnetic force. The results show that radial electromagnetic force can be effectively reduced by avoiding rotor eccentricity and adjusting the thickness of permanent magnet appropriately. The BP neural network can recognize the thickness of permanent magnet through the air gap electromagnetic force.

Key words: radial electromagnetic force, modal analysis, electromagnetic noise, BP neural network, permanent magnet motor, piezoelectric impedance

摘要： 通过变量分析电机气隙中的电磁力得出电磁噪声的影响因素。首先对电机进行模态分析得出模态频率,然后运用Mawell2D对电机转子偏心、永磁体不同厚度等工况下进行静磁场分析,得出两者对电机气隙电磁力的影响,运用压电阻抗实验得出电机的模态频率验证模态仿真的结果,最后运用BP神经网络通过气隙电磁力实现对电机永磁体的厚度进行预测。结果表明:避免转子偏心、适当微调永磁体的厚度,可以有效地减小径向电磁力;BP神经网络可以通过气隙电磁力实现对永磁体厚度的识别。

关键词: 径向电磁力, 电磁噪声, BP神经网络, 永磁电机, 压电阻抗