数字孪生模型驱动的永磁同步电机偏心故障定量诊断方法

doi:10.3901/JME.260743

摘要/Abstract

摘要： 机械偏心故障是永磁同步电机的常见故障，会引起内部磁场与电流波动。基于改进绕组函数法，结合真实气隙分布建立高保真仿真增强模型，揭示偏心故障与电流信号间的机电耦合机理。在此基础上，针对多偏心程度故障样本不足问题，提出一种高保真仿真与迁移学习联合驱动的定量诊断方法：首先利用仿真模型生成不同偏心量下的故障样本；其次引入迁移学习实现源域(仿真数据)与目标域(试验数据)的特征对齐；并结合边界感知正则化提升在高度不平衡条件下的识别能力。最后搭建试验台验证，结果表明在故障样本仅占总样本4.8%时，所提方法平均诊断准确率达92.5%，有效提升了定量诊断的准确性与鲁棒性。

关键词: 永磁同步电机, 数字孪生, 故障诊断, 迁移学习

Abstract: Eccentricity fault is a common type of failure in permanent magnet synchronous motors, which causes fluctuations in the internal magnetic field and current signals. Based on an improved winding function approach, a high-fidelity simulation-enhanced model considering actual air-gap distribution is established to reveal the electromechanical coupling mechanism. To address the lack of fault samples under multiple eccentricity levels, a quantitative diagnosis method driven by high-fidelity simulation and transfer learning is proposed. Specifically, fault samples are generated using the simulation model, transfer learning aligns features between the source domain (simulation data) and target domain (experimental data), and margin-aware regularization enhances fault recognition under imbalanced conditions. Finally, experiments on a dedicated test bench demonstrate that when fault samples account for only 4.8% of the total, the proposed method achieves an average diagnostic accuracy of 92.5%, significantly improving accuracy and robustness.

Key words: permanent magnet synchronous motor, digital twin, fault diagnosis, transfer learning

中图分类号:

TH17

李乃鹏, 张涛, 雷亚国, 李响, 杨彬, 江金泽. 数字孪生模型驱动的永磁同步电机偏心故障定量诊断方法[J]. 机械工程学报, 2026, 62(14): 59-71.

LI Naipeng, ZHANG Tao, LEI Yaguo, LI Xiang, YANG Bin, JIANG Jinze. Digital Twin Model-Driven Eccentricity Fault Quantitative Diagnosis of Permanent Magnet Synchronous Motors[J]. Journal of Mechanical Engineering, 2026, 62(14): 59-71.

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http://www.cjmenet.com.cn/CN/Y2026/V62/I14/59

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