【摘要】 永磁同步电机作为工业设备的核心部件，对其准确地故障诊断至关重要，智能数据驱动方法和实时监督技术可助力制定精准维护计划，实现低碳节能运行。基于数字孪生技术开展永磁同步电机高效运维研究，可实现虚实同步、监测电机运行状况，并进一步结合信号处理和机器学习技术，提出由变分模态分解、最大相关峭度解卷积联合BP-Adaboost的故障诊断方法，所提方法能够显著提升永磁同步电机故障诊断的准确率和效率。针对永磁同步电机匝间短路，最优诊断误差率低至1.75%,平均诊断误差控制为约3.6%,诊断精度高。更多还原

【Abstract】 As the core component of industrial equipment, permanent magnet synchronous motors are essential for accurate fault diagnosis, and intelligent data-driven methods and real-time supervision technology can help formulate accurate maintenance plans to achieve low-carbon and energy-saving operation. Based on the digital twin technology, the efficient operation and maintenance of permanent magnet synchronous motor can be carried out, which can realize virtual and real synchronization and monitor the operation status of the motor, and further combines with signal processing and machine learning technology, a fault diagnosis method based on variational mode decomposition, maximum correlation kurtosis deconvolution and BP-Adaboost is proposed, which can significantly improve the accuracy and efficiency of fault diagnosis of permanent magnet synchronous motor. For the inter-turn short circuit of the permanent magnet synchronous motor, the optimal diagnostic error rate is as low as 1.75%, and the average diagnostic error is controlled to about 3.6%, with high diagnostic accuracy.更多还原