中国电机工程学报2026，Vol.46Issue(5)：2080-2091,中插28,13.DOI:10.13334/j.0258-8013.pcsee.241867
仿真数据驱动的感应电动机定转子故障迁移识别方法
Simulation Data-driven Transfer Recognition Method for Stator and Rotor Faults of Induction Motors
摘要
Abstract
In engineering practice,the scarcity of induction motor fault data has become a bottleneck that restricts the widespread application of data-driven diagnostic methods in field applications.In order to overcome the reliance on actual data,a simulation data-driven transfer recognition method for stator and rotor faults of induction motors is proposed.This method firstly generates voltage and current data of the motor under different health states through finite element modeling.Then,it analyzes the performance of different faults in the instantaneous power and converts the fault feature components at different frequencies into trajectories of different colors.The trajectories are thus fused into graphical samples with multiple feature components.The graphical samples reduce the distribution differences between simulation and actual data,thereby decreasing the difficulty of transfer recognition.Finally,using the graphical samples as input,the convolutional neural network trained by the simulation data is directly applied to fault recognition of actual motors.The experimental result shows that the method can still accurately recognize stator and rotor faults in the actual motor in the case of learning only from simulation data samples,with an accuracy as high as 97.7％,meeting engineering requirements.关键词
感应电动机/迁移学习/定转子故障/图形化样本/卷积神经网络Key words
induction motor/transfer learning/stator and rotor faults/graphical samples/convolutional neural network分类
信息技术与安全科学引用本文复制引用
王攀攀,王宇佩,张成,刘扬,戴诗科,韩丽..仿真数据驱动的感应电动机定转子故障迁移识别方法[J].中国电机工程学报,2026,46(5):2080-2091,中插28,13.基金项目
国家自然科学基金项目(62076243).Project Supported by National Natural Science Foundation of China(62076243). （62076243）
