中国电机工程学报2024，Vol.44Issue(12)：4933-4944,中插28,13.DOI:10.13334/j.0258-8013.pcsee.230139
基于图像融合与迁移学习的永磁同步电机驱动器强泛化性故障诊断研究
A Strong Generalized Fault Diagnosis Method for PMSM Drives With Image Fusion and Transfer Learning
摘要
Abstract
The dead zone effect of the inverter,the nonlinear characteristics,the parameter mismatch,sampling deviation,and the setting error of the controller will lead to the three-phase current of the permanent magnet synchronous motor(PMSM)containing high-order harmonics,and then result in torque fluctuation and vibration noise.This paper proposes a data-driven diagnosis method based on image fusion and deep learning to solve this problem.First,a multi-source fault database is established based on simulation and experimental data.The three-phase current signal of a PMSM is used as the original data source without the aid of an external testing instrument.The spectrum image is obtained by a short-time Fourier transform.The time-frequency gray images are fused into a color image by the method of image fusion.After classifying and labeling the data,the samples are trained using the transfer learning of SqueezeNet.The test results show that the fault diagnosis accuracy of this method is 98.63%.Compared with the traditional method,the proposed method realizes the system-level multi-source fault diagnosis and has higher practicability.Moreover,the feasibility and generalization of fault diagnosis under the condition of insufficient sample data are realized,and the accuracy of fault diagnosis is effectively improved.关键词
永磁同步电机/故障诊断/图像融合/深度学习Key words
permanent magnet synchronous motor(PMSM)/fault diagnosis/image fusion/deep learning分类
信息技术与安全科学引用本文复制引用
李政,汪凤翔,张品佳..基于图像融合与迁移学习的永磁同步电机驱动器强泛化性故障诊断研究[J].中国电机工程学报,2024,44(12):4933-4944,中插28,13.基金项目
国家自然科学基金(面上基金项目)(52277070). Project Supported by National Natural Science Foundation of China(General Program)(52277070). （面上基金项目）
