Partial Demagnetization Fault Diagnosis Research of Permanent Magnet Synchronous Motors Based on the PNN Algorithm

被引：0

作者：

Zhang D. ^{[1
]}

Zhao J. ^{[1
]}

Dong F. ^{[1
]}

Song J. ^{[1
]}

Dou S. ^{[1
]}

Wang H. ^{[1
]}

Xie F. ^{[1
]}

机构：

[1] School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, Anhui Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2019年 / 39卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Fault feature quantity; Gap flux density; Partial demagnetization fault diagnosis; Permanent magnet synchronous linear motor (PMSLM); Probabilistic neural network (PNN);

D O I：

10.13334/j.0258-8013.pcsee.172531

中图分类号：

学科分类号：

摘要：

A partial demagnetization fault classification and identification method based on the spatial air gap flux density reconstruction and probabilistic neural network (PNN) algorithm was introduced to solve the partial demagnetization faults of permanent magnet synchronous linear motors (PMSLM). An equivalent magnetization method was used to analyze the distribution characteristics of PMSLM air gap flux density in different spatial locations under the condition of local demagnetization of permanent magnets. Finite element method was used to quantitatively calculate the air gap flux density on, above and below the air gap centerline, and they were fused into a special fault feature quantity to uniquely identify the types of demagnetization, and the simulation analysis under various types of demagnetization faults were carried out to build rich demagnetization sample database. Finally, the neural network and the radial basis network were established to make the grid structure optimized, and the PNN algorithm was used to realize the accurate classification and identification of local demagnetization faults, and the accuracy of the classifier was simulated. Prototype experiment results show that the proposed approach can classify PMSLM local demagnetization fault types, and the recognition rate is of 99.4%. © 2019 Chin. Soc. for Elec. Eng.

引用

页码：296 / 306

页数：10

共 22 条

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