A Neural Network Based Surrogate Model for Predicting Noise in Synchronous Reluctance Motors

被引：0

作者：

Wang, Bofan ^{[1
]}

Rahman, Tanvir ^{[1
]}

Chang, Kang ^{[2
]}

Mohammadi, Mohammad Hossain ^{[1
]}

Lowther, David A. ^{[1
]}

机构：

[1] McGill Univ, Dept Elect & Comp Engn, Montreal, PQ, Canada

[2] Infolytica Corp, Montreal, PQ, Canada

来源：

2016 IEEE CONFERENCE ON ELECTROMAGNETIC FIELD COMPUTATION (CEFC) | 2016年

关键词：

neural network; electric motors; reluctance motors; noise; vibrations;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper proposes a neural network (NN) based noise prediction model for electric machines, applied to the case of synchronous reluctance motors (SynRMs). The natural frequencies of various vibration modes for a SynRM stator with generalized tooth geometry and slot numbers have been obtained using structural FEA based computations and then used to build a NN based surrogate model. The accuracy of the surrogate model has been tested and applied to predict the noise level in SynRMs. Also, varying trends in the noise levels for single-barrier SynRMs have been analyzed as a function of the rotor's flux carrier and barrier widths using the natural frequency prediction model.

引用

页数：1

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