A neural network based surrogate model for predicting noise in synchronous reluctance motors

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...

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Bibliographic Details
Published in2016 IEEE Conference on Electromagnetic Field Computation (CEFC) p. 1
Main Authors Bofan Wang, Rahman, Tanvir, Chang, Kang, Mohammadi, Mohammad Hossain, Lowther, David A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Artificial neural networks
Computational modeling
electric motors
Mathematical model
neural network
noise
Noise level
Predictive models
reluctance motors
Stators
Vibrations
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DOI10.1109/CEFC.2016.7816297

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Summary: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.
DOI:10.1109/CEFC.2016.7816297