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

• Published in: 2016 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
• Language: English
• 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
• DOI: 10.1109/CEFC.2016.7816297

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.