A Fem-based Quasi-static Neuro-model for Acoustic Noise in Switch Reluctance Motors

Elimination of stator oscillations in electromotors is among the least studied, but important problems that can influence the electric performance, mechanical wear and tear and acoustical noise. In this paper, we focus on a switch reluctance motor (SRM) designed and prototyped for eventual production in Iran. The stator oscillation problems due to radial forces in SRMs have been reported as more acute as compared to similar motors. Due to the complicated mechanical and electrical structure of SRMs, obtaining any analytical model for its acoustical noise is quite impossible. This necessitates using numerical and experimental methods for obtaining an approximating model for acoustical noise. Since, off line simulation can be carried out, finite element (FE) analysis can be utilized so as to generate enough data for identifying a valid model. In this paper, we introduce a neuro-model with excitation current (amp) and position of stator (degree) as inputs and acoustical noise (db) as output. We performed two subsequent FE analysis using ANSYS package. Our first magnetic analysis is aimed at getting magnetic forces on the body of the stator and rotor, by exerting excitation current. In the second acoustical analysis, we give the magnetic forces, resulting from previous corresponding analysis, to the model and get the acoustical noise. We consider the acoustical behavior of the motor, in its inside and outside spaces, separately. Our approach is quasistatic, since we performed some FEM static analyses for approximating the dynamic behavior of the motor. Some simulation results of a acoustic noise in a designed SR motor, using our proposed model, is also given.