Electric Machine Stator FRF Prediction using Machine Learning

Abstract In this paper, a Machine Learning surrogate model is developed for Noise, Vibration, and Harshness (NVH) analysis of a 48-slot, 8-pole Permanent Magnet Synchronous Motor (PMSM) stator. A parametric stator model is used to generate a Finite Element based dataset. The resultant Frequency Response Functions (FRFs) due to dedicated force waves are the output to be predicted. To this end, several pre-processing data reduction techniques combined with Machine Learning (ML) regressions are investigated. The findings confirm the workflow ability to generalize effectively, offering a robust and efficient method for NVH analysis of PMSM without the need for an FE model.