Machine learning predictive model for motor moise by expanding dataset using electromagnetic force characteristics

Abstract

To understand the variations of electric vehicle motor noise, the electromagnetic force change due to the tilting and the eccentricity between the rotor and the stator on the noise should be studied. However, it takes enormous computation time to consider all the various conditions, so machine learning (ML) model that can reduce the analysis time of electromagnetic force and noise was developed. The developed ML model showed very high accuracy prediction performance with an R2 value higher than 0.99 even for data that was not used at all for training. The speed increase was more than 100 times of FE analysis. The authors doubt that the high accuracy of the developed model implies the potential for electromagnetic force data structure to be much simpler than it appears. Therefore, various analyzes were conducted on the force and uneven magnetic pull. The change in the temporal-spatial main frequency component was not large, and only the change in the side order was meaningful. It was confirmed that to create a predictive model using a smaller dataset for not only the UMP but also all the individual teeth force is possible. This new way of creating a training dataset for an ML model helps to increase the training efficiency. Based on the generated prediction model, it can be used to identify changes in noise characteristics in major rotational orders and derive suitable improvements.