Towards End-to-end Deep Learning Analysis of Electrical Machines
Convolutional Neural Networks (CNNs) and Deep Learning (DL) revolutionized numerous research fields including robotics, natural language processing, self-driving cars, healthcare, and others. However, DL is still relatively under-researched in fields such as physics and engineering. Recent works on DL-assisted analysis showed emerging interest and enormous potential of CNN applications. This paper explores the possibility of developing an end-to-end DL pipeline for the analysis of electrical machines. The CNNs are trained on conventional finite element method (FEA) data to predict the output torque curves of electric machines. FEA is only used for dataset collections and CNN training, whereas the analysis is done solely using CNNs. The required depth in CNN architecture is studied by comparing a simplistic CNN with three ResNet architectures. The effects of dataset balancing and data normalization are studied and torque clipping inspired by offset normalization is proposed to ease CNN training and improve the prediction accuracy. The relation between architecture depth and accuracy is identified showing that deeper CNNs improve the curve shape prediction accuracy even after torque magnitude prediction accuracy saturates. Over 90% accuracy for analysis conducted under a minute is reported for CNNs, whereas FEA of comparable accuracy required 200 hours. Predicting multidimensional outputs can improve CNN performance, which is essential for multiparameter optimization of electrical machines.