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This document presents a study on using deep neural networks (DNNs) for predicting the temperature of internal components in permanent magnet synchronous motors (PMSMs), which is crucial for their safe operation. Two models, a convolutional neural network (CNN) and a long short-term memory (LSTM) network, were developed, with the CNN model demonstrating superior accuracy with a mean squared error of 2.64 °C² and an R² value of 0.9924. The study highlights the potential of machine learning techniques to impr

Author

Amir Krz

Language

EN