Towards End-to-End Deep Learning Performance Analysis of Electric Motors

Abstract

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 physics and engineering. Recent works on DL-assisted analysis showed enormous potential of CNN applications in electrical engineering. This paper explores the possibility of developing an end-to-end DL analysis method to match or even surpass conventional analysis techniques such as finite element analysis (FEA) based on the ability of CNNs to predict the performance characteristics of electric machines. The required depth in CNN architecture is studied by comparing a simplistic CNN with three ResNet architectures. Studied CNNs show over 90% accuracy for an analysis conducted under a minute, whereas a FEA of comparable accuracy required 200 h. It is also shown that training CNNs to predict multidimensional outputs can improve CNN performance. Multidimensional output prediction with data-driven methods is further discussed in context of multiphysics analysis showing potential for developing analysis methods that might surpass FEA capabilities.

A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network

“…The 11 , 12 , 21 , 22 and are hyperparameters initialized with empirical evidence in the simulation. In the training process of the network, the reason why we use Adamax instead of the Adam that comes with CGAN is because Adam's ability to adjust the learning rate changes based on a simpler range for the upper limit of the learning rate, as shown in (9). The definition of this range allows our network to process discrete data without modifying the initialization deviation, and has a more flexible adjustment method and a smaller magnitude of change.…”

“…A lot of assumed conditions are required in the modeling process to guarantee solvability and availability. However, these conditions, with regard to theoretical physical equations, do not reflect transmission gears under realistic operating conditions, leading to objective deviations [9]. For instance, the autoregressive model (AR) [10] evaluates data using the autocorrelation function, but is vulnerable to data noise.…”

A Regularized Procedure to Generate a Deep Learning Model for Topology Optimization of Electromagnetic Devices

“…Recently, many research areas have benefited from the potential offered by deep learning (DL) tools, such as convolutional neural networks (CNNs) [1,2]. In fact, recent works on the DL-assisted analysis of electromagnetic (EM) field computation problems showed the promising potential of CNN applications [3][4][5][6][7][8][9][10][11][12][13][14][15].…”

“…The idea that properly trained DL models can substitute the direct calculation of Maxwell's equations [3,14], or provide an end-to-end solution for the performance analysis of electric devices [5] is maturing in the community of computational electromagnetics. A comprehensive review of recent works on machine learning for the design optimization of electromagnetic devices can be found in [4], where the growing interest of the community for DL is clearly evidenced.…”

Artificial Intelligence in Electric Machine Drives: Advances and Trends

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