Estimating Electric Motor Temperatures With Deep Residual Machine Learning
Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition...
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Veröffentlicht in: | IEEE transactions on power electronics 2021-07, Vol.36 (7), p.7480-7488 |
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description | Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this article, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors. Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multihead architecture, lean models are presented that exhibit a strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented. |
doi_str_mv | 10.1109/TPEL.2020.3045596 |
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Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this article, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors. Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multihead architecture, lean models are presented that exhibit a strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. 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Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this article, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors. Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multihead architecture, lean models are presented that exhibit a strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented.</description><subject>Artificial neural networks</subject><subject>Deep learning</subject><subject>Electric motors</subject><subject>Estimation</subject><subject>functional safety</subject><subject>Ground truth</subject><subject>Learning curves</subject><subject>Machine learning</subject><subject>monitoring</subject><subject>Neural networks</subject><subject>neural networks (NNs)</subject><subject>Optimization</subject><subject>Permanent magnet motors</subject><subject>permanent magnet synchronous motor (PMSM)</subject><subject>Permanent magnets</subject><subject>Powertrain</subject><subject>supervised machine learning</subject><subject>Synchronous motors</subject><subject>temperature estimation</subject><subject>Temperature measurement</subject><subject>Temperature profiles</subject><subject>Temperature sensors</subject><subject>Thermal analysis</subject><subject>thermal management</subject><subject>Training</subject><issn>0885-8993</issn><issn>1941-0107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNo9kD1PwzAQQC0EEqXwAxCLJeaUc-w49ohK-FIqECpitFznSlO1SbCdgX9PolZMt7x3p3uEXDOYMQb6bvlelLMUUphxEFmm5QmZMC1YAgzyUzIBpbJEac3PyUUIWwAmMmAT8lqEWO9trJtvWuzQRV87umhj6-kS9x16G3uPgX7VcUMfEDv6gaGuerujC-s2dYO0ROubwb8kZ2u7C3h1nFPy-Vgs589J-fb0Mr8vE8e5jImVzuYrtDnLdQXAXcod5NpWjmu50jmTQqFyK8dzVJnkYl1VINKKp5KBhIxPye1hb-fbnx5DNNu2981w0qRCK5UzIUeKHSjn2xA8rk3nh0f9r2FgxmRmTGbGZOaYbHBuDk6NiP-8TrVUIuV_BQNmxw</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Kirchgassner, Wilhelm</creator><creator>Wallscheid, Oliver</creator><creator>Bocker, Joachim</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multihead architecture, lean models are presented that exhibit a strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPEL.2020.3045596</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-9362-8777</orcidid><orcidid>https://orcid.org/0000-0001-9490-1843</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Artificial neural networks Deep learning Electric motors Estimation functional safety Ground truth Learning curves Machine learning monitoring Neural networks neural networks (NNs) Optimization Permanent magnet motors permanent magnet synchronous motor (PMSM) Permanent magnets Powertrain supervised machine learning Synchronous motors temperature estimation Temperature measurement Temperature profiles Temperature sensors Thermal analysis thermal management Training |
title | Estimating Electric Motor Temperatures With Deep Residual Machine Learning |
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