A Hybrid Electromagnetic Model of Tubular Permanent Magnet Linear Synchronous Motors Based on Transfer Learning

Wu, T; Xue, G; Lei, G; Guo, Y; Zhu, J

A Hybrid Electromagnetic Model of Tubular Permanent Magnet Linear Synchronous Motors Based on Transfer Learning

Wu, T Xue, G Lei, G

Guo, Y

Zhu, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Electronics, 2025, PP, (99)
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Wu, T
dc.contributor.author	Xue, G
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.contributor.author	Zhu, J
dc.date.accessioned	2025-11-21T04:16:47Z
dc.date.available	2025-11-21T04:16:47Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Transactions on Industrial Electronics, 2025, PP, (99)
dc.identifier.issn	0278-0046
dc.identifier.issn	1557-9948
dc.identifier.uri	http://hdl.handle.net/10453/190728
dc.description.abstract	Accurate electromagnetic modeling and analysis are crucial for the design optimization of tubular permanent magnet linear synchronous motors (TPMLSMs). This article proposes a new electromagnetic modeling method for TPMLSMs based on transfer learning deep neural network (DNN). It integrates the electromagnetic mechanism into the data-driven model to achieve high-precision electromagnetic analysis with a small number of finite element analysis (FEA) samples. First, the electromagnetic analytical model of the slotted TPMLSM is derived from the motors’ structural parameters and relative permeability function. Second, a hybrid model based on transfer learning is developed. This model is pretrained by a large sample set generated from the analytical model in the mechanism analytical model. The trained model is then optimized in the target field using a few high-precision FEA sample sets to improve the prediction accuracy of the hybrid model. Appropriate combinations of training FEA samples are selected based on sensitivity analysis to further improve the model’s prediction accuracy and generalization ability. Third, experimental results demonstrate that the proposed method can significantly enhance the performance of the DNN model, especially when training sets are small. The key performance metrics 1−R<sup>2</sup> decreased from 0.0607 to 0.0125, median absolute error (MEDAE) decreased from 3.535 to 1.311, RMSE decreased from 7.834 to 3.575, respectively, by DNN and the proposed method when the training set size is 5%.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Industrial Electronics
dc.relation.isbasedon	10.1109/TIE.2025.3594456
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	A Hybrid Electromagnetic Model of Tubular Permanent Magnet Linear Synchronous Motors Based on Transfer Learning
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2025-11-21T04:16:45Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Accurate electromagnetic modeling and analysis are crucial for the design optimization of tubular permanent magnet linear synchronous motors (TPMLSMs). This article proposes a new electromagnetic modeling method for TPMLSMs based on transfer learning deep neural network (DNN). It integrates the electromagnetic mechanism into the data-driven model to achieve high-precision electromagnetic analysis with a small number of finite element analysis (FEA) samples. First, the electromagnetic analytical model of the slotted TPMLSM is derived from the motors’ structural parameters and relative permeability function. Second, a hybrid model based on transfer learning is developed. This model is pretrained by a large sample set generated from the analytical model in the mechanism analytical model. The trained model is then optimized in the target field using a few high-precision FEA sample sets to improve the prediction accuracy of the hybrid model. Appropriate combinations of training FEA samples are selected based on sensitivity analysis to further improve the model’s prediction accuracy and generalization ability. Third, experimental results demonstrate that the proposed method can significantly enhance the performance of the DNN model, especially when training sets are small. The key performance metrics 1−R² decreased from 0.0607 to 0.0125, median absolute error (MEDAE) decreased from 3.535 to 1.311, RMSE decreased from 7.834 to 3.575, respectively, by DNN and the proposed method when the training set size is 5%.

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http://hdl.handle.net/10453/190728