Comprehensive Sensitivity and Cross-Factor Variance Analysis-Based Multi-Objective Design Optimization of a 3-DOF Hybrid Magnetic Bearing

Jin, Z; Sun, X; Cai, Y; Zhu, J; Lei, G; Guo, Y

Comprehensive Sensitivity and Cross-Factor Variance Analysis-Based Multi-Objective Design Optimization of a 3-DOF Hybrid Magnetic Bearing

Jin, Z Sun, X Cai, Y Zhu, J

Lei, G

Guo, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Magnetics, 2021, 57, (2)
Issue Date:: 2021-02-01

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Field	Value	Language
dc.contributor.author	Jin, Z
dc.contributor.author	Sun, X
dc.contributor.author	Cai, Y
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
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.date.accessioned	2022-04-08T07:01:27Z
dc.date.available	2022-04-08T07:01:27Z
dc.date.issued	2021-02-01
dc.identifier.citation	IEEE Transactions on Magnetics, 2021, 57, (2)
dc.identifier.issn	0018-9464
dc.identifier.issn	1941-0069
dc.identifier.uri	http://hdl.handle.net/10453/155997
dc.description.abstract	Multi-degree-of-freedom (MDOF) magnetic bearings have been widely investigated and designed for various applications. However, a new design magnetic bearing cannot be directly used without optimization due to the not relatively excellent performance. Besides, there may be a lack of consideration of the interaction of parameters in the design process. Hence, in this article, a three-degree-of-freedom hybrid magnetic bearing (THMB) is optimized as an example. First, a comprehensive sensitivity analysis is carried out to show the relationship between the parameters and optimization objectives in detail. Second, a cross-factor variance analysis is considered due to the possibility of parameter interaction. And then, a hierarchical multi-objective optimization structure is used with the Kriging model and the non-dominated Sorting Genetic Algorithm II (NSGA II). The simulation results verify the validity of the proposed method, and the prototype is under manufacture for further evaluation.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Magnetics
dc.relation.isbasedon	10.1109/TMAG.2020.3005446
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.subject.classification	Applied Physics
dc.title	Comprehensive Sensitivity and Cross-Factor Variance Analysis-Based Multi-Objective Design Optimization of a 3-DOF Hybrid Magnetic Bearing
dc.type	Journal Article
utslib.citation.volume	57
utslib.for	02 Physical 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/Strength - CCET - Centre for Clean Energy 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	*
utslib.copyright.embargo	2023-02-01T00:00:00+1000Z
dc.date.updated	2022-04-08T07:01:26Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	57
utslib.citation.issue	2

Abstract:

Multi-degree-of-freedom (MDOF) magnetic bearings have been widely investigated and designed for various applications. However, a new design magnetic bearing cannot be directly used without optimization due to the not relatively excellent performance. Besides, there may be a lack of consideration of the interaction of parameters in the design process. Hence, in this article, a three-degree-of-freedom hybrid magnetic bearing (THMB) is optimized as an example. First, a comprehensive sensitivity analysis is carried out to show the relationship between the parameters and optimization objectives in detail. Second, a cross-factor variance analysis is considered due to the possibility of parameter interaction. And then, a hierarchical multi-objective optimization structure is used with the Kriging model and the non-dominated Sorting Genetic Algorithm II (NSGA II). The simulation results verify the validity of the proposed method, and the prototype is under manufacture for further evaluation.

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http://hdl.handle.net/10453/155997