A Machine Learning Based Method to Efficiently Analyze the Cogging Torque under Manufacturing Tolerances

Andrea Reales, Werner Jara, Gabriel Hermosilla, Carlos Madariaga, Juan Tapia, Gerd Bramerdorfer

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This paper addresses a new technique based on machine learning which reduces the number of evaluations required to perform robustness analysis of permanent magnet synchronous machines. This methodology is based on the logical behavior of possible faulty magnet combinations produced by manufacturing tolerances. Groups of faulty combinations with a similar structure and cogging output are identified by means of a fuzzy-logic algorithm. Subsequently, only a single faulty combination of each group needs to be evaluated through the finite element method, which severely decreases the computational burden of the tolerance analysis. A 6-slot 4-pole and a 9-slot 6-pole machine were subject to tolerance analysis considering the displacement of the magnets. Both machines were evaluated through the proposed method and the results were validated by means of the finite element method (FEM).

Idioma originalInglés
Título de la publicación alojada2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1353-1357
Número de páginas5
ISBN (versión digital)9781728151359
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Virtual, Online, Canadá
Duración: 10 oct. 202114 oct. 2021

Serie de la publicación

Nombre2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings

Conferencia

Conferencia13th IEEE Energy Conversion Congress and Exposition, ECCE 2021
País/TerritorioCanadá
CiudadVirtual, Online
Período10/10/2114/10/21

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