Gerd Bramerdorfer, Werner Jara, Carlos Madariaga, Juan A. Tapia,
"A Machine Learning Based Method to Efficiently Analyze the Cogging Torque Under Manufacturing Tolerances"
: IEEE ECCE2021, Energy Conversion Congress and Expo, Vancouver, Canada, 10-2021
Original Titel:
A Machine Learning Based Method to Efficiently Analyze the Cogging Torque Under Manufacturing Tolerances
Sprache des Titels:
Englisch
Original Buchtitel:
IEEE ECCE2021, Energy Conversion Congress and Expo, Vancouver, Canada
Original Kurzfassung:
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).
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