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Data-Driven Electrical Machines Structural Model Using the Vibration Synthesis Method

delete2022-09-01
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OA
AI
S
Sebastian Ciceo *
F
Fabien Chauvicourt
J
Johan Gyselinck
C
Claudia Marțiș
DOI:10.1109/TTE.2022.3164644delete
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摘要

摘要

En 中文
This article proposes a data-driven structural modeling approach for electrical machine airborne vibration that can be used both in the design stage for fast optimization routines and in system-level simulations. This method is able to capture both in-plane and out-of-plane modal behavior along with local effects, such as tooth bending. The model relies on fitting unitary force-shape responses to transfer function models using a zero-pole representation. These frequency response functions are obtained from applying the vibration synthesis process to a series of finite-element (FE) stator models that have material properties updated using the experimental modal analysis. The obtained zeros and poles are used to construct three lookup table models with their accuracy varying between the three of them with the number of samples used in the data generation process. The accuracy of these models is validated at the unitary force-shape response level using four stator FE model samples. Also, at the run-up vibration spectrogram level, the models are validated, with good results, using radial and tangential force excitation obtained from an updated electromagnetic FE model of an interior permanent magnet (PM) synchronous machine designed for electric power steering. The computational cost compared to the standard FE vibration synthesis method is shown.
Keyword:
Computational modeling
Vibrations
Iron
Atmospheric modeling
Force
Sensors
Data models
Data-driven engineering
electrical machine structural model
internal permanent magnet synchronous machine (IPMSM)
noise
vibration
and harshness (NVH)
vibration synthesis

期刊

I
IEEE Transactions on Transportation Electrification
IF:
8.3
论文数:
2.9K
被引数:
1.6W

机构

S
siemens ag
学者数:
5.6K
论文数: 4.6K
被引数: 3
T
Technical University of Cluj Napoca
学者数:
2.1K
论文数: 1.6K
被引数: 1.2K
U
universite libre de bruxelles
学者数:
2.0W
论文数: 1.7W
被引数: 27
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