Disturbance auto-encoder generation model: Few-shot learning method for IGBT open-circuit fault diagnosis in three-phase converters

Disturbance auto-encoder generation model: Few-shot learning method for IGBT open-circuit fault diagnosis in three-phase converters

With the rapid development of converters in a variety of industrial fields, the fault diagnosis of power switching devices has become an important factor in ensuring the safe and reliable operation of related systems. In recent years, machine learning has performed well in many fault diagnosis tasks...

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Bibliographic Details
Main Authors: Fan Wu, Gen Qiu, Lefei Zhang, Kai Chen, Li Wang, Jinxu Yu, Jinqi Gao
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-01-01
Series:Frontiers in Energy Research
Subjects:
IGBT open-circuit fault
data augmentation
generation model
auto-encoder
few-shot learning
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2022.1077519/full

Internet

https://www.frontiersin.org/articles/10.3389/fenrg.2022.1077519/full

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