A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. In this paper, a novel multi-index generative adversarial network (MI-GAN) is proposed to detect the tool wear conditions subject to imbalanced signal data. First, the generator in the MI-GAN is t...

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Bibliographic Details
Main Authors:	Guokai Zhang, Haoping Xiao, Jingwen Jiang, Qinyuan Liu, Yimo Liu, Liying Wang
Format:	Article
Language:	English
Published:	Wiley 2020-01-01
Series:	Complexity
Online Access:	http://dx.doi.org/10.1155/2020/5831632
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