Multifault Feature Wasserstein Generative Adversarial Networks for Fault Diagnosis in Unbalanced Data

Due to the limitation of industrial conditions in production, raw sensor data are always shown as an unbalanced dataset, characterized by abundant normal operational data and scarce fault instances. This unbalance can degrade the performance of conventional fault diagnosis methods, leading to reduce...

Full description

Bibliographic Details

Published in:	IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 9
Main Authors:	Ren, Weibo, Wang, Zhijian, Chen, Zhongxin, Zhao, Shun, Dong, Lei, Li, Yanfeng, Fan, Xin
Format:	Journal Article
Language:	English
Published:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Artificial intelligence Bearing-unbalanced data Data mining Datasets Fault diagnosis Generative adversarial networks multiencoder denoising autoencoder (DAE) Training Wasserstein generative adversarial networks (WGANs)
ISSN:	0018-9456, 1557-9662
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!