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...

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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 74; s. 1 - 9
Hlavní autori:	Ren, Weibo, Wang, Zhijian, Chen, Zhongxin, Zhao, Shun, Dong, Lei, Li, Yanfeng, Fan, Xin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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