Contrastive adversarial domain adaptation for machine remaining useful life prediction

Contrastive adversarial domain adaptation for machine remaining useful life prediction

Enabling precise forecasting of the remaining useful life (RUL) for machines can reduce maintenance cost, increase availability, and prevent catastrophic consequences. Data-driven RUL prediction methods have already achieved acclaimed performance. However, they usually assume that the training and t...

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Bibliographic Details
Main Authors:	Mohamed Ragab, Chen, Zhenghua, Wu, Min, Foo, Chuan Sheng, Kwoh, Chee Keong, Yan, Ruqiang, Li, Xiaoli
Other Authors:	School of Computer Science and Engineering
Format:	Journal Article
Language:	English
Published:	2022
Subjects:	Engineering::Computer science and engineering Domain Adaptation Deep Learning
Online Access:	https://hdl.handle.net/10356/157026

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