Mohamed Ragab


PhD Student

Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction


Journal article


Mohamed Ragab, Zhenghua Chen, Min Wu, Chuan Sheng Foo, Kwoh Chee Keong, Ruqiang Yan, Xiao-Li Li
IEEE Transactions on Industrial Informatics, 2020, pp. 1--1

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In this paper, we develop a novel contrastive adversarial domain adaptation (CADA) approach for machine RUL prediction. Specifically, it is able to transfer the knowledge learned from the data under one condition (labeled source domain) to the data from another condition (unlabeled target domain). The proposed CADA can find domain invariant representations of the target domain data while preserving their intrinsic structure which is crucial to achieve satisfactory performance in the target domain.

Cite

APA
Ragab, M., Chen, Z., Wu, M., Foo, C. S., Keong, K. C., Yan, R., & Li, X.-L. (2020). Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction. IEEE Transactions on Industrial Informatics, 1–1.

Chicago/Turabian
Ragab, Mohamed, Zhenghua Chen, Min Wu, Chuan Sheng Foo, Kwoh Chee Keong, Ruqiang Yan, and Xiao-Li Li. “Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction.” IEEE Transactions on Industrial Informatics (2020): 1–1.

MLA
Ragab, Mohamed, et al. “Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction.” IEEE Transactions on Industrial Informatics, 2020, pp. 1–1.