Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157026
Title: Contrastive adversarial domain adaptation for machine remaining useful life prediction
Authors: Mohamed Ragab
Chen, Zhenghua
Wu, Min
Foo, Chuan Sheng
Kwoh, Chee Keong
Yan, Ruqiang
Li, Xiaoli
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Mohamed Ragab, Chen, Z., Wu, M., Foo, C. S., Kwoh, C. K., Yan, R. & Li, X. (2020). Contrastive adversarial domain adaptation for machine remaining useful life prediction. IEEE Transactions On Industrial Informatics, 17(8), 5239-5249. https://dx.doi.org/10.1109/TII.2020.3032690
Project: A1788a0023
Journal: IEEE Transactions on Industrial Informatics
Abstract: 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 testing data are collected from the same condition (same distribution or domain), which is generally not valid in real industry. Conventional approaches to address domain shift problems attempt to derive domain-invariant features, but fail to consider target-specific information, leading to limited performance. To tackle this issue, in this article, we propose a contrastive adversarial domain adaptation (CADA) method for cross-domain RUL prediction. The proposed CADA approach is built upon an adversarial domain adaptation architecture with a contrastive loss, such that it is able to take target-specific information into consideration when learning domain-invariant features. To validate the superiority of the proposed approach, comprehensive experiments have been conducted to predict the RULs of aeroengines across 12 cross-domain scenarios. The experimental results show that the proposed method significantly outperforms state-of-the-arts with over 21% and 38% improvements in terms of two different evaluation metrics.
URI: https://hdl.handle.net/10356/157026
ISSN: 1551-3203
DOI: 10.1109/TII.2020.3032690
DOI (Related Dataset): 10.21979/N9/FMUP9M
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TII.2020.3032690.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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