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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 | 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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