Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction

Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction
Title:
Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction
Other Titles:
IEEE Transactions on Industrial Informatics
Publication Date:
21 October 2020
Citation:
M. Ragab et al., "Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction," in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2020.3032690.
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, 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 aero-engines 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.
License type:
PublisherCopyrights
Funding Info:
This work is supported by the A*STAR Industrial Internet of Things Research Program under the RIE2020 IAF-PP Grant A1788a0023, and partially supported by National Natural Science Foundation of China (No. 51835009). In addition, the first author of this work is supported by A*STAR SINGA Scholarship.
Description:
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ISSN:
1551-3203
1941-0050
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