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Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks | |
Xiang Li![]() ![]() | |
Source Publication | IEEE/CAA Journal of Automatica Sinica
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ISSN | 2329-9266 |
2023 | |
Volume | 10Issue:1Pages:121-134 |
Abstract | In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing data-fusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potential sensor malfunctions, which are likely to occur in real industries especially for machines in harsh operating environments. In this paper, a deep learning-based remaining useful life (RUL) prediction method is proposed to address the sensor malfunction problem. A global feature extraction scheme is adopted to fully exploit information of different sensors. Adversarial learning is further introduced to extract generalized sensor-invariant features. Through explorations of both global and shared features, promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions. The experimental results suggest the proposed approach is well suited for real industrial applications. |
Keyword | Adversarial training data fusion deep learning remaining useful life (RUL) prediction sensor malfunction |
DOI | 10.1109/JAS.2022.105935 |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/50731 |
Collection | 学术期刊_IEEE/CAA Journal of Automatica Sinica |
Recommended Citation GB/T 7714 | Xiang Li,Yixiao Xu,Naipeng Li,et al. Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(1):121-134. |
APA | Xiang Li,Yixiao Xu,Naipeng Li,Bin Yang,&Yaguo Lei.(2023).Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks.IEEE/CAA Journal of Automatica Sinica,10(1),121-134. |
MLA | Xiang Li,et al."Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks".IEEE/CAA Journal of Automatica Sinica 10.1(2023):121-134. |
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JAS-2022-0913.pdf(2608KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download |
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