Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
For prognostics and health management of mechanical systems, a core task is to predict the machine remaining useful life (RUL). Currently, deep structures with automatic feature learning, such as long short-term memory (LSTM), have achieved great performances for the RUL prediction. However, the con...
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| Veröffentlicht in: | IEEE transactions on industrial electronics (1982) Jg. 68; H. 3; S. 2521 - 2531 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
IEEE
01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 0278-0046, 1557-9948 |
| Online-Zugang: | Volltext |
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| Abstract | For prognostics and health management of mechanical systems, a core task is to predict the machine remaining useful life (RUL). Currently, deep structures with automatic feature learning, such as long short-term memory (LSTM), have achieved great performances for the RUL prediction. However, the conventional LSTM network only uses the learned features at last time step for regression or classification, which is not efficient. Besides, some handcrafted features with domain knowledge may convey additional information for the prediction of RUL. It is thus highly motivated to integrate both those handcrafted features and automatically learned features for the RUL prediction. In this article, we propose an attention-based deep learning framework for machine's RUL prediction. The LSTM network is employed to learn sequential features from raw sensory data. Meanwhile, the proposed attention mechanism is able to learn the importance of features and time steps, and assign larger weights to more important ones. Moreover, a feature fusion framework is developed to combine the handcrafted features with automatically learned features to boost the performance of the RUL prediction. Extensive experiments have been conducted on two real datasets and experimental results demonstrate that our proposed approach outperforms the state-of-the-arts. |
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| AbstractList | For prognostics and health management of mechanical systems, a core task is to predict the machine remaining useful life (RUL). Currently, deep structures with automatic feature learning, such as long short-term memory (LSTM), have achieved great performances for the RUL prediction. However, the conventional LSTM network only uses the learned features at last time step for regression or classification, which is not efficient. Besides, some handcrafted features with domain knowledge may convey additional information for the prediction of RUL. It is thus highly motivated to integrate both those handcrafted features and automatically learned features for the RUL prediction. In this article, we propose an attention-based deep learning framework for machine's RUL prediction. The LSTM network is employed to learn sequential features from raw sensory data. Meanwhile, the proposed attention mechanism is able to learn the importance of features and time steps, and assign larger weights to more important ones. Moreover, a feature fusion framework is developed to combine the handcrafted features with automatically learned features to boost the performance of the RUL prediction. Extensive experiments have been conducted on two real datasets and experimental results demonstrate that our proposed approach outperforms the state-of-the-arts. |
| Author | Wu, Min Yan, Ruqiang Zhao, Rui Chen, Zhenghua Guretno, Feri Li, Xiaoli |
| Author_xml | – sequence: 1 givenname: Zhenghua orcidid: 0000-0002-1719-0328 surname: Chen fullname: Chen, Zhenghua email: chen0832@e.ntu.edu.sg organization: Institute for Infocomm Research, ASTAR, Sinagpore, Singapore – sequence: 2 givenname: Min orcidid: 0000-0003-0977-3600 surname: Wu fullname: Wu, Min email: wumin@i2r.a-star.edu.sg organization: Institute for Infocomm Research, ASTAR, Sinagpore, Singapore – sequence: 3 givenname: Rui orcidid: 0000-0002-9699-9984 surname: Zhao fullname: Zhao, Rui email: rzhao001@e.ntu.edu.sg organization: Harveston Asset Management Company, Singapore, Singapore – sequence: 4 givenname: Feri surname: Guretno fullname: Guretno, Feri email: guretnof@i2r.a-star.edu.sg organization: Institute for Infocomm Research, ASTAR, Sinagpore, Singapore – sequence: 5 givenname: Ruqiang orcidid: 0000-0002-1250-4084 surname: Yan fullname: Yan, Ruqiang email: yanruqiang@xjtu.edu.cn organization: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China – sequence: 6 givenname: Xiaoli orcidid: 0000-0002-0762-6562 surname: Li fullname: Li, Xiaoli email: xlli@i2r.a-star.edu.sg organization: Institute for Infocomm Research, ASTAR, Sinagpore, Singapore |
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| CODEN | ITIED6 |
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| Title | Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach |
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