Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism
Prediction of remaining useful life (RUL) is of vital significance in the prognostics health management (PHM) tasks. To deal with the reverse time series and to reflect the difference in RUL prediction results at different time instances, this paper proposes a novel bidirectional gated recurrent uni...
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| Vydané v: | Reliability engineering & system safety Ročník 221; s. 108297 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
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Elsevier Ltd
01.05.2022
Elsevier BV |
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| ISSN: | 0951-8320, 1879-0836 |
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| Abstract | Prediction of remaining useful life (RUL) is of vital significance in the prognostics health management (PHM) tasks. To deal with the reverse time series and to reflect the difference in RUL prediction results at different time instances, this paper proposes a novel bidirectional gated recurrent unit with temporal self-attention mechanism (BiGRU-TSAM) to predict RUL. Specifically, a novel approach is proposed where each of the considered time instance is assigned a self-learned weight according to the degree of significance. Furthermore, the parameter update process of the TSAM is obtained with solid theoretical foundation, and as a sign of interpretability, it is shown that the assigned weights can remain consistency over several independent training processes. On this basis, the BiGRU-TSAM is applied to predict RUL online. An aircraft turbofan engine dataset and a milling dataset are applied to verify the proposed RUL prediction approach. The experimental results show the superiority of the proposed approach over the existing ones based on machine learning and deep learning.
•It is proposed to predict RUL with the aid of a novel BiGRU-TSAM network.•Each of the considered time instance is assigned a self-learned weight.•The parameter update process of the TSAM layer is obtained.•The assigned weights can remain consistency over several independent training processes. |
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| AbstractList | Prediction of remaining useful life (RUL) is of vital significance in the prognostics health management (PHM) tasks. To deal with the reverse time series and to reflect the difference in RUL prediction results at different time instances, this paper proposes a novel bidirectional gated recurrent unit with temporal self-attention mechanism (BiGRU-TSAM) to predict RUL. Specifically, a novel approach is proposed where each of the considered time instance is assigned a self-learned weight according to the degree of significance. Furthermore, the parameter update process of the TSAM is obtained with solid theoretical foundation, and as a sign of interpretability, it is shown that the assigned weights can remain consistency over several independent training processes. On this basis, the BiGRU-TSAM is applied to predict RUL online. An aircraft turbofan engine dataset and a milling dataset are applied to verify the proposed RUL prediction approach. The experimental results show the superiority of the proposed approach over the existing ones based on machine learning and deep learning.
•It is proposed to predict RUL with the aid of a novel BiGRU-TSAM network.•Each of the considered time instance is assigned a self-learned weight.•The parameter update process of the TSAM layer is obtained.•The assigned weights can remain consistency over several independent training processes. Prediction of remaining useful life (RUL) is of vital significance in the prognostics health management (PHM) tasks. To deal with the reverse time series and to reflect the difference in RUL prediction results at different time instances, this paper proposes a novel bidirectional gated recurrent unit with temporal self-attention mechanism (BiGRU-TSAM) to predict RUL. Specifically, a novel approach is proposed where each of the considered time instance is assigned a self-learned weight according to the degree of significance. Furthermore, the parameter update process of the TSAM is obtained with solid theoretical foundation, and as a sign of interpretability, it is shown that the assigned weights can remain consistency over several independent training processes. On this basis, the BiGRU-TSAM is applied to predict RUL online. An aircraft turbofan engine dataset and a milling dataset are applied to verify the proposed RUL prediction approach. The experimental results show the superiority of the proposed approach over the existing ones based on machine learning and deep learning. |
| ArticleNumber | 108297 |
| Author | Wu, Shimeng Yin, Shen Li, Xiang Luo, Hao Jiang, Yuchen Zhang, Jiusi |
| Author_xml | – sequence: 1 givenname: Jiusi orcidid: 0000-0001-7971-680X surname: Zhang fullname: Zhang, Jiusi organization: Department of Control Science and Engineering, School of Astronautics, Harbin Institute of Technology, Harbin, China – sequence: 2 givenname: Yuchen orcidid: 0000-0003-3918-7039 surname: Jiang fullname: Jiang, Yuchen organization: Department of Control Science and Engineering, School of Astronautics, Harbin Institute of Technology, Harbin, China – sequence: 3 givenname: Shimeng surname: Wu fullname: Wu, Shimeng organization: Department of Control Science and Engineering, School of Astronautics, Harbin Institute of Technology, Harbin, China – sequence: 4 givenname: Xiang surname: Li fullname: Li, Xiang organization: Department of Control Science and Engineering, School of Astronautics, Harbin Institute of Technology, Harbin, China – sequence: 5 givenname: Hao surname: Luo fullname: Luo, Hao email: hao.luo@hit.edu.cn organization: Department of Control Science and Engineering, School of Astronautics, Harbin Institute of Technology, Harbin, China – sequence: 6 givenname: Shen orcidid: 0000-0002-3802-9269 surname: Yin fullname: Yin, Shen organization: Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway |
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| SubjectTerms | Bidirectional gated recurrent unit Datasets Deep learning Machine learning Milling (machining) Prediction Predictions Process parameters Prognostics health management Reliability engineering Remaining useful life Temporal self-attention mechanism Turbofan engines Useful life |
| Title | Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism |
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