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DC Field | Value | Language |
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dc.contributor.author | Wang, W | - |
dc.contributor.author | Lei, Y | - |
dc.contributor.author | Yan, T | - |
dc.contributor.author | Li, N | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2021-12-29T17:38:02Z | - |
dc.date.available | 2021-12-29T17:38:02Z | - |
dc.date.issued | 2021-12-20 | - |
dc.identifier | ORCID iDs: Yaguo Lei https://orcid.org/0000-0002-5167-1459; Tao Yan https://orcid.org/0000-0002-3328-2118; Asoke Nandi https://orcid.org/0000-0001-6248-2875. | - |
dc.identifier.citation | Wang, W. et al. (2022) 'Residual Convolution LSTM Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification', Journal of Dynamics, Monitoring and Diagnostics, 1 (1), pp. 2 - 8. doi: 10.37965/jdmd.v2i2.43. | en_US |
dc.identifier.issn | 2833-650X | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23837 | - |
dc.description.abstract | Copyright © 2021 The Author(s). Recently, deep learning is widely used in the field of remaining useful life (RUL) prediction. Among various deep learning technologies, recurrent neural network (RNN) and its variant, e.g., long short-term memory (LSTM) network, are gaining more attention because of their capability of capturing temporal dependence. Although the existing RNN-based approaches have demonstrated their RUL prediction effectiveness, they still suffer from the following two limitations: 1) it is difficult for RNN to extract directly degradation features from original monitoring data, and 2) most of the RNN-based prognostics methods are unable to quantify the uncertainty of prediction results. To address the above limitations, this paper proposes a new method named Residual convolution LSTM (RC-LSTM) network. In RC-LSTM, a new ResNet-based convolution LSTM (Res-ConvLSTM) layer is stacked with convolution LSTM (ConvLSTM) layer to extract degradation representations from monitoring data. Then, predicated on the RUL following a normal distribution, an appropriate output layer is constructed to quantify the uncertainty of the forecast result. Finally, the effectiveness and superiority of RC-LSTM is verified using monitoring data from accelerated degradation tests of rolling element bearings. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China (52005387, 52025056); Project funded by China Postdoctoral Science Foundation (2020M673380); Fundamental Research Funds for the Central Universities. | en_US |
dc.format.extent | 2 - 8 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Intelligence Science and Technology Press Inc. | en_US |
dc.rights | This is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. | - |
dc.rights | Copyright © The Author(s) 2022. This is an open access article published under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | deep learning | en_US |
dc.subject | residual convolution LSTM network | en_US |
dc.subject | remaining useful life prediction | en_US |
dc.subject | uncertainty quantification | en_US |
dc.title | Residual Convolution LSTM Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.37965/jdmd.v2i2.43 | - |
dc.relation.isPartOf | Journal of Dynamics, Monitoring and Diagnostics | - |
pubs.issue | 1 | - |
pubs.publication-status | Published online | - |
pubs.volume | 1 | - |
dc.identifier.eissn | 2831-5308 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © The Author(s) 2022. This is an open access article published under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 1.98 MB | Adobe PDF | View/Open |
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