Recurrent neural networks and its variants in Remaining Useful Life prediction

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dc.contributor.authorWang, Youdao
dc.contributor.authorAddepalli, Sri
dc.contributor.authorZhao, Yifan
dc.date.accessioned2021-05-04T10:46:39Z
dc.date.available2021-05-04T10:46:39Z
dc.date.issued2020-12-18
dc.description.abstractData-driven techniques, especially artificial intelligence (AI) based deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector because of the rapid growth of the industrial Internet of Things (IoT) and Big Data. Tremendous researches of DL techniques have been applied in machine health monitoring, but still very limited works focus on the application of DL on the Remaining Useful Life (RUL) prediction. Precise RUL prediction can significantly improve the reliability and operational safety of industrial components or systems, avoid fatal breakdown and reduce the maintenance costs. This paper reviews and compares the state-of-the-art DL approaches for RUL prediction focusing on Recurrent Neural Networks (RNN) and its variants. It has been observed from the results for a publicly available dataset that Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks outperform the basic RNNs, and the number of the network layers affects the performance of the prediction.en_UK
dc.identifier.citationWang Y, Addepalli S, Zhao Y. (2020) Recurrent neural networks and its variants in Remaining Useful Life prediction. IFAC-PapersOnLine, Volume 53, Issue 3, 2020, pp. 137-142en_UK
dc.identifier.issn2405-8963
dc.identifier.urihttps://doi.org/10.1016/j.ifacol.2020.11.022
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16643
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectGated Recurrent Uniten_UK
dc.subjectLong Short-Term Memoryen_UK
dc.subjectRecurrent Neural Networksen_UK
dc.subjectDeep Learningen_UK
dc.subjectasset lifecycle managementen_UK
dc.subjectPrognosticsen_UK
dc.subjectRemaining useful lifeen_UK
dc.titleRecurrent neural networks and its variants in Remaining Useful Life predictionen_UK
dc.typeArticleen_UK

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