Wang, YoudaoAddepalli, SriZhao, Yifan2021-05-042021-05-042020-12-18Wang 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. 137-1422405-8963https://doi.org/10.1016/j.ifacol.2020.11.022https://dspace.lib.cranfield.ac.uk/handle/1826/16643Data-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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Gated Recurrent UnitLong Short-Term MemoryRecurrent Neural NetworksDeep Learningasset lifecycle managementPrognosticsRemaining useful lifeArticle