@inproceedings{2480d45b46c74d8aabf3272c22b59641,
title = "Remaining useful life estimation using long short-term memory deep learning",
abstract = "This paper proposes a deep learning method to estimate the remaining useful life (RUL) of aero-propulsion engines. The proposed method is based on the long short-term memory (LSTM) structure of the recurrent neural network (RNN). LSTM can effectively extract the relationship between data items that are far separated in the time series. The proposed method is applied to the NASA C-MAPSS data set for RUL estimation accuracy evaluation and is compared with the methods using the multi-layer perceptron (MLP), support vector regression (SVR), relevance vector regression (RVR) and convolutional neural network (CNN). Comparisons show that the proposed method is better than others in terms of the root mean squared error (RMSE) and the value of a scoring function.",
keywords = "deep learning, long short-term memory, recurrent neural network, remaining useful life",
author = "Hsu, {Che Sheng} and Jiang, {Jehn Ruey}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th IEEE International Conference on Applied System Innovation, ICASI 2018 ; Conference date: 13-04-2018 Through 17-04-2018",
year = "2018",
month = jun,
day = "22",
doi = "10.1109/ICASI.2018.8394326",
language = "???core.languages.en_GB???",
series = "Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "58--61",
editor = "Lam, {Artde Donald Kin-Tak} and Prior, {Stephen D.} and Teen-Hang Meen",
booktitle = "Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018",
}