@inproceedings{ee437177625b406aa1a059fbb1fdd3fb,
title = "Aero-engine remaining useful life estimation based on 1-dimensional FCN-LSTM neural networks",
abstract = "To estimate the remaining useful life of aero-engines rapidly and accurately, a 1-Dimensional Fully-Convolutional LSTM algorithm is proposed. First, SVM is utilized as anomaly detector to find failures and it will help label training data. Then, K-means clustering with new operational features for multiple operation modes is employed. Finally, Convolutional neural network and LSTM are combined in parallel as the main estimating algorithm. The proposition is evaluated on the publicly available health monitoring dataset C-MAPSS of aircraft turbofan engines provided by NASA. The prognostic accuracy of the proposed algorithm is benchmarked against single LSTM and 1-D CNN and demonstrated to be more efficient.",
keywords = "1-D FCN-LSTM, Anomaly detect, Convolutional neural network, LSTM, Remaining useful life estimation",
author = "Wei Zhang and Feng Jin and Guigang Zhang and Baicheng Zhao and Yuqing Hou",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8866118",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "4913--4918",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "United States",
}