@inproceedings{a72de3528a0743d78a8ca780f68955e3,
title = "Intelligent Fault Diagnosis of Hypersonic Vehicle Based on ResCNN-LSTM-ATT",
abstract = "Accurate fault diagnosis is critical because it has a great impact on operational stability of hypersonic vehicles. Recent trends on various literatures shows that deep learning is a promising methodology to tackle many challenging tasks. In this study, a intelligent fault diagnosis method based on network is proposed for fault diagnosis. The network is constructed by a serial coupling of the one-dimensional Residual Convolution neural networks with Attention mechanism (ResCNN-ATT) and the Long short-term memory networks with Attention mechanism (LSTM-ATT), which is referred to as deep Residual Convolution LSTM attention network (ResCNN-LSTM-ATT). Experiments show that the proposed ResCNN-LSTM-ATT network endows a better ability to capture spatiotemporal correlations, which thus leads to the better accuracy comparing with the fault diagnosis algorithms based on FC-LSTM and ConvLSTM. According to the comparisons, effective improvements are guaranteed by the proposed ResCNN-LSTM-ATT based data-driven fault diagnosis method.",
keywords = "Attention mechanism, CNN, Data-driven, Hypersonic vehicle, Intelligent fault diagnosis, LSTM",
author = "Jiaxin Zhao and Liang Wang and Pingli Lu and Changkun Du",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022 ; Conference date: 28-10-2022 Through 30-10-2022",
year = "2022",
doi = "10.1109/CVCI56766.2022.9964643",
language = "English",
series = "2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 6th CAA International Conference on Vehicular Control and Intelligence, CVCI 2022",
address = "United States",
}