@inproceedings{7695b474fdbf4e4a998b2e712acf0a8b,
title = "Fault Diagnosis Based on Fault Tree and Bayesian Network with Grey Optimization",
abstract = "Remote-controlled engine ignition of unmanned aerial vehicle (UAV) is the key node for its successful flight mission. The effective fault diagnosis and prevention of remote-controlled engine is the guarantee of its safe and reliable operation. This paper proposes a fault diagnosis method based on the fusion of fault tree (FT) and Bayesian network (BN). Further, a grey analysis model is employed to mine the in-depth information of the FT model, so as to find the key factors affecting the ignition failure of the remote-controlled engine, and provide support for maintenance and product design. Finally, the correctness and validity of the proposed model are verified by using a the typical fault mode of the remote-controlled engine.",
keywords = "Bayesian network, Fault diagnosis, Fault tree, Grey analysis, Remote-controlled engine",
author = "Dong Liu and Xiaoyu Xu and Ke Ma and Laifa Tao and Mingliang Suo",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 34th Chinese Control and Decision Conference, CCDC 2022 ; Conference date: 15-08-2022 Through 17-08-2022",
year = "2022",
doi = "10.1109/CCDC55256.2022.10033578",
language = "英语",
series = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1787--1792",
booktitle = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",
address = "美国",
}