@inproceedings{03db1f2e91464c1ab6e3c70f12ef9c3d,
title = "Research on Fusion Method of Fault Diagnosis Based on DBN and Correlation Model for Optimized D-S Evidence Theory",
abstract = "With the intelligent development of weaponry, the system integration structure is more and more complicated. Based on the information fusion algorithm of the decision-making layer, the accuracy and speed of weapon equipment fault diagnosis can be greatly improved. The expert system fault diagnosis method and the neural network fault diagnosis method make the information fusion effect more superior. Based on the correlation model and the deep belief network diagnosis model, this paper applies the optimized D-S evidence theory to the information fusion method and expounds its workflow.",
keywords = "Correlation model, D-S evidence theory, DBN, fault diagnosis",
author = "Junyou Shi and Lan Luo and Chuxuan Fan",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 ; Conference date: 02-05-2019 Through 05-05-2019",
year = "2019",
month = may,
doi = "10.1109/PHM-Paris.2019.00067",
language = "英语",
series = "Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "356--361",
editor = "Chuan Li and \{de Oliveira\}, \{Jose Valente\} and Ping Ding and Ping Ding and Diego Cabrera",
booktitle = "Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019",
address = "美国",
}