@inproceedings{b953cfd652e54d3895e0715bd37b828b,
title = "Research on Prediction Method of Armored Vehicle Fire Control System Based on BAS-RVM",
abstract = "Due to the particularity of armored vehicles, the failure information is very insufficient, and the prediction of its main core part, the fire control system, is more difficult. Based on the analysis of the fault characteristics of armored vehicle fire control system and the regression algorithm of correlation vector machine, a fault prediction method of fire control system is proposed. The prediction model was established by using the relevant vector machine optimized by the beetle search algorithm, and simulation tests were performed. The results show that BAS-RVM can effectively predict the failure of a gyroscope component in the fire control system of armored vehicles, which proves that rationality.",
keywords = "beetle Antennae Search, fault prediction, fire control system, parameter optimization, relevance vector machine",
author = "Yingshun Li and Runhao Li and Xiaojian Yi and Haiyang Liu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2020 ; Conference date: 05-08-2020 Through 07-08-2020",
year = "2020",
month = aug,
day = "5",
doi = "10.1109/SDPC49476.2020.9353131",
language = "English",
series = "Proceedings of 2020 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "26--29",
editor = "Yong Qin and Zuo, {Ming J.} and Xiaojian Yi and Limin Jia and Dejan Gjorgjevikj",
booktitle = "Proceedings of 2020 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2020",
address = "United States",
}