@inproceedings{edde24cbf13444c9a724cec8605968b4,
title = "Health Assessment of Armored Vehicles Based on K-means",
abstract = "In the field of weapon equipment fault diagnosis, a large amount of fault data is lacking, which makes it difficult to evaluate the condition of armored vehicles. In order to save the high cost of armored vehicle failure repair, reduce the redundant investment of manpower and material resources for armored vehicle maintenance, and improve the reliability of armored vehicle performance, a type of armored vehicle fire control system gyroscope is used as the research object. First, the rough set theory was used to reduce the data characteristics of the performance parameters of the gyroscope group of the fire control system, and then the initial clustering center was selected by the minimum variance to reduce the randomness of the initial clustering center and improve the accuracy. Finally, the reliability and accuracy of the method are verified by benchmark sample data and test data.",
keywords = "K-means algorithm, armored vehicle, rough set theory, state assessment",
author = "Yingshun Li and Hongli Zheng 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/SDPC49476.2020.9353121",
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 = "223--228",
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",
}