@inproceedings{dbee29e59f3e44e5ba89530a312ee541,
title = "Fault diagnosis method for fire control system based on empirical wavelet transform and relevance vector machine",
abstract = "The fire control system is an extremely important part of the tank and directly determines whether the tank can accurately hit the target. In extremely sophisticated fire control system devices, the signals generated by faults are mostly non-stationary, nonlinear, multi-component complex signals. In order to improve the accuracy of fault diagnosis of fire control systems, it is necessary to analyze and process complex signals more accurately. In this paper, a fault diagnosis method for fire control system is proposed. The acquired signal is denoised and extracted by empirical wavelet transform (EWT). The extracted signal is sent to the trained relevance vector machine (RVM) model. To achieve fault diagnosis of the fire control system.",
keywords = "Empirical wavelet transform, Fault diagnosis, Feature extraction, Fire control system, Relevance vector machine",
author = "Yingshun Li and Runhao Li and Xiaojian Yi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SDPC.2019.00040",
language = "English",
series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "178--182",
editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",
booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
address = "United States",
}