Solenoid directional control valve fault pattern recognition based on multi-feature fusion

被引：0
|
作者：
Ma D. [1 ]
Liu Z. [1 ]
Gao Q. [1 ]
Huang T. [1 ]
机构：
[1] National Key Discipline Laboratory of Armament Launch Theory & Technology, Rocket Force University of Engineering, Xi’an
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 04期
基金：
中国国家自然科学基金;
关键词：
C-support vector machine; current detection; fault diagnosis; multi-feature fusion; principal component analysis; solenoid valve; wavelet packet decomposition;
D O I：
10.13700/j.bh.1001-5965.2021.0367
中图分类号：
学科分类号：
摘要：
In order to further improve the reliability and recognition accuracy of the solenoid valve fault diagnosis method based on current detection at the drive end, a research was conducted on the solenoid valve fault pattern recognition method. First, a method for extracting eigenvalues based on time-frequency analysis of current signals and time-domain parameters was proposed; then, through designing an acquisition experiment of the current signal at the solenoid valve drive end, the time domain signal of the solenoid valve drive end current and the multicharacteristic curve of the second-order rate of change were obtained. Meanwhile, the time-domain parameters and the frequency band energy corresponding to the second-order rate of change were extracted as the characteristic value, in order to construct the feature vector of multi-feature fusion. Finally, a multi-class support vector machine based on the radial basis kernel function was used to identify the electromagnetic directional valve pattern. The research results showed that compared with the support vector machine based on energy eigenvalues, the support vector machine based on multi-feature fusion can improve the recognition accuracy by 8.7% and the verification accuracy by 42.11%. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
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页码：913 / 921
页数：8
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