Signal recognition of loose particles inside aerobat based on support vector machine

被引：0
作者：
Meng C. [1 ,2 ]
Li Y. [1 ]
Zhang G. [3 ]
Zhao C. [3 ]
机构：
[1] School of Astronautics, Beihang University, Beijing
[2] Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing
[3] Defense Technology Research and Test Center, China Aerospace Science and Industry Corporation, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2020年 / 46卷 / 03期
关键词：
Loose particle detection; Machine learning; Particle impact noise detection (PIND); Signal recognition; Support vector machine;
D O I：
10.13700/j.bh.1001-5965.2019.0266
中图分类号：
学科分类号：
摘要：
Loose particles such as metal fragments and wires may be left in the control circuit of the aerobat during the process of manufacture, which will cause potential danger like short circuits. To solve this problem, a method of identifying material of loose particles in the aerobat based on particle impact noise detection (PIND) is proposed. This method firstly uses short-time autocorrelation function to obtain the pulse part of PIND signal, and then extracts various statistic features in time domain and frequency domain, which is combined with Mel frequency cepstral coefficient (MFCC) feature, and finally trains support vector machine model for material classification. In order to verify the effectiveness of the proposed method, loose particles' PIND signals with three different types of material are acquired and used for model training and tests. Test results show that the accuracy of identification can reach 98% which is better than related papers' results, verifying the effectiveness of the proposed method. © 2020, Editorial Board of JBUAA. All right reserved.
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页码：488 / 495
页数：7
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