北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (3): 488-495.doi: 10.13700/j.bh.1001-5965.2019.0266

• 论文 • 上一篇    下一篇

基于支持向量机的飞行器多余物信号识别

孟偲1,2, 李阳刚1, 张国强3, 赵长兴3   

  1. 1. 北京航空航天大学 宇航学院, 北京 100083;
    2. 北京航空航天大学 生物医学工程高精尖创新中心, 北京 100083;
    3. 航天科工防御技术研究试验中心, 北京 100854
  • 收稿日期:2019-05-28 发布日期:2020-03-28
  • 通讯作者: 孟偲 E-mail:Tsai@buaa.edu.cn
  • 作者简介:孟偲,男,博士,副教授,博士生导师。主要研究方向:机器视觉、机器人智能系统;李阳刚,男,硕士研究生。主要研究方向:图像处理、机器学习;张国强,男,硕士研究生。主要研究方向:无损检测;赵长兴,男,高级工程师。主要研究方向:无损检测。

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

MENG Cai1,2, LI Yanggang1, ZHANG Guoqiang3, ZHAO Changxing3   

  1. 1. School of Astronautics, Beihang University, Beijing 100083, China;
    2. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, China;
    3. Defense Technology Research and Test Center, China Aerospace Science and Industry Corporation, Beijing 100854, China
  • Received:2019-05-28 Published:2020-03-28

摘要: 针对飞行器控制电路在生产制造过程中可能引入金属线头等微小多余物,从而留下短路等安全隐患的问题,提出了一种基于微粒碰撞噪声检测(PIND)的飞行器多余物材质识别方法。首先,利用短时自相关函数提取PIND信号的脉冲部分;然后,提取多种时频域统计特征,并与梅尔频率倒谱系数(MFCC)特征结合起来;最后,训练多分类支持向量机模型实现材质分类。为验证所提方法的有效性,采集了3种不同材质多余物的PIND信号进行模型训练及测试,实验结果表明,所提方法材质识别准确率达98%,优于同类方法的相关结果。

关键词: 多余物检测, 微粒碰撞噪声检测(PIND), 机器学习, 信号识别, 支持向量机

Abstract: 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.

Key words: loose particle detection, particle impact noise detection (PIND), machine learning, signal recognition, support vector machine

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