| Citation: | MENG Cai, LI Yanggang, ZHANG Guoqiang, et al. Signal recognition of loose particles inside aerobat based on support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(3): 488-495. doi: 10.13700/j.bh.1001-5965.2019.0266(in Chinese)
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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.
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