Detection and identification of acoustic emission signals based on recurrence quantification analysis and support vector machines
SI Li1,4，BI Gui-hong2,4，WEI Yong-gang3,4, TAO Ran4，ZHANG Shou-ming1
1.Faculty of Information Engineering and Automation Kunming University of Science and Technology, Kunming 650500, China；
2. Faculty of Electrical Engineering Kunming University of Science and Technology, Kunming 650500, China；
3. Faculty of metallurgy and energy engineering Kunming University of Science and Technology, Kunming 650093, China；
4. Yunnan Special Equipment Inspection and Research Engineering Center, Kunming 650050, China
To prevent the leakage accidents of pipes and boiler, the key lies in whether the signals of cracks and small leaks can be detected effectively. For the problem of acoustic emission signals detection, a new method of detecting acoustic emission signals based on RQA and SVM was described. Firstly, the theory of wavelet threshold de-noising was used to reduce the noise signal. Secondly, the principle of RQA was borrowing to detection of acoustic emission signal, by calculation, we can get some quantifiable feature parameters. Using these parameters as SVM input parameters, the simulation acoustic emission signals of cracks can be identified. In the experimental conditions, the feasibility of this method has been validated.
司 莉1,4，毕贵红2,4，魏永刚3,4，陶 然4，张寿明1. 基于RQA与SVM的声发射信号检测识别方法[J]. 振动与冲击, 2016, 35(2): 97-103.
SI Li1,4，BI Gui-hong2,4，WEI Yong-gang3,4, TAO Ran4，ZHANG Shou-ming1. Detection and identification of acoustic emission signals based on recurrence quantification analysis and support vector machines. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(2): 97-103.
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