Detection and identification of acoustic emission signals based on recurrence quantification analysis and support vector machines

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (2) : 97-103.

SI Li1,4，BI Gui-hong2,4，WEI Yong-gang3,4, TAO Ran4，ZHANG Shou-ming1

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Abstract

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.

Key words

acoustic emission / wavelet threshold de-noising / recurrence quantification analysis / support vector machine

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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[J]. Journal of Vibration and Shock, 2016, 35(2): 97-103

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