Feature extraction of acoustic emission debonding signals based on CCSO-VMD
Acoustic emission detection methods are widely used in the defect detection of equipment,for most of the equipment generated acoustic emission signal amplitude is small,large noise and features are difficult to extract the problem,this paper proposes a signal processing method:the CCSO algorithm based on the Pearson correlation coefficient-envelope entropy minimum principle optimizes the processing method of parameters in VMD.In this method,the cross algorithm is integrated on top of the classical flock optimization algorithm,and the key parameters in the VMD,namely the modal number K and the penalty factor α,are accurately optimized by the improved CCSO algorithm.By using the CCSO-VMD method based on the new fitness function,the analog signal was analyzed,and the signal-to-noise ratio reached 25.814 1 dB.This result proves that the CCSO-VMD algorithm based on the new objective function can significantly reduce the noise level while retaining the valid information in the signal to the greatest extent.In addition,this paper proposes a comprehensive spectral difference index,and the CSDI value can effectively distinguish the acoustic emission signals in different states.
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