Pitting acoustic emission signal separation and recognition method based on FCM-FastICA
Metal pitting is a destructive and hidden damage form of equipment,which will generate acoustic emission signals.The various sound source types generated in the pitting process will cause signal aliasing and affect the judgment of corrosion process.Aiming at the problem of pitting signal aliasing,a pitting signal separation and recognition method based on fuzzy C-means(FCM)clustering and fast independent component analysis(FastICA)algorithm was proposed.By analyzing single and double pitting acoustic emission(AE)data,the pitting process was divided into passive film rupture stage,pitting induced nucleation stage and development stage.The signal categories were determined by clustering and the mixed signals were separated by FastICA,and the separation effect was verified by correlation function.The results show that there are three kinds of original signals in single pitting process and seven kinds of signals in double pitting process,including single signal and mixed signal.The correlation between a single signal and the original signal is extremely high,reaching above 0.8.The correlation between the separation component of the mixed signal and the original signal is more than 0.6.The separation effect is good.This method can effectively separate and identify the pitting mixture signals and provide support for judging the corrosion process.
Pitting mixed signalBlind source separationCoefficient of correlationSignal separationClustering
NETL
NSTL
万方数据
维普
