Research on classification method of welding cold crack's AE signals based on VMD energy entropy and GA-SVM
Aiming at the delayed cold crack caused by non-standard welding process in the welding process of low-alloy high-strength steel,a monitoring and classification method is proposed.First,acoustic emission(AE)technology is used to continuously monitor the post-welding specimen.Then,crack initiation signal and hydrogen aggregation signal in the AE signal of cold crack are selected according to the monitoring data of the specimen with cold crack,and energy entropy feature extraction based on variational mode decomposition(VMD)is carried out for both.Finally,genetic algorithm(GA)is used to improve the traditional support vector machine(SVM)to classify and recognize the signal,and the recognition and classification results of the traditional SVM are compared.At the same time,in order to verify the accuracy of VMD energy entropy in feature extraction,the EMD energy entropy and CEEMDAN energy entropy of cold crack acoustic emission signal extraction were compared for classification and recognition effect.The results show that when the VMD energy entropy is used as the identification feature of the acoustic emission signal of welding cold crack,the recognition accuracy is higher than other energy entropy features,and the recognition accuracy will further increase to 95%with the optimization of support vector machine.
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维普
