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Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.

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In order to investigate the feasibility of acoustic emission phenomenon aroused by electromagnetic excitation in metal specimens, electromagnetic acoustic emission model based on COMSOL software was established. On this basis, electromagnetic acoustic emission experiments were carried out and real signals with a lot of noise were obtained. To remove the noise of the electromagnetic acoustic emission signal, wavelet packet transform and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) was introduced. Firstly, the noise signal of low-frequency was removed by the wavelet packet transform, and the retained node signal was reconstructed. Then the reconstructed signal was resolved by CEEMDAN, and the effective component signal was obtained by the variance contribution rate. Finally, to obtain denoised electromagnetic acoustic emission signal, the effective component signal was reconstructed. The result of simulation and experiment shows that the electromagnetic acoustic emission signal could be effectively obtained by loading electromagnetic excitation, which has greater practical value in engineering applications.