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The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process.

The acoustic emission (AE) signal is weak due to the coupling and intersecting coexistence of other disturbance components in aluminum alloy metal inert-gas welding (MIG) process. It is necessary to analyze and process the AE signal for the accurate identification of the welding state. A time frequency feature extraction method based on synchronous compression wavelet (SST) and principal component analysis (PCA) is proposed in this paper. The SST transform is performed to the collected AE signal of the MIG welding process to obtain the time frequency distribution. The PCA is subsequently performed to the time frequency distribution of the AE signals to determine the principal components. The approximate entropies of the principal components are calculated to quantitatively express the state characteristics of the welding process. The proposed method is applied to the AE signal of the friction, arc shock and the crack in the aluminum alloy MIG welding process. The three kinds AE signals are identified by inputting the calculated approximate entropies into the support vector machine (SVM). The results indicate that the calculated approximate entropies highlight the characteristic ability of the different modal AE signals, which can be used for monitoring the aluminum alloy MIG welding process quantitatively.