Abstract
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.
Highlights
Aluminum alloy welding structure has the advantages of light weight, high specific strength and strong corrosion resistance, which is applied in national defense, aerospace, automobile and other fields
The welding acoustic emission (AE) signals are collected by the AE sensors, preamplifier and the AE acquisition system, which are subsequently transmitted to industrial control computer by cable transmission
The approximate entropy is used as a characteristic parameter to identify the AE signals in aluminum alloy metal inert-gas welding (MIG) welding process
Summary
Aluminum alloy welding structure has the advantages of light weight, high specific strength and strong corrosion resistance, which is applied in national defense, aerospace, automobile and other fields. Liu et al [21] proposed a detection method by SST to extract the instantaneous frequency of the damaged structure, which have effectively tracked the time varying damage. In order to extract the feature parameters from the large amount time frequency domain data by SST, it is necessary to highlight the characteristic ability of the AE signal. PCA is used to compress the time frequency data obtained by SST, which can effectively extract the more effective feature information. A time frequency feature extraction method of the AE signals is proposed by combination of SST and PCA, which is used for the monitoring of the aluminum alloy MIG welding process. The proposed method is applied to the AE signals of the friction, the arc shock and the crack in the aluminum alloy MIG welding process.
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