In order to reduce noise interference and improve detection accuracy, a wavelet soft threshold denoising method based on particle swarm optimization (PSO) for electrical signals of underwater welding was presented. Through wavelet decomposition of the signals, the signal-to-noise ratio was selected as the evaluation index, and the optimal threshold value was searched for the global iteratively to complete the signal denoising. The denoising experiments for the simulated and the measured electrical signals of underwater welding process were carried out. It is proved that the signal-to-noise ratio was improved, the root mean square error was reduced, and the detailed information of the signals can be effectively preserved after denoising by this algorithm. It is suitable for the extraction of electrical signals features of underwater welding, laying the foundation for the online monitoring of underwater welding.
underwater weldingparticle swarm optimization algorithmwavelet soft thresholdelectrical signal denoising
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