面向水下焊接电信号的粒子群优化小波软阈值去噪法研究

De-Noising for Underwater Welding Electrical Signals by PSO Wavelet Soft Threshold Method

为降低噪声干扰以及提高检测精度,提出一种针对水下焊接电信号的基于粒子群优化的小波软阈值去噪方法。通过对信号进行小波分解,以信噪比作为评价指标,不断迭代进行全局搜索最优阈值从而完成信号去噪,并分别进行仿真及实测水下焊接电信号的去噪实验。结果表明,提出的算法具有信号信噪比更高、均方根误差更小、能有效保留信号的细节信息等优点,有利于水下焊接电信号特征的提取,从而为水下焊接质量在线监测提供基础。

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.