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由于材料结构的复杂性,超声检测回波信号往往存在很多干扰噪声。针对钢制结构中平底孔的超声检测信号传统小波去噪方法中小波阈值难确定的问题,结合小波良好时频特性和果蝇的全局优化能力,提出基于果蝇算法(FOA)优化小波阈值函数的超声检测信号去噪方法。对原始信号叠加5d B高斯白噪声,通过测试最大信噪比改善量获得最佳小波基和分解层数,采用sym5小波对超声检测信号进行6层分解后,利用果蝇算法对小波阈值进行参数优化,对比传统4种阈值确定方法,提高小波阈值的精度。验证结果表明:该方法对超声检测信号去噪后信噪比、均方根误差和相关性等参数具有满意的效果,去噪效果明显。
Due to the complexity of the material structure, there is often a lot of interference noise in the ultrasonic echo signal detection. Aiming at the difficulty of determining the wavelet threshold in the traditional wavelet denoising method of ultrasonic detection signal of flat bottom hole in steel structure, combining with the good time-frequency characteristics of wavelet and the global optimization ability of fruit fly, a FOA-optimized wavelet threshold function Ultrasonic detection signal denoising method. By adding 5d B Gaussian white noise to the original signal and obtaining the best wavelet base and decomposition level by testing the maximum signal-to-noise ratio improvement, using the sym5 wavelet to decompose the ultrasonic detection signal by 6 levels, the Drosophila algorithm is used to carry out the wavelet threshold parameter Optimize and compare the traditional four thresholds to improve the accuracy of wavelet threshold. The verification results show that this method has a satisfactory effect on parameters such as signal-to-noise ratio, root mean square error and correlation of de-noised ultrasonic signals, and the denoising effect is obvious.