集成经验模式分解在液压支架焊缝超声信号消噪中的应用研究

Study on application of EEMD to denoise ultrasonic signal of weld seam of hydraulic support

针对煤矿液压支架焊缝超声信号受到非线性、非平稳噪声干扰的问题,研究了基于集成经验模式分解(EEMD)的焊缝超声信号自适应去噪方法,首先对原始信号进行EEMD分解得到一系列固有模态函数(IMF),然后利用各IMF分量与原信号的相关系数的大小关系来重构超声回波信号。通过对仿真信号和试验信号进行分解,结果表明,该方法能够自适应地去除超声回波信号中的噪声成分,提高了信噪比,避免了模态混淆。

In view of the ultrasonic signal of the weld seam of the hydraulic support in colliery was disturbed by nonlinear and unstable noise, an adaptive denoising method for the ultrasonic signal of the weld seam based on ensemble empirical mode decomposition（EEMD） was proposed. First, EEMD of the original signal was conducted to obtain a series of intrinsic mode function（IMF）, and the magnitude of the correlation coefficient between components of each IMF and original signal was applied to reconstruct the ultrasonic echo signal. After the decomposition of simulation signal and test one, the results showed the method adaptively denoised the ultrasonic echo signal, enhanced the ratio of signal to noise and avoided modal confusion.