摘要
矿山环境中爆破振动信号含有大量的高频噪声,严重影响了爆破振动信号的真实性。针对爆破信号中含有大量高频噪声这一现象,以云南某露天矿爆破振动数据为研究对象,提出了基于自适应WOA-VMD-MPE爆破振动信号降噪方法开展爆破振动信号降噪研究。将3个矿山爆破振动信号进行WOA优化算法处理,得出VMD算法参数中最佳组合K、a,带入VMD算法分解,将MPE值大于0.6的IMF成分去除,以达到降噪效果。结果表明:振动波形经过WOA-VMD算法优化后信噪比均为最高,分别为15.23、25.51和27.2,其均方根误差最小,分别为8.51、8.73和5.91。WOA-VMD算法相较EEMD、CEEMD算法在信噪比方面平均增加56%和44%、均方根误差平均减少46%和42%,其降噪效果优于EEMD、CEEMD算法,能够较好地去除高频噪声,保留原始爆破振动波形信息,验证了算法的普适性。
The blasting vibration signal in the mine environment contains a lot of high-frequency noise,which seriously affects the authenticity of the blasting vibration signal.In this paper,targeting the phenomenon that there is a lot of high frequency noise in the blast signals,using blast vibration data from an open pit mine in Yunnan as a research object,this paper proposes research on noise reduction of blast vibration signals based on adaptive WOA-VMD-MPE blast vibration signal denoising method.WOA optimization algorithms are used to process blast vibration signals from three mines,and the optimal parameter combination of the VMD algorithm K、a is found,which is broken down via the VMD algorithm,and the IMF components with MPE above 0.6 are removed in order to obtain the effect of the noise reduction.The results show that it can be seen that the signal-to-noise ratio of the vibration waveforms optimized by the WOA-VMD algorithm is the highest,which is 15.23,25.51,27.2 respectively,and the mean squared error is the lowest,which is 8.51,8.73,5.91,respectively.Compared to the EEMD and CEEMD algorithms,the signal-to-noise ratio of the WOA-VMD algorithm is increased by 56%and 44%,and the mean squared error is decreased by 46%and 42%,respectively.It has a better noise reduction effect than the EEMD and CEEMD algorithms.It can better remove high frequency noise and retain the original vibration waveform information from the blast,thus verifying the universality of the algorithm.
作者
贾皓琦
黄永辉
张智宇
JIA Haoqi;HUANG Yonghui;ZHANG Zhiyu(Faculty of Land Resources Engineering,Kunming University of Science and Technology,Kunming 650093,China;Faculty of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650500,China)
出处
《有色金属工程》
CAS
北大核心
2023年第12期151-162,共12页
Nonferrous Metals Engineering
基金
国家自然科学基金资助项目(52064025、52164009)
云南省重大科技项目(202202AG050014)。