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作 者:田婕 张云鹏 闫鹏[1] 孙文诚 杨曦 TIAN Jie;ZHANG Yun-peng;YAN Peng;SUN Wen-cheng;YANG Xi(College of Mining Engineering,North China University of Science and Technology,Tangshan 063210,China;Hebei Provincial Key Laboratory of Mine Development and Safety Technology,Tangshan 063210,China;Chengde Guangxing Mining Co.,Ltd.,Chengde 067103,China)
机构地区:[1]华北理工大学矿业工程学院,唐山063210 [2]河北省矿业开发与安全技术重点实验室,唐山063210 [3]承德广兴矿业有限责任公司,承德067103
出 处:《爆破》2024年第2期143-150,159,共9页Blasting
基 金:河北省教育厅在读研究生创新能力培养资助项目(CXZZBS2023124);河北省高等学校科学技术研究项目(QN2023166);河北省自然科学基金(E2016209388)。
摘 要:由于爆破区域地形地质条件的复杂、监测仪器的误差、振动传播介质的反射以及磁场的干扰等原因,采集的原始爆破振动信号常常会掺杂大量的噪声,针对此问题提出了基于互补集合经验模态分解(CEEMD)的信号降噪光滑模型。此模型将爆破振动信号进行CEEMD,基于分解所得到的IMF分量建立低通滤波算法。根据滤波算法的相似度与光滑度,构造目标函数并计算最优解,其对应的滤波算法模型即为爆破振动信号的最优降噪光滑模型。通过构造仿真信号验证了降噪光滑模型算法的可行性,并将模型应用了实际露天深孔爆破振动信号的研究。采用信噪比和均方根误差两种指标对比经验模态分解(EMD)方法、小波阈值法、CEEMD-小波阈值法与滤波算法模型BP3的降噪效果,验证了降噪光滑模型在对露天矿山爆破振动信号降噪方面的有效性,并通过频谱分析进一步验证了降噪光滑模型较CEEMD-小波阈值法的优越性。结果表明:基于CEEMD的露天深孔爆破振动信号降噪光滑模型具有良好的降噪能力,能够在保留原始爆破振动信号真实特征信息的前提下对信号进行降噪,且降噪效果优于EMD方法、小波阈值法和CEEMD-小波阈值法。Due to the complex terrain and geological conditions in the blasting area,as well as errors in monitoring instruments,reflections of vibration propagation medium,and interference from magnetic fields,a significant amount of noise is often present in the original blasting vibration signals collected.To address this issue,a signal noise reduction smooth model based on complementary ensemble empirical mode decomposition(CEEMD)is proposed.Firstly,the measured blasting vibration signal is decomposed using CEEMD and an algorithm for low-pass filtering is established based on the obtained intrinsic mode function(IMF)component from the decomposition.Additionally,an objective function is constructed to calculate the optimal solution according to similarity and smoothness criteria for filtering algorithms.The resulting filtering algorithm model represents an optimal denoising smooth model for blasting vibration signals.To verify our noise reduction smooth model,a simulation signal is constructed and applied to actual open-pit deep-hole blasting vibration signal research.Finally,the noise reduction effects of empirical mode decomposition(EMD)method,wavelet threshold method,CEEMD-wavelet threshold method,and filter algorithm model BP3 are quantified and compared using two indexes:signal-to-noise ratio and root-mean-square error.It has been confirmed that the proposed noise reduction smooth model effectively reduces noise in open-pit blasting vibration signals.The findings demonstrate that our CEEMD-based noise reduction smooth model for open-pit deep-hole blasting vibrations possesses excellent denoising capabilities while preserving essential characteristic information from the original signals.Furthermore,the denoising effect of the proposed model surpasses that of EMD method,wavelet threshold method,and CEEMD-wavelet threshold method.
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