基于经验模态分解的爆破延期识别优化方法

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摘要针对爆破延期识别中采用经验模态分解（empirical mode decomposition ,EMD）方法存在模态混叠现象，导致延期识别精度低的问题。提出了一种完全正交经验模态分解（Principal Empirical Mode Decomposition, PEMD）方法，首先对原始信号进行EMD初步分解，得到多个具有模态混叠现象的IMF分量，其次对IMF分量进行主成分分析（principal component analysis，PCA），将混叠的IMF分量完全正交化，之后选择幅值较大且波形衰减明显的主分量，使用Hilbert变换提取包络线，最后对包络线峰值点进行识别。通过相似物理模拟实验证明，PEMD与传统方法EMD相比，有效地抑制振动信号EMD分解时出现模态混叠现象，延期识别误差降低至0 ms，并通过控制高程和延期时间对PEMD方法的稳定性进行了检验；同时以德兴露天边坡延期爆破实验为例，PEMD能够更好地对爆破振动波峰值点进行精确识别，识别率稳定在90%以上，对后续爆破工程中爆破参数设计优化和盲炮的识别具有重要意义。

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	易文华
	刘连生
	闫雷
	董斌斌
	刘伟
	杨砚

关键词 ：经验模态分解（EMD）, 模态混叠, 主成分分析（PCA）, 完全正交经验模态分解（PEMD）, 延期识别

Abstract：In order to solve the problem of low identification accuracy caused by modal aliasing in the delay identification process of blasting signal in EMD method. A completely orthogonal empirical mode decomposition method is proposed. Firstly, the original signal is decomposed by EMD to obtain several IMF components with modal aliasing. Secondly, the IMF components are analyzed by principal component analysis. The IMF components are completely orthogonalized, and the main IMF components with large amplitude and obvious waveform attenuation are selected. The envelope is extracted by Hilbert transform. Finally, the peak points of the envelope are identified. Through the similar physical simulation experiment, it is proved that compared with the traditional EMD method, principal empirical mode decomposition (PEMD) can effectively suppress the modal aliasing when EMD decomposition of vibration signal, reduce the delay identification error to 0ms, and test the stability of PEMD method by controlling the elevation and delay time variables. At the same time, taking the delay blasting experiment of open-pit slope as an example, PEMD can identify the peak value of blasting vibration better, and the recognition rate is more than 90%. It is of great significance to the optimization of blasting parameters and the identification of blind gun in subsequent blasting engineering.

Key words： empirical Mode Decomposition (EMD) modal aliasing Principal Component Analysis (PCA) Principal Empirical Mode Decomposition (PEMD) delay recognition

收稿日期: 2020-06-16 出版日期: 2022-01-28

引用本文:

易文华,刘连生，闫雷，董斌斌，刘伟，杨砚. 基于经验模态分解的爆破延期识别优化方法[J]. 振动与冲击, 2022, 41(2): 217-223.
YI Wenhua,LIU Liansheng,YAN Lei,DONG Binbin,LIU Wei,YANG Yan. An optimization method for the identification of blasting delay based on empirical mode decomposition. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(2): 217-223.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I2/217

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