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An Improved Empirical Mode Decomposition Method for Vibration Signal
Wireless Communications and Mobile Computing Pub Date : 2021-04-28 , DOI: 10.1155/2021/5525270
Xiaohan Liu 1, 2 , Guangfeng Shi 1 , Weina Liu 1, 2
Affiliation  

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

中文翻译:

一种改进的振动信号经验模态分解方法

随着电子测量和信号处理技术的发展,非平稳和非线性信号特征已广泛用于错误诊断,系统识别和生物医学仪器领域。是否可以有效地提取这些功能通常会影响整个系统的性能。基于上述背景,本文的研究目的是一种改进的振动经验模态分解方法。本文介绍了爆破振动信号处理的一种方法-微分经验模态分解(DEMD),结合磷矿岩工程爆破振动监测测试,以及经验模态分解(EMD),对爆破振动信号的频率筛选,混叠进行了比较和分析。失真,以及分解信号的功率谱特性。结果表明,与EMD相比,DEMD有效抑制了信号混叠和失真,并且从信号功率谱变化的特征出发,DEMD提取了不同的主频分量,爆破振动信号的频率屏蔽效果优于EMD。它可以带来明显的精度提高,并且计算时间约为EMD方法的4倍。本文在对地面运动信号进行地面分析的基础上,运用EMD算法对实测的地面爆炸运动信号进行分析,研究其速度特征和时差,为研究运动信号提供了一种新途径。从信号功率谱变化的特点出发,DEMD提取了不同的主频分量,爆破振动信号的频率屏蔽效果优于EMD。它可以带来明显的精度提高,并且计算时间约为EMD方法的4倍。本文在对地面运动信号进行地面分析的基础上,运用EMD算法对实测的地面爆炸运动信号进行分析,研究其速度特征和时差,为研究运动信号提供了一种新途径。从信号功率谱变化的特点出发,DEMD提取了不同的主频分量,爆破振动信号的频率屏蔽效果优于EMD。它可以带来明显的精度提高,并且计算时间约为EMD方法的4倍。本文在对地面运动信号进行地面分析的基础上,运用EMD算法对实测的地面爆炸运动信号进行分析,研究其速度特征和时差,为研究运动信号提供了一种新途径。计算时间约为EMD方法的4倍。本文在对地面运动信号进行地面分析的基础上,运用EMD算法对实测的地面爆炸运动信号进行分析,研究其速度特征和时差,为研究运动信号提供了一种新途径。计算时间约为EMD方法的4倍。本文在对地面运动信号进行地面分析的基础上,运用EMD算法对实测的地面爆炸运动信号进行分析,研究其速度特征和时差,为研究运动信号提供了一种新途径。
更新日期:2021-04-29
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