煤矿机械在重载情况下运行，其振动信号往往具有非线性、不平稳等特性，其不仅带有大量设备运动状态的信息，同时也夹杂着大量的环境噪声，无法直接对其进行分析。而经验模式分解（EMD）在处理非线性、非平稳信号时具有一定优势，是一种自适应的信号处理方法。针对煤矿机械振动信号的特性，提出基于EMD的去噪方法，首先将振动信号进行EMD分解，得到各固有模态函数（IMF），然后计算各IMF与原始信号的相关系数，并将相关系统按照从小到大进行排序，通过相邻两个相关系数的差值最大，找到敏感IMF分量重构，实现非平稳信号的滤波，为机械设备后期故障诊断奠定了良好基础。并通过实验数据分析，验证了EMD方法对振动信号进行去噪的有效性及可行性。
Abstract
The vibration signal, of coal mine machinery when overloading, often has the nonlinear and non-stationary characteristics. It contains much information of running status of equipment and all manner of ambient noise mixed up with, so the spectrum analysis can’t be carried out directly. However, the EMD method has a certain advantage in dealing with the nonlinear and non-stationary signal, which is an adaptive signal processing method. According to the characteristics of coal mine machinery vibration signal, the de-noising method was proposed based on empirical mode decomposition (EMD). Firstly, decomposing the mechanical vibration signal based on EMD, to obtain the intrinsic mode function (IMF). Secondly, calculating the correlation coefficient of the IMF and the original signal and sorting them from smallest to largest. Then, calculating the maximum difference between two adjacent correlation coefficients to get the sensitive IMF, which realized filtering of the non-stationary signal and offered a good theoretical foundation for late fault diagnosis of mechanical equipment. And through the experimental data analysis, the effectiveness and feasibility of the EMD method for vibration signal de-noising is verified.
关键词
振动信号 /
EMD方法 /
去噪 /
煤矿机械
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Key words
vibration signal /
EMD /
de-noising /
coal mine machinery
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