摘要
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。
The vibration signal of heavy-loaded coal mine machinery often has the nonlinear and non-stationary characteristics.It contains much information about the running status of equipment mixed with large amount of ambient noises,so the conventional spectrum analysis can＇t be applied directly.According to the characteristics of vibration signals in coal mine machinery,a de-noising method was proposed based on the empirical mode decomposition （EMD）,which has the advantages in dealing with nonlinear and non-stationary signals.In the method,the mechanical vibration signal was decomposed by EMD,to obtain the intrinsic mode functions （IMFs）.The correlation coefficient between each IMF and the original signal was calculated and sorted from smallest to largest.Then,the maximum difference between two adjacent correlation coefficients was searched to get the sensitive IMF for signal reconstruction.The filtering of the non-stationary signal was thus realized,which offers a good theoretical foundation for the fault diagnosis of mechanical equipments.Through the experimental data analysis,the effectiveness and feasibility of the EMD method for vibration signal de-noising were verified.
出处
《振动与冲击》
EI
CSCD
北大核心
2016年第22期38-40,共3页
Journal of Vibration and Shock
基金
国家自然科学基金(U1361121)
长安大学高速公路施工机械陕西省重点实验室开放基金(310825161124)
