矿井主通风机电机轴承振动信号EMD和CEEMD故障提取对比研究

Comparative Study on Fault Extraction of EMD and CEEMD Vibration Signals of Motor Bearings of Mine Main Fan

矿井主通风机电机轴承状态监测信号一般表现出复杂的非平稳、非线性的特征,因此需要展开对非平稳信号分析与非线性特征提取的故障诊断方法的研究。为此,本文对比研究了经验模态分解(EMD)和互补总体平均经验模态分解(CEEMD)在信号去噪及特征提取部分的最优算法。实验结果表明:CEEMD相较于EMD对原始轴承振动信号具有更好的降噪效果及较为准确的特征提取效果,更接近于计算得出的故障特征频率理论值,为之后模式识别奠定良好的基础。

The state monitoring signal of the motor bearing of the mine main fan generally shows complex non-stationary and non-linear characteristics.Therefore,it is necessary to carry out the research on the fault diagnosis method of non-stationary signal analysis and non-linear feature extraction.To this end,this paper compares and studies the optimal algorithms of Empirical Mode Decomposition(EMD)and Complementary Overall Average Empirical Mode Decomposition(CEEMD)in signal denoising and feature extraction.The experimental results show that CEEMD has a better noise reduction effect and more accurate feature extraction effect on the original bearing vibration signal than EMD,which is closer to the calculated theoretical value of the fault characteristic frequency,and lays a good foundation for subsequent pattern recognition.