基于改进的EEMD数据融合方法在轴承故障诊断中的应用

Fault diagnosis of rolling bearings based on combining improved EEMD adaptive denoising with multi-sensor joint acquisition

提出一种基于改进的EEMD谱峭度的多传感器联合轴承故障诊断方法,通过多个加速度传感器联合采集振动信号,利用EEMD自适应地将信号分解成多个分量,通过计算每个IMF分量的互相关系数法进行自适应重构以突出故障特征信号;对合成后的信号画快速峭度图,获得峭度最大时的中心频率和带宽;根据快速峭度图自适应确定电子谐振器的参数,并对此合成信号进行谐振增益;对谐振增益后的信号进行Hilbert解调,并与理论计算的轴承故障特征频率比较,从而确定故障部位.通过仿真的故障轴承信号和滚动轴承实验进行了验证,结果表明:该方法对滚动轴承故障的检测精度更高,提取精度和抗噪声能力方面有了明显的改进,对工程实践具有重要的指导意义.

A method for fault diagnosis of rolling bearings based on combining improved EEMD(Ensemble Empirical Mode Decomposition)adaptive denoising with multi-sensor joint acquisition is presented.First,vibration signal is jointly acquired through multiple acceleration sensors.Second,the original signal is decomposed into many components via EEMD adaptively,and adaptive reconstruction is performed by using the correlation coefficient method to highlight fault characteristic signals.Then,the central frequency and bandwidth of an electronic resonant are determined with spectral kurtosis,conducting resonant gain for synthesized signal.Last,the gained resonant signal is analyzed by using the Hilbert envelope,and the fault location can be determined after comparison with the bearing fault characteristic frequency calculated theoretically.This method is verified through simulated fault bearing signal and rolling bearing tests.The results prove the correctness and effectiveness of the method used in this paper,which has an important guiding significance for engineering practice.