基于EEMD的相关排列熵的滚动轴承故障诊断

Fault Diagnosis of Rolling Element Bearing of Correlation Coefficient and Arrangement Entropy Based on EEMD

针对滚动轴承振动信号非线性、非平稳难以有效诊断的问题,提出了一种集合经验模态分解的相关排列熵的滚动轴承故障诊断方法。利用集成经验模态分解方法将振动信号分解为多个本征模函数,结合排列熵和相关系数进行特征提取。并通过滚动轴承故障实验验证了结合相关系数和排列熵方法在特征提取时的有效性和互补性,同时有效抑制了重构信号的模态混叠问题,并根据重构信号的包络谱分析完成了滚动轴承故障诊断。通过仿真和实验验证了本方法的有效性。

Aiming at the problem that the nonlinear and non-stationary vibration signal of rolling element bearing is difficult to diagnose effectively,the fault diagnosis of rolling element bearing of correlation coefficient and permutation entropy based on EEMD was proposed.The EEMD is used to decompose the vibration signal into Intrinsic Mode Functions(IMF),and then combining permutation entropy and correlation coefficient is to extract features.According to the fault experiments of rolling element bearings,the validity and complementarity of the method is verified,at the same time the mode mixing of the reconstructed signal are effectively suppressed,and the fault diagnosis of rolling bearing is completed according to the envelope spectrum analysis of the reconstructed signal.The effectiveness of the method is verified by simulation and experiment.