基于FCEEMD复合筛选的故障特征提取方法

A fault feature extraction method based on FCEEMD composite screening

针对快速集成经验模态分解(FEEMD)和固有模态函数(IMF)选择方法的缺陷,提出一种基于快速互补总体经验模态分解(FCEEMD)复合筛选的故障特征提取方法。首先,引入符号相反的成对的白噪声来中和FEEMD中的残余噪声,抑制IMF之间的模态混叠并得到一系列新的IMF;然后,基于能量及相关系数构建复合筛选模型并根据筛选得到的有效IMF构建重构信号;最后,通过希尔伯特(Hilbert)包络解调提取重构信号中包含的周期性脉冲特征来诊断轴承故障。凯斯西储大学(CWRU)轴承数据集上的实验结果表明,该方法能高效、准确地提取出轴承故障特征,在旋转机械故障诊断中有借鉴意义和应用前景。

Aiming at the defects of fast ensemble empirical mode decomposition(FEEMD)and intrinsic mode functions(IMF)selection method in feature extraction,a fast complementary ensemble empirical mode decomposition(FCEEMD)composite screening based fault feature extraction method is proposed.Firstly,pairs of white noise with opposite signs are introduced to neutralize the residual noise in FEEMD and suppress the mode aliasing,and obtain a series of IMF.Secondly,a composite screening model is constructed based on the energy and correlation coefficients,and the reconstructed signal is constructed according to the effective IMF obtained by screening.Finally,the periodic pulse features contained in the reconstructed signal are extracted by Hilbert envelope demodulation to diagnose the bearing fault.The analysis results of Case Western Reserve bearing data show that the method can extract bearing fault features efficiently and accurately,which has reference significance and application prospects in the fault diagnosis of rotating machinery.