基于CEEMD和奇异值差分谱的滚动轴承故障特征提取

发布时间：2018-09-01 18:16

【摘要】：针对滚动轴承故障信号非线性、非平稳特征导致的故障频率难以提取的问题,提出一种基于补充总体平均经验模态分解(Complementary EEMD,CEEMD)和奇异值差分谱结合的滚动轴承故障诊断方法。CEEMD分解向原信号成对地添加符号相反的白噪声,几乎消除残留白噪声的影响。首先,对故障信号利用CEEMD算法进行分解,得到若干IMF(Intrinsic Mode Function)分量,然后运用相关系数—峭度准则对IMF分量进行筛选并重构,再对重构信号进行奇异值分解,并求出奇异值差分谱,根据奇异值差分谱理论进行消噪和重构,最后对重构信号进行Hilbert包络谱分析,提取故障频率。实验结果表明,提出的方法,能精确地提取滚动轴承的故障频率。
[Abstract]:Aiming at the problem that the fault signal of rolling bearing is nonlinear and the fault frequency caused by non-stationary characteristic is difficult to extract, A rolling bearing fault diagnosis method based on complementary average empirical mode decomposition (Complementary EEMD,CEEMD) and singular value differential spectrum is proposed. The white noise with opposite symbols is added to the original signal in pairs, which almost eliminates the influence of residual white noise. Firstly, the fault signal is decomposed by CEEMD algorithm, and some IMF (Intrinsic Mode Function) components are obtained. Then the correlation coefficient kurtosis criterion is used to screen and reconstruct the IMF component, and then the singular value decomposition of the reconstructed signal is carried out, and the singular value difference spectrum is obtained. According to the singular value difference spectrum theory, the noise reduction and reconstruction are carried out. Finally, the Hilbert envelope spectrum of the reconstructed signal is analyzed, and the fault frequency is extracted. The experimental results show that the proposed method can accurately extract the fault frequency of rolling bearings.
【作者单位】：华北电力大学能源动力与机械工程学院;
【分类号】：TH133.33

【相似文献】