基于EEMD与奇异熵增量谱的齿轮故障特征提取

Feature Extraction of Gear Fault based on EEMD and Incremental Spectrum of Singularity Entropy

针对齿轮振动信号非线性、非平稳的特点,提出一种基于集合经验模态分解(EEMD)与奇异熵增量谱的齿轮故障特征提取方法。首先,利用EEMD方法将齿轮振动信号分解为若干个平稳的本征模态函数(IMF)分量。EEMD方法利用正态分布白噪声的二进尺度分解特性,能够有效抑制经验模态分解(EMD)中的模态混叠现象。但由于背景噪声和残余辅助白噪声的影响,EEMD分解得到的IMF分量难以准确提取齿轮故障特征。利用奇异值分解(SVD)对IMF分量进行消噪和重构,根据奇异熵增量谱确定重构阶次,准确地提取齿轮的故障特征频率。仿真信号分析和齿轮箱齿轮故障实验验证了该方法的准确性和有效性。

A method of feature extraction for gear fault based on Ensemble empirical mode decom- position （EEMD） and incremental spectrum of singularity entropy is put forward for the non-station- ary and non-linear characteristics of gear vibration signal. Firstly, the gear vibration signal is decom- posed into several smooth intrinsic mode functions （IMFs） by EEMD. The method of EEMD could take advantage of dyadic scaledecomposition characteristics of the normal distribution white noise to suppress the problem of mode confusion in EMD. Because of the interference of background noise and residual assisted white noise, the gear fault feature is not extracted exactly from IMF. The method of singular value decomposition is used to remove the noise and reconstruct the IMF. The reconstruction order is determined according to the incremental spectrum of singularity entropy. Therefore the gear fault feature frequency could be extracted exactly. Results of simulation analysis and the gear fault test indicated that this method is accurate and effective for gear fault feature extraction.