基于EEMD-增强因子自适应的液压泵微弱故障特征提取

针对斜盘式轴向柱塞泵微弱故障特征难以提取的问题，提出了一种基于EEMD-增强因子自适应的液压泵微弱故障特征提取方法。对故障信号EEMD分解得到一组IMFs，采用增强因子作为各IMF权值合成信号以突出故障特征并抑制不相关成分；对合成信号EEMD分解，用敏感因子筛选出最能够表征故障信息的IMFs分量重构信号；对重构信号做Hil-bert变换求得包络谱，分析包络谱诊断出具体故障。仿真信号和液压泵实测信号的分析结果均很好地验证了该方法的有效性和优越性。

Faint Fault Feature Extraction of Hydraulic Pump Based on Adaptive EEMD-Enhancement Factor

Aimed at the problem of difficult to extract the faint fault feature of axial plunger piston pump of inclined disk type, a method based on adaptive EEMD-enhancement factor was presented. Fault signals were decomposed into a group of intrinsic mode functions (IMFs) with ensemble empirical mode decomposition (EEMD), as to highlight the fault characteristic and inhibit unrelated elements by using the enhancement factor of IMF as its weight to synthetic signal. The synthetic signal was decomposed with EEMD operation, and the sensitive component was constructed with the IMFs which were the best represents of fault information as they were selected according to their sensitive factor. The envelope spectral of reconstructed signal was obtained by executing Hilbert transform to it, and the actual fault was diagnosed by the analysis of the gained envelope spectral. The validity and superiority of the method are demonstrated by the analysis results of simulation signal and the engineering measured data of hydraulic pump.