基于DEMD的高压隔膜泵单向阀早期故障诊断

针对高压隔膜泵单向阀的早期故障特征提取困难的问题,提出基于微分经验模态分解(differential empirical mode decomposition,简称DEMD)的高压隔膜泵单向阀早期故障诊断方法。首先,对振动信号进行微分运算,提高高频成分的振幅比,使微弱高频成分在后续分解中更易提取;其次,对得到的新信号进行经验模态分解(empirical mode decomposition,简称EMD),并将分解后的本征模函数(intrinsic mode function,简称IMF)分量信号进行积分还原;最后,计算分量信号与原振动信号的Kullback-Leibler散度(Kullback-Leibler divergence,简称K-L散度)值,选取K-L散度值较小的分量信号进行重构,并利用Hilbert边际谱对重构信号进行瞬时频谱分析,以提取故障振动信号的特征。仿真与工程实验分析表明,该方法能够较好地提取出单向阀早期故障特征信息。

Early Fault Diagnosis of High Pressure Diaphragm Pump Check Valve Based on Differential Empirical Mode Decomposition

This paper presents an early fault diagnosis method for the high-pressure diaphragm pump check valve based on the differential empirical mode decomposition. First, the differential transformation of the vibration signals is derived to increase the amplitude ratio of the high-frequency component for easy extraction in the subsequent decomposition. Further, empirical mode decomposed (EMD) is applied on the signals and the intrinsic mode functions obtained from EMD are restored by integral. Finally, the components with smaller K-L divergence with the original signals is selected to reconstruct, and the instantaneous frequency spectrum of reconstructed signals is analyzed by Hilbert marginal spectrum to extract the characteristics of the fault vibration signals. Simulation and engineering experimental analysis show that the proposed method can extract the fault feature information better.