基于VMD-MDE和ELM的柱塞泵微弱故障诊断

针对早期微弱故障信号易受噪声干扰、难以提取和识别的问题,提出一种基于变分模态分解(variational mode decomposition,简称VMD)多尺度散布熵(multiscale dispersion entropy,简称MDE)和极限学习机(extreme learning machine,简称ELM)的柱塞泵微弱故障诊断方法。首先,采集各状态的振动信号进行VMD分解,得到若干模态分量,根据各模态分量Hilbert包络谱中特征频率能量贡献率大小,提出以归一化特征能量占比(feature energy ratio,简称FER)为重构准则的变分模态分解特征能量重构法(variational mode decomposition feature-energyreconsitution,简称VMDF),对各模态分量进行信号重构;其次,计算重构信号的MDE,对各尺度散布熵进行分析,选择有效尺度散布熵作为特征向量;最后,将提取的特征向量输入ELM完成故障模式识别。柱塞泵不同程度滑靴端面磨损故障的实验结果表明,该方法不仅提高了模式识别效率,还可以更好地反映故障程度变化规律,具有较好的应用性。

Weak Fault Diagnosis of Axial Piston Pump Based on VMD-MDE and ELM

To solve the problems that early weak fault signals are susceptible to noise interference and difficult to extract and identify, a piston pump weak fault diagnosis method based on variational mode decomposition (VMD), multiscale dispersion entropy (MDE) and extreme learning machine (ELM) is proposed. First, the vibration signals of various states to perform VMD are collected to obtain several modal components. According to the feature frequency energy contribution rate in the Hilbert envelope spectrum of each modal component, the variational modal decomposition feature energy reconstruction method (VMDF) with normalized feature energy ratio (FER) as the reconstruction criterion is proposed to reconstruct the signal of each modal component. Then, the MDE of the reconstructed signals are calculated. After analyzing the dispersion entropy at each scale, the effective scale dispersion entropy is selected as the feature vector. Finally, the feature vector is input to the ELM for pattern recognition. The verification results of the examples of the sliding shoe surface wear fault to varying degrees show that the proposed method can not only improve the efficiency of pattern recognition, but also better reflect the change law of fault degree. So, the proposed method has better applicability.