Check valve fault diagnosis based on VMD parametric optimization and enhanced multi-scale permutation entropy
PAN Zhen1,2， HUANG Guoyong1,2， WU Man1,2
1.Faculty of Information Engineering & Automation, Kunming University of Science and Technology, Kunming 650500, China;
2.Yunnan Institute of Mineral Pipeline Engineering Technology,Kunming 650500, China
摘要针对高压隔膜泵机械结构复杂，单向阀故障特征信息分布在多尺度上，单一尺度难以全面提取特征的问题，提出了一种基于参数优化变分模态分解(variational mode decomposition，VMD)和增强多尺度排列熵(enhanced multi-scale permutation entropy，EMPE)的单向阀故障诊断方法。首先对单向阀振动信号进行VMD分解，以包络熵最小原则对其进行参数优化，获得既定的若干本征模态函数(intrinsic mode function，IMF)分量；然后，计算IMF分量的增强多尺度排列熵，构建故障特征值向量；最后，利用基于变量预测模型的模式识别(variable predictive model based class discriminate，VPMCD)方法对故障特征值向量进行训练和识别，进而实现单向阀的故障诊断。仿真信号和工程实验分析表明，该方法可以准确地识别单向阀的故障类型，具有一定的可靠性和工程应用价值。
Abstract：Aiming at the complex mechanical structure of the high-pressure diaphragm pump, the fault characteristic information of the check valve is distributed on multiple scales, and it is difficult to extract the feature comprehensively at a single scale. A fault diagnosis method for check valve based on parameter optimization variational mode decomposition (VMD) and enhanced multi-scale permutation entropy (EMPE) was proposed. Firstly, the vibration signals of check valve were decomposed by VMD, and the parameters of VMD were optimized with the minimum envelope entropy principle to obtain several intrinsic mode functions (IMFs). Then, the enhanced multi-scale permutation of the IMFs was extracted to construct the fault characteristics vector; Finally, variable predictive model based class discriminate (VPMCD) was used to train and identify the fault feature vector, and then the fault diagnosis of the check valve was realized. The simulation signal and engineering experiment analysis show that the method can accurately identify the fault type of the check valve, and has certain reliability and engineering application value.
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