基于多尺度排列熵和正则化RVFL的高压隔膜泵单向阀故障诊断

高压隔膜泵单向阀运行工况复杂，运行时产生的振动信号具有非线性、非平稳特性，导致信号特征提取困难，故障状态难以识别.为了提取单向阀运行状态的非线性动力学特征，提升故障诊断模型的识别精度和泛化能力，提出了一种基于多尺度排列熵（Multi-scale Permutation Entropy,MPE）和正则化随机向量函数链接（Random Vector Functional Link,RVFL）网络的单向阀故障诊断方法.首先，对工况下采集的单向阀振动信号进行变分模态分解（Variational Mode Decomposition,VMD）获得既定的若干本征模态函数（Intrinsic Mode Function,IMF）分量；然后，计算IMF分量的多尺度排列熵，构建表征单向阀运行状态的特征值向量；最后，基于运行状态的特征值向量，建立正则化随机RVFL的故障诊断模型，并应用于单向阀的运行状态监测与识别.实验结果表明，构建的故障诊断模型能够精确地识别单向阀的故障类型，准确率达到98.89%.

Check fault of high pressure diaphragm pump valve based on multi-scale permutation entropy and regularized RVFL

The operating conditions of the valve of the high-pressure diaphragm pump are complex, and the vibration signal is in the characteristics of non-linear and non-stationary, which makes it difficult to extract the signal characteristic information and the fault state to be difficult to identify. In order to extract the non-linear dynamic characteristics of the operating state of the one-way valve and improve the recognition accuracy and generalization ability of the fault diagnosis model, a method based on Multi-scale Permutation Entropy (MPE) and Random Vector Functional Link network (RVFL) was proposed. Firstly, the vibration signal of the check valve which was collected under the working condition was decomposed by Variational Mode Decomposition (VMD) to obtain certain Intrinsic Mode Function (IMF) components. Then, the multi-scale permutation entropy of IMF component was calculated, and the eigenvalue vector representing the operation state of one-way valve was constructed. Finally, based on the eigenvalue vectors of the operating state, a regularized RVFL fault diagnosis model was established, which could be applied to the monitoring and identification of the operating state of one-way valves. The experimental analysis showed that the fault diagnosis model could accurately identify the fault type of one-way valve and the accuracy rate was 98.89%.