基于优化SVM的多级离心泵定转子碰摩故障诊断

Rubbing Fault Diagnosis between Stator and Rotor of Multi-stage Centrifugal Pump Based on Optimized Support Vector Machine

针对工程实际中多级离心泵碰摩故障诊断准确率不高的现象,提出了一种基于优化支持向量机(SVM)的多级离心泵碰摩故障诊断方法,其特点是能从小样本振动信号中提取最有效的故障特征,优化模型的输入样本质量和参数并完成诊断。通过多级离心泵故障模拟试验台,获得实际产品的碰摩信号,采用集合经验模态分解(EEMD)算法进行时频域和信息熵特征的提取,并采用顺序特征选择(SFS)算法进行特征选择,结合网格搜索(GS)和粒子群优化(PSO)算法进行模型参数优化,建立了基于SVM的故障分类模型。实验结果表明,该方法对多级离心泵的碰摩故障有较高的识别精度,具有良好的实用性,可以为多级离心泵的碰摩故障诊断提供一定的参考价值。

In allusion to the low accuracy of rubbing fault diagnosis of multi-stage centrifugal pump in engineering,a rubbing fault diagnosis method for multi-stage centrifugal pump based on optimized support vector machine(SVM)is proposed.The method can complete diagnosis with a small amount of sample data by extracting the most effective fault features from vibration signal to optimize the input sample quality of the model and optimizing the parameters of the model.The rubbing signal of the actual product is obtained through the fault simulation test-bed of multistage centrifugal pump,and an SVM model is constructed by extracting time-frequency domain characteristics and information entropy characteristics of vibration signal with ensemble empirical mode decomposition(EEMD),features are selected with sequential fea⁃ture selection(SFS)algorithm,and the parameters of SVM with grid search(GS)algorithm and particle swarm optimization(PSO)algo⁃rithm are optimized.The experimental results show that the proposed model has high recognition accuracy for rubbing fault of multi-stage centrifugal pump in engineering and has good practicality.It can provide a certain reference for the rubbing fault diagnosis of the multistage centrifugal pump.