基于最大奇异值能量熵和随机森林的真空快速开关机械故障诊断

Mechanical Fault Diagnosis of Vacuum Fast Switch Based on Energy Entropy of Maximum Singular Value and Random Forest Algorithm

针对电磁斥力机构真空快速开关的机械状态监测问题,提出了一种最大奇异值能量熵(energy entropy of maximum singular value,EEMSE)和随机森林的故障诊断方法。首先,在真空快速开关中采集振动信号,对振动信号进行改进S变换得到模矩阵,随后对该矩阵的子矩阵进行奇异值分解,再利用信息熵理论对最大奇异值求熵得到特征向量,最后将特征向量输入随机森林模型进行故障分类和诊断。与不同特征量和分类器比较后的结果表明,文中提出的真空快速开关机械故障诊断方法特征一致性好,模型诊断速度较快,对实验样本总体诊断准确率达到了100%。

Aiming at the problem of mechanical state monitoring of vacuum fast switch based on electromagnetic repulsion mechanism,a kind of fault diagnosis method based on energy entropy of maximum singular value(EEMSE)and random forest is proposed.Firstly,the vibration signal is collected in the vacuum fast switch,and the vibration signal is transformed by improved S-transform to obtain the modular matrix.Then,the submatrix of the matrix is decomposed into singular value,and the feature vector is obtained by calculating the entropy of the maximum singular value by using the information entropy theory.Finally,the feature vector is input into the random forest model for fault classification and diagnosis.The result shows that after comparing with different feature and classifiers the mechanical fault diagnosis method of vacuum fast switch proposed in this paper has good feature consistency,fast model diagnosis speed and the overall diagnosis accuracy of experimental samples reaches 100%.