基于符号动力学信息熵与SVM的液压泵故障诊断  

Fault Diagnosis of Hydraulic Pump Based on Symbolic Dynamics Entropy and SVM

作　　者：张华[1] 曾文韬 鄢威[1] 

机构地区：[1]武汉科技大学机械自动化学院,武汉430081

出　　处：《振动．测试与诊断》2017年第2期288-293,共6页Journal of Vibration,Measurement & Diagnosis

基　　金：国家科技支撑计划资助项目(2012BAF02B01;2011BAF11B01)

摘　　要：针对泵车液压泵早期故障特征信号微弱、故障特征难以提取的问题,提出了一种基于符号动力学信息熵与支持向量机(support vector machine,简称SVM)的泵车液压泵故障诊断方法。分别模拟了液压泵9种故障状态,测取了各状态下多测点的振动信号样本值。利用时间序列的符号动力学信息熵,计算各振动信号的符号动力学信息熵Hk,确定了各状态下相应的信息熵特征向量。建立了不同状态特征向量训练集,再结合支持向量机对液压泵故障模式进行诊断与识别,测试结果准确率为98.71%。将该方法与改进的BP(back propagation,简称BP)神经网络诊断结果进行了对比,结果表明该方法的识别率更高,诊断时间更短,适用于现场液压泵故障的在线诊断。In the light of the problem that the early fault feature signal of the hydraulic pump is weak and the fault feature is difficult to extract, a fault diagnosis method of hydraulic pump based on symbolic dynamics entropy and support vector machine(SVM) was proposed. Nine kinds of fault states of hydraulic pump were simulated, and the sample values of the vibration signal of the multi measurement points were measured then, the symbolic dynamics entropy Hkof each vibration signal were calculated using time series symbolic dynamics entropy to determine the corresponding symbol dynamics entropy feature vector of each state. Finally, the training set of feature vectors in different status was established for SVM to diagnose and identify the fault state of hydraulic pump, the accuracy of the test was 98.71%.The comparison of the diagnostic results with the improved BP neural network showed that the method has a higher recognition rate of 98.71% and cost less time which helps in online diagnosis. © 2017, Editorial Department of JVMD. All right reserved.

关 键 词：液压泵 故障诊断 符号动力学信息熵 支持向量机 

分 类 号：TH165.1[机械工程—机械制造及自动化] TP306.3[自动化与计算机技术—计算机系统结构]